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The dangers of becoming a full-stack tester

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The dangers of becoming a full-stack tester

Get up to speed fast on the techniques behind successful enterprise application development, QA testing and software delivery from leading practitioners.

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The dangers of becoming a full-stack tester

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#StoptheSpread: Extended Offers for Business Continuity

An increasing number of firms are now on the hunt for what they call “full-stack testers.” What they mean by that is people who can test everything in the company’s process, and do so at garage-sale prices. It may appear to these firms that they’re getting high quality at low cost, but full-stack testing comes with significant dangers.

It’s not just that there’s the potential for missed bugs and delayed delivery. There are also dangers for the professionals working in software testing and quality assurance (QA), and even their employers.

Fortunately, there is a straightforward way to avoid some of these dangers and, in the process, create a more rewarding career. It’s called “niching,” and in an age when everyone seems to want all their testers to do everything under the sun, it may sound counterintuitive.

However, while some hiring managers might advocate for becoming a generalist, niching is a real, potentially lucrative option—and it works as a career strategy. Here’s what you need to know.

[ Learn best practices for reducing software defects with TechBeacon’s Guide. Plus: Get the report “Agile and DevOps Reduces Volume, Cost, and Impact of Production Defects” ]

Managers, recruiters, and even testers are drawn to the lure of solving any problem at any time. Nobody ever wants to say no to a project. It sounds like an admission of defeat or a lack of skill, but the reality is quite different.

The challenge is that requiring testers to develop familiarity with everything doesn’t allow them to build the deep knowledge they need to solve the unique problems that inevitably arise. They’ll be forever learning, and never applying that knowledge.

That also keeps them from advancing to a more senior role, which often involves more critical thinking and strategy, rather than rote execution. It may sound like a paradox, but trying to broaden your skills by becoming a full-stack tester will limit your QA career development.

The most visible impact is on testers themselves. Trying to do everything for everyone ultimately leads to burnout. This results in increased stress, frustration, fear of losing a job, low performance, job hopping, and, in the extreme people leave the profession all together.

Some testers move to business analyst or project management roles, which use overlapping skill sets, but don’t have the overwhelming and burdensome requirements to be able to do everything. This allows them to focus on what they do well, and to continue to do it even better.

However, they may have had to leave something they loved and potentially taken a pay cut to do so. That can have a big impact.

A second danger is to software development firms. When their testers are overwhelmed, they’re not performing at their best. This leads to excess bugs, delayed delivery, and lower end-user satisfaction. That’s not good for their reputation or their bottom line.

Plus, requiring a full-stack tester for every role is not a scalable strategy. Full-stack testers are few and far between, which means that, if you need to ramp up your team, you’ll do better to focus on getting the specific skills you’re missing rather than shooting for the moon.

There is a danger to the software testing industry, too. We need to keep smart and dedicated people around. If they get frustrated and leave, the testing profession won’t have the innovative problem solvers it needs to advance our craft in the future.

Those huge lists of requirements are barriers to entry that are more likely to scare away the next generation than attract them. If you don’t have anyone stepping up to fill tester roles after the good ones retire or get promoted, what’s going to become of testing itself?

Finally, don’t forget that end users are at risk when the requirements for testers are too broad. When software development companies can’t find the right people to test their products, users get a lower-quality product on a delayed time scale. They don’t get what they need to serve their own customers or clients, so their reputations and profits are affected.

With all those downsides to full-stack testing, how can software testers position themselves better?

[ Understand what your team needs to know to take advantage of test automation with TechBeacon’s Guide. Plus: Get the Buyer’s Guide For Software Test Automation Tools ]

Niching may look a little different for everyone, but it boils down to concentrating on a specialty and going very deep in that area. Rather than trying to do everything for everyone (and burning out), niching allows for a more satisfying and lucrative career.

It’s no secret that brain surgeons have more specialized knowledge and skill than general surgeons. And because they are solving deeper problems that can be understood only with those specialized skills, they are compensated more, and they are sought out for opportunities to use those special skills.

A niche within software testing might be a specific type of test: functional testing, performance testing, security testing, penetration testing, etc. Or it might be testing for a certain industry: medical, or education, or consumer goods, and so on.

The deepest niche would be to combine the two and concentrate solely on functional testing for the medical industry, for example. That’s going to provide a tester with the chance to have the deepest, most precise skill set to bring to the problems that will arise when it comes to functional testing of new medical software.

When testers do this, they increase their market value, just as a brain surgeon is more valuable than a general practitioner. They get good at what they do, and they get compensated more, because they’re more valuable.

It’s not just the technical skills that improve with niching. Testers can realize significant emotional and empowerment benefits as well.

First, testers can give themselves a significant amount of confidence in their skills when they know they’re focusing on just one thing, and not worrying about what they don’t know how to do. Dedicating your time to functional testing means you’re going to know much more about that specific aspect than those who have only scratched the surface.

It’s quite freeing to be able to attack that next-level problem within your niche and know that you have the skills to take on those challenges when they arise.

Along with confidence comes focus. When you’re bouncing back and forth all the time between functional testing and performance testing and security testing, you often end up losing momentum and your train of thought. Picking just one allows you to remain focused and dedicated to the tasks at hand, which gives you a faster delivery and a better-quality work product.

A third benefit of niching is empowerment. This is the opportunity for testers to perform adjacent work to their niche.

If you get known as an expert in functional testing for medical professionals, that doesn’t necessarily mean you’re pigeonholed and destined to work in that very narrow niche forever. Inevitably someone will hear that you’re great at functional testing for medical companies, and they’ll ask, “Okay, but can you do it for something that’s kind of like medical but not quite?”

Your answer will be, obviously, “Of course I can.” And thus, choosing a niche and going deep within it solves the paradox and gives you a broader range of opportunities.

Yes, becoming a full-stack QA tester has cachet, but it also presents career dangers. It’s loaded with potentially overwhelming expectations that create stress and may drive many testing professionals out of the industry. What’s worse, companies and their clients don’t get the high-quality products they need.

So pick a niche and go deep. Not only will you gain significant improvements in your technical abilities, but you’ll also reap emotional the benefits of owning your niche. That’s as valuable as any certification.

[ Practice quality-driven development with best practices from QA practitioners in TechBeacon’s Guide. Plus: Download the World Quality Report 2019-20 ]

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Lessons in Project Management From the COVID-19 Disruption

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Lessons in Project Management From the COVID-19 Disruption

COVID-19 has been one of the most disruptive events mankind has faced in generations. Not once in the last 100 years have governments had to seal borders or ask companies to shut down non-essential services. For project management practitioners, “safety first” has taken a new direction, a big change from delivery “on time and on budget”.

Many projects are being put on hold, not because they were not needed or because there was no funding available, but because of uncertainty and market volatility. Worldwide, governments are collaborating at an unprecedented scale and for a while political differences and old rivalry are forgotten.

At a smaller scale, organisations, teams, and individuals are adapting to the crisis and the savvy ones are learning from the impacts of actions taken at a larger scale.

As industries adapt to the new normal, what should organisations be asking themselves to ensure that they can embrace a new and more effective way of delivering projects over the long term? How can project management professionals adapt to the post COVID world and maintain or even increase their employability?

In this ebook, we present an analysis by Dan S. Roman, an outcome-driven Senior Project Manager and Scrum Master with over four decades of project management experience. Dan is a pioneer of Agile delivery, using light documentation, incremental and iterative development since 1990 and formal Agile Frameworks (XP, Scrum) since early 2000.

A champion of combining best practices to achieve results, Dan reflects on possible changes that the project management profession may see in the future due to the disruption brought about by COVID-19.

Dan makes his projections on four key aspects in the delivery of projects and programs in response to the COVID-19 crisis are examined, namely: project delivery disciplines, the role of project leadership, management of project phases and the need for increased focus on upskilling.

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Why agile leadership is key in these uncertain times

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Why agile leadership is key in these uncertain times

Get up to speed fast on the techniques behind successful enterprise application development, QA testing and software delivery from leading practitioners.

The dangers of becoming a full-stack tester

Top developer projects fighting on the front lines of COVID-19

Agile strategy: How to go from projects to products in 3 steps

Virtual software development teams: 4 challenges to overcome

Why enterprises are adapting their COBOL apps vs. ditching them

Software development and IT operations teams are coming together for faster business results. Learn from enterprise dev and ops teams at the forefront of DevOps.

Are poor team interactions killing your DevOps transformation?

SAP S/4HANA app migration: Lessons from the trenches

6 proven metrics for DevOps success

Is DevOps fatigue next? Don’t buy into the dogma

3 best practices of the DevSecOps elite

Trends and best practices for provisioning, deploying, monitoring and managing enterprise IT systems. Understand challenges and best practices for ITOM, hybrid IT, ITSM and more.

Why you should share BI resources with your data science teams

INSPIRE 20 Podcast: June Manley, Female Founders Faster Forward

ESM essentials: What it is, why it matters

INSPIRE 20 podcast: Michael Fieldhouse, social impact practice leader, DXC

4 ways digital transformation can help you adapt to a post-pandemic world

All things security for software engineering, DevOps, and IT Ops teams. Stay out front on application security, information security and data security.

30 app sec stats that matter

Feds warn: MSPs being hacked—so stop your complacency

Go beyond policy: 5 keys to data protection compliance

AI and security: Machine learning is a threat detection game-changer

Google, Apple, Mozilla enforce 1-year max certificate expiration

Technical conference highlights, analyst reports, ebooks, guides, white papers, and case studies with in-depth and compelling content.

INSPIRE 20 Podcast Series: 20 Leaders Driving Diversity in Tech

TechBeacon Guide: Cloud Security & Data Privacy

TechBeacon Guide: Zero Trust Security

TechBeacon Guide: Application Security Testing

#StoptheSpread: Extended Offers for Business Continuity

As our world turns upside down with the Coronavirus pandemic, most companies are reacting rather than responding. But in times of uncertainty, business agility provides stability—a way to manage change and respond productively. It’s the difference between how an agile organization operates in uncertain markets versus how traditional organizations do it.

Organizations that have been undergoing business agility transformations have shown benefits such as increased revenue, faster turnaround times, and higher-quality offerings, according to the Business Agility Institute’s 2019 Business Agility Report.

However, the biggest challenge highlighted in that report was leadership style. As a leader you need to model behavior exemplifying the agile mindset, and results indicated that leaders have not always demonstrated agile leadership skills, buy-in, or support for an agile transformation.

Leadership is a key success factor in any enterprise agile transformation. Here’s what you need to know about being an agile leader.

[ Learn best practices for reducing software defects with TechBeacon’s Guide. Plus: Get the report “Agile and DevOps Reduces Volume, Cost, and Impact of Production Defects” ]

As an enterprise agile coach, I often see confusion and misinformation when it comes to understanding leadership’s role in an agile transformation. Executives and managers often associate agility with a framework such as Scrum, rather than a mindset. They may not understand how critical it is not only to understand the agile mindset, but to regularly practice that mindset and lead by example as well.

Even when they understand that their role is critical, leaders may be unclear as to what type of leadership is expected in an agile environment. The industry is full of buzzwords and courses promoting “servant leadership,” “lean and agile leadership,” or “adaptive leadership.”

Evan Leybourn, founder and CEO at the Business Agility Institute, advises to look beyond the buzzwords and focus on the leadership concepts associated with agility.

What is important is not what it is called, but what are the characteristics, he said. Leaders must be able to delegate outcomes, inspire individuals, and give them the space to do a great job.

“[Effective leaders] need to learn quickly that they can’t do everything. A good leader is one who elevates the people around them.”
Evan Leybourn

These attributes of empowering others, giving autonomy to staff, and involving them in the decision-making process at appropriate levels are universal in all styles of leadership taught in agility classes.

As followers of agile know, there is no one-size-fits-all answer, even when it comes to a specific leadership style. But understanding and adopting an agile mindset forms the basis for strong leadership.

Every leader has a unique personality, and each has probably met with more success when demonstrating his or her own authentic style than when trying to follow a prescribed set of guidelines. So, for example, a naturally very technical person who feels an expectation to facilitate “touchy-feely” team-building exercises but doesn’t buy into that approach will probably not be successful.

Some leaders were promoted into leadership because of their expertise rather than their people or leadership skills. Others may have had great success using a style of leadership that isn’t consistent with the styles usually promoted in agile cultures.

Should those who have had success with their current style change? 

“The question is whether that success was tied to a specific context. A leader who cannot adapt will find success fleeting. We see this all the time in companies and even have a name for it: one-hit wonders.” 
—Evan Leybourn

Different leaders will always have different personalities, he added. Two leaders with different personalities can both be inspiring and adaptable.

A strong leader recognizes that there is always room for growth and will work to understand what parts of her leadership style are working well in what context and what parts could be improved, Leybourn said.

[ Understand what your team needs to know to take advantage of test automation with TechBeacon’s Guide. Plus: Get the Buyer’s Guide For Software Test Automation Tools ]

In a traditional leadership model, leaders are decision-makers who assign tasks and work to subordinates. These leaders both reap the benefits of recognition for success and take the blame for failure. With their reputation and careers at stake, they are less likely to delegate decision making to those who report to them.

If leaders at the top hold managers accountable in a traditional way, those managers will have a difficult time practicing an agile style of leadership. Practicing agility only within software development teams isn’t as effective as practicing business agility throughout an organization, and the same applies to agile leadership. You won’t create complete success unless the entire system changes.

Education can be the answer, but Leybourn suggests executive coaching over training classes.

“Bring in someone to sit beside you and help you during the difficult times. Training will help you learn the concepts, but to make it stick—especially in difficult times—you need a great coach.”
—Evan Leybourn

One of the most important agile concepts is that there are no right or wrong answers; everything depends on context. When you are dealing with uncertainty and change, there’s no leadership book you can refer to that will tell you what to do in every situation.

That’s why you must develop a mindset that will guide you to act in accordance with the set of values and principles that you believe in.

Agile values and principles encourage leaders to use collaboration and empower others to grow and share in decision making. Agile leaders look at failures as opportunities to learn and foster trust and psychological safety in their organizations.

“Look beyond the buzzwords. But also understand that, just because something is a buzzword doesn’t make it wrong. It doesn’t matter how you lead, if you lead in the right way for your teams.”
—Evan Leybourn 

[ Practice quality-driven development with best practices from QA practitioners in TechBeacon’s Guide. Plus: Download the World Quality Report 2019-20 ]

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Why agile leadership is key in these uncertain times

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How to set up local environment for Angular 9

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How to set up local environment for Angular 9

This article is a guide to building a solid atmosphere to facilitate the best Angular learning and work experience. Let us make sure that we have the best possible development experience and do not run into common issues with the development environment. Facing issues with the development environment is one of the biggest obstacles for someone new to the Angular ecosystem, perhaps even more so than the reactive concepts themselves. 

It is therefore crucial to get the environment correctly set up right from the start. The development environment must be easily upgradeable and create minimal issues over time due to things like semantic versioning. 

In this guide, I discuss how to set up your Angular Development environment using the Angular CLI method. I will talk about the prerequisites, how to install a CLI, how to build an initial workspace and starter app, and how to run the app locally to test your setup. 

You should be familiar with the following to use the Angular framework: 

HTML, CSS, JavaScript, Typescript 

Knowledge of TypeScript is helpful, but not essential. 

Development Environment 

In this case, Node.js is used to create the backend of your app and can be replaced with any server-side technology you like, such as PHP, Ruby, or Python. However, Angular does not rely on Node.js, except for its CLI tool and for downloading NPM packages. 

To set up the best possible development experience, and if you do not have node yet installed: please visit the nodejs.org website and install the most recent version of node.  

Please make sure that you use the long-term support version (LTS) instead of the latest version.Open a browser, type https:/nodejs.org/en/download/, and click the Windows Installer button. You will seethe LTS displayed along with the current version of the node. Go ahead and download the recommended or current version of the node. 

We will be using node for frontend tooling purposes and for running our development server. For this specific purpose, you would be likely to run into fewer issues if you employed the latest version. However, if you already have node installed (any version), there is a far better way to upgrade your node version than to run an installer.Instead of overwriting your current version of node with the latest one, let us use a simple command line tool that easily allows switching node versions. 

There are several advantages to using the command line tool:  

So, let us make sure you have at least some version of the node installed on your machine before you start. To check your version, run node -v in a terminal/console window. 

NPM is the Node Package Manager. This is a list for hosting node packages. It has also been used in recent years to publish front end packages and libraries such as Angular, React, Vue.js and even Bootstrap. 

Angular, the Angular CLI, and Angular apps depend upon features and functionality provided by libraries that are available as npm packages. To download and install npm packages, you need to have a npm package manager. The npm client command line interface is used to access NPM package manager which gets installed with node js. 

To check that you simply have the npm client installed, run npm -v during a terminal/console window. 

Angular CLI is the official resource for beginning and operating with Angular projects. It saves you from the mess of complicated configurations and builds resources like TypeScript, Webpack, and so on.  Like most modern frontend tools these days, Angular CLI is also built at the top of Node.js. Node.js is a server technology that allows you to run JavaScript on your server and build server-side web applications. However, Angular is the front-end code, so even if you need to install Node.js on your development computer, it would just be for running the CLI. 

When you create your production software, you won’t need Node.js because the final bundles are static HTML, CSS, and JavaScript can be supported by any server or CDN. If you are building a full-stack web application with Angular, you may need Node.js to create the back end if you want to use JavaScript for the front end and back end. 

Deploy the Angular CLI  

Open the terminal and type below command with help of NPM to install angular CLI: 

You can run many commands once you download Angular CLI, such as: 

After you have installed Angular CLI, you will need to run one command to create a project and another to support it using a local development server to play with your program. 

You can use Angular CLI to create your first Angular project by executing the below command in your command line interface: 

The command ng new will prompt you to include default a feature in the initial request. Usethe Enter or Return key to acknowledge the defaults. 

Note: ‘Helloworld’ is the name of this project. Of course, you can select any appropriate name for your project. Because this is our initial set up, I’mgoing with‘hello world’ as a name for the front-end application. 

That will launch http:/localhost:4200/  

* Note: ng serve command launches a server, searches for changes to your files, and rebuilds the software when you save changes * 

Open file in dev/myproject/helloworld/src/app/app.component.ts: 

Edit the file and make some changes and save it. Changes will be highlighted:  

Within app root (app root is in /myproject/helloworld/ for our app,), Run: 

ng serve –open or just navigate tohttp:/localhost:4200/ . The web UI will reflect the changes. 

With ng serve still running, make and save a new change in app.component.ts:  

You will be surprised to see the changes automatically reflected in  http:/localhost:4200/  

This command creates a new folder in your app root named “/dist/” which is your entire package, compiled and ready for shipment. 

Nice Flags optional:  

In this guide, we have gone through the steps to setting up a solid local environment to facilitate the best Angular learning and work experience. Following these steps, you will ensure that the development environment is easily upgradeable, minimizing the possibility of any issues arising over time due to things like semantic versioning. 

We have discussed how to set up your Angular Development environment using the Angular CLI method, the prerequisites necessary and walked through how to install a CLI, how to build an initial workspace and starter app, and how to run the app locally to test your setup. 

I hope this has been a useful guide and I wish you the very best Angular learning and work experience!  

  • Node 
  • NPM 
  • Editor-Visual Studio Code 
  • Angular CLI 
  • It is very useful to be able to quickly change node versions if, for example, there are multiple projects to be maintained on the same machine, and each project needs different node versions.  
  • With this tool, it will be much easier to upgrade to newer versions of the node in the future – we will not have to run the installer again on your machine. 
  • To check version of CLI: $ ng version 
  • To add support to your project for an external library: $ng add 
  • To compile the Angular app in the output directory called dist.or in the output path defined; should be executed from inside a working space directory: $ng construct 
  • To retrieve or set Angular configuration values: $ng configure 
  • To open the official Angular Documentation (angular.io) in the browser and searches for a keyword: $ng doc 
  • To generate and/or change schematic-based files: $ng generate  
  • To list the commands available and their brief descriptions: $ng help  
  • To run lining tools on the Angular device code in the project folder: $ng lint 
  • To build a new workspace and an initial Angular app: $ng new  
  • To run a custom target set for your project: $ng run  
  • To develop and support your request, restore on file changes: $ng serve 
  • Prod this creates a version of your app ready for production  
  • output-path /to / your / path/ this changes the default path to create your Angular files  
  • output-hashing none remove additional hash on your file name
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    Top Real World Use Cases and Applications of MongoDB

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    Top Real World Use Cases and Applications of MongoDB

    With the evolution of several databases, choosing the right database has become an important aspect of the design. On one hand we have RDBMS databases like Oracle, SQL Server, Postgres and DB2 while NoSQL databases like MongoDB, Cassandra and HBase have been prominent. Due to cut-throat competition today, choosing the right database based on the application use case could mean having an advantage over the competitor.

    MongoDB’s focus is not only on the database but on overall data. They have several services like MongoDB Atlas, which is a database-as-a-service on multi-cloud, MongoDB stitch used to build faster and better applications on serverless environment.

    In this blog, we will discuss some of the real-world use cases of MongoDB.

    Customers these days want to have their business on the smartphone. Millions of users use their applications constantly. RDBMS cannot handle such large simultaneous transactions, and MongoDB provides a cost-effective way to scale the users and mobile app development. MongoDB Mobile applications have been used by several financial bodies, healthcare providers, and retail giants. MongoDB’s flexible data model and rich query functionality enable teams to build killer mobile apps and help customers to grow their business.

    Enterprises like Automatic Data Processing, Inc. (ADP) and The Weather Channel have been very vocal about their awesome experience of using MongoDB for their mobile apps to grow their business.

    ADP has satisfied more than 41000 of its clients managing their employee’s finances, health, and working lives.

    Employees can see information of their health, paychecks, insurance, and other benefits on the mobile app. Currently, the ADP mobile app is used in 17 countries and 23 languages serving more than one million users. Low cost and minimal downtime were needs for a mobile app that MongoDB was able to deliver.

    The Weather Channel was running their website, weather.com, and facing issues serving a large number of clients due to the use of the traditional RDBMS database. MongoDB helped them build their Mobile app serving more than 40 million users and providing real-time weather data to their customers.

    The Weather Channel also used the MongoDB flexible schema and MapReduce features to do live analytics and predictions based on the weather data collected. The development release that was taking weeks earlier were pushed within hours and helped The weather channel have an advantage over their competitors.

    Today, IoT connects billions of devices worldwide. Companies are redefining their revenue models, improving productivity, and leveraging operational efficiency by realizing the business value of connecting all things. MongoDB helps in capturing most from IoT devices. IoT devices have event-driven real-time architecture, require high-speed data ingest and fast development cycles to match changing needs, and MongoDB serves all these things efficiently.

    MongoDB intelligent data platform accelerates the delivery and operation of IoT devices. MongoDB has bonded with technologies like Apache Kafka to be part of an integrated and event-driven IoT platform.

    Bosch is leading the charge for IoT betting on MongoDB for building their application. Bosch with more than 300000 employees, is known as one of the largest automotive component manufacturers.

    Bosch uses several apps collecting data from IoT like braking system and power steering to improve diagnostics and preventive maintenance needs. Bosch can now also monitor how operators use highly advanced power tools to tighten more than 6 million screws of aircraft. MongoDB has played an important role in building such modern apps.

    In the 90’s one could build the website with static text, but today with the changing times, a website must have array of text, audio, video, and social media to get the user’s attention. Building such an application on a relational database is not easy. MongoDB provides the customers with the ability to have such content on a single database as it supports a variety of structured and unstructured data.

    As a story goes viral, people visit whatever website they can to get the information. To retain the reader, publishers have to be on their toes and provide share-worthy content as quickly as possible.

    Using MongoDB, Forbes was able to build its CMS within two months and its mobile application within one month. Forbes changed its entire website and moved to MongoDB so that content can be added from anywhere around the globe without going offline in a quick manner.

    The publishers leveraged flexible schema of MongoDB which provided the dynamic quality content in quick time to their readers. Their move was bold, and it paid off as they got rid of traditional systems without increasing operational cost.

    Data has been an integral part of video games. From managing player profiles to leaderboards, data plays an important role in making gaming better. But now, with most of the games being played online where one may start the game with a small number of users but need to scale to millions of users in no time. Choosing the correct database can prove to be a game-changer as multiplayer playing online with scale capabilities can make your game popular.

    Massive scale, globally available, and always on are some of the features that are a must for modern games. Fortunately, MongoDB has proved its worth in providing clients with these features at a competitive price. Many gaming companies are leveraging MongoDB atlas, which is a multi-cloud database-as-a-service helping scale up and down automatically.

    Popular Gaming companies like SEGA, FACEIT, and Lucid Sight are successfully using MongoDB for their users’ better gaming experience.

    FACEIT uses MongoDB as their main database under the hood. Orchestrating the services between players, teams and competitions are all managed by MongoDB.

    FACEIT uses MongoDB even to manage all user-profiles and tournament data. Live streaming data from the game is stored in MongoDB and analytics to track player behavior and engagement is done on data. MongoDB’s flexible schema and rich query model helped FaceIT to maintain user profiles efficiently.

    With RDBMS, there was a culture of having transactional and analytics databases separate. Daily data load was needed to move data from the transactional database to the analytics environment. With MongoDB, companies can analyze the data in real-time with less money.

    Chicago’s Department of Innovation and Technology (DoIT) used MongoDB for analytics to cut the crime and citizen welfare using an analytics platform called WindyGrid. Data from different stations, 911 calls and Tweets are analyzed using MongoDB to better respond to emergencies. From better managing city traffic to garbage complaints, all are done using the WindyGrid application.

    Many retail companies have the requirement of real-time analytics. For this, their applications must be up all the time and no downtime can be afforeded. Otto is one of the largest e-commerce companies. With the cut-throat competition among retail clients, slow reaction time causes a lot of business loss as with many options, consumers don’t stay long.

    Otto used MongoDB to reduce their reaction time to 1-2 seconds. This was a huge challenging as we are talking about more than 500 brands on their website. The flexible schema capability combined with availability and scalability features helped Otto to reconstruct their entire catalog application on MongoDB.

    Even as we enter the Big Data era with new databases dominating the market, Mainframe continues to have a place in infrastructure despite high operational cost. Many have found moving data off the Mainframe to be a difficult task, but MongoDB has proven to be way ahead in offloading data from mainframe systems efficiently, thus modernizing the apps and reducing the operational cost.  

    Human capital services like Alight Solutions have been successful in offloading their data from Mainframe to MongoDB, thus improving application performance by 250× and reducing the overall cost of operations.

    MongoDB has grown fast and has taken over in the field of the database. There are several other use cases like managing biometric data of 1.3 billion Indians for Aadhar. This huge data is stored by Ministry of Electronics and Information Technology, India, in MongoDB. Among several databases, MongoDB is specifically used for storing images.

    Internet-based popular photo-sharing vendor Shutterfly uses MongoDB for storing more than 6 billion images handling 10,000 transactions per second. The volume of data and high transaction rate forced them to move off Oracle to MongoDB.

    Metlife was among one of the first insurance company that took the initiative to migrate their advanced customer service application famously known as ‘The Wall’ from legacy database to MongoDB. The application servers more than 90 million users across different continents managing their employees’ insurance, benefits, and annuities.

    MongoDB also continues to focus on database-as-a-service. MongoDB Atlas enables clients to run managed MongoDB on AWS, Azure and GCP cloud. With Atlas, MongoDB continues to acquire new business every quarter.

    Apart from database solutions, MongoDB has also captured the market due to other services like MongoDB Stitch and MongoDB Realm. Seeing the R&D being put for the development of new services and focus on solving real-time issues, it might not be a bad choice to bet on MongoDB in the future.

    In summary

    Although it has taken a while for MongoDB to catch the wind, its popularity has clearly soared in the last few years. The demand for MongoDB database has increased exponentially and it has become one of the most used databases due to its flexible schema, MapReduce capability, and Scale performance.

    With DB-engine, which is recognized for rating databases, recognizing MongoDB as the Database Management System in 2019, we are sure to see its use growing every more rapidly.

    Research & References of Top Real World Use Cases and Applications of MongoDB|A&C Accounting And Tax Services
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    How to Get the Best Out of Your Machine Learning Course

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    How to Get the Best Out of Your Machine Learning Course

    As a programmer, you understand well how a program works: it runs based on certain commands and statements written by you. However, some smart people asked whether it would be possible for a program to learn things depending upon past experiences and improve its decision-making ability to enhance its overall performance.

    This is the most fundamental and simplified version of the idea of Machine Learning.

    The term “Machine Learning” was coined by American Pioneer, Arthur Samuel. Arthur defined Machine Learning as “the field of study that gives computers the ability to learn without being programmed explicitly”.

    In simple terms, Machine Learning is the science of getting things done using intelligent machines. It is a subset of Artificial Intelligence. It teaches a computer system to make precise predictions when some data is given as input. A Machine Learning model can make predictions by answering questions like whether a piece of fruit in a picture is an orange or mango, whether an email you received is spam or not, or recognizing speeches in a YouTube video for generating captions.

    A Machine Learning algorithm is fed with data and information in the form of observations and real-world interactions. It studies the data available and improves its learning over time in its own way until the algorithm can make decisions and predictions.

    The applications of Machine Learning are widely used in several sectors ranging from science, telecom, healthcare, production, and so on.

    If you want to become an expert in Machine Learning, you need to follow several steps which require you to invest a significant amount of time to learn about the principles behind it and acquire a firm grasp on it. The steps to learn Machine Learning in the most efficient way is described below.

    Machine Learning is a deep domain technology and before you get started with ML, you need to spend a couple of weeks grasping the “general and basic knowledge” about the field of Machine Learning.

    In the beginning phase, you should become well aware of the detailed and correct answers to the following questions:

    After you have gathered the fundamentals, you can head on to the other related domains which are often associated with Machine Learning: Analytics, Data Science, Big Data, and Artificial Intelligence.

    If you want to become an expert, you need to interpret the finer details of all the topics mentioned earlier. Try to understand the concepts in your own specific manner so that you can explain it in a simple way to just about anyone.

    Recommended exercise

    Write a blog about “The Basics of Machine Learning” on any blogging website. Your article must answer questions about Machine Learning considering that it is asked in an interview.

    Data plays a very important role in the field of Machine Learning. In your Machine Learning career, you will have to spend most of your time working with data. This is where statistics comes in picture.

    Statistics is a field of mathematics that deals with the collection and analysis of data and also explains how you can present your data efficiently. It is a prerequisite for understanding Machine Learning deeply.

    Though it is said that you can achieve to be a Machine Learning expert without any such expertise in statistics, it is also considered that you cannot completely avoid statistical concepts when the question is about Machine Learning and Data Science.

    The concepts you need to learn in the domain of statistics are –

    You can also gather information about the Bayesian model and its various concepts which tend to be an essential part of Machine Learning.

    Recommended exercise
    As an exercise in Statistics, you can create a list of references for each topic mentioned above which will explain them in the easiest manner and then put it out in a blog.

    If you want to become a master in any programming language, it could well take an eternity. However, in your quest of becoming a Machine Learning expert, you need to get familiar with learning a language. Experts say this it is not too difficult.

    There are numerous languages like Java, C, C++, Scala, Python, R, etc. by which you can implement your Machine Learning algorithms. However, Python and R are the most popular languages, and learning one can certainly make it easy to learn the other.

    Most of the experts prefer Python since it is easier to build Machine Learning models in this language than any other programming language. While Python is best for writing code related to Machine Learning, but when it comes to managing a huge amount of data for a Machine Learning project, experts suggest R.

    Python also offers certain libraries that are specifically built for Machine Learning like Keras, TensorFlow, Scikit-learn, etc. Thus, it can be said learning both Python and R can be an upper hand in your journey of becoming a Machine Learning expert.

    Now that we have covered the prerequisites, let us reach out to the heart of Machine Learning. Algorithms are an important part in the world of programming. You need to learn about all the algorithms particularly designed for Machine Learning and the applications of these algorithms in your projects.

    Machine Learning is a wide field of study and algorithms act as the bread and butter in your journey of learning it. Along with Machine Learning algorithms, you should also know about the types and building blocks of Machine Learning:

    Learn about all the concepts in detail such as what do they mean and why they are used in Machine Learning.

    The most fundamental idea of any Machine Learning model is that the model is given a large amount of data as input and the corresponding output is also supplied to them. Here, we will take into consideration the two common Machine Learning models – Unsupervised learning model and Supervised learning model.

    Unsupervised learning is a Machine Learning technique where the model works on its own to discover information. It uses unlabeled data and then finds the internal pattern in the data to learn more and more about the data itself. It can be used in a situation where you are given data about different countries as input and you need to find out the countries similar to each other based on a particular factor like population or health.

    Some of the concepts you need to learn about Unsupervised learning algorithms are –

    A supervised learning algorithm is a Machine Learning algorithm that takes place in the presence of a supervisor or a teacher. The training dataset is well labeled, and this learning process goes on until the required performance is obtained. It is useful in a situation where you need to identify if someone is likely to acquire a disease depending upon factors like lifestyle and habits.

    Some of the concepts you need to learn about Supervised learning algorithms are –

    Recommended exercise
    As an exercise on learning models, you can take a certain dataset and create models with the help of all the algorithms you have learned. Train and test each of the models to enhance their performance.

    Data Science competitions provide a certain platform to interact and compete in solving real-world problems since most data scientist’s work is theoretical and they lack the skill of working with real-world data.

    Competitions are the best place to learn and augment your skills in Machine Learning and they also act as an opportunity to enhance boundaries and promote creativity among the brightest minds. The experience you gather from these competitions will help you to develop the most feasible solutions while working with big data.

    Some of the most popular data competitions to practice Machine Learning algorithms are listed below:

    Deep Learning is a subfield of Machine Learning which is more powerful and flexible since its process of learning considers the world as a series of concepts where each concept is explained with some other simpler concepts.

    The popularity of Deep Learning is because it is power-driven by a huge amount of data. Smartphone assistants like Google Assistant or Siri were created with the help of deep learning models. They also helped global companies to build self-driving cars.

    Machines in this era can perform all the basic things that a human can perform like see, listen, read, write, and even speak to deep learning models. They are also a great influence on enhancing the skill set of people working on Artificial Intelligence.

    Some of the topics you can cover to gather detailed insights about deep learning models are –

    Recommended exercise
    Create a model that can identify a flower from a fruit.

    Big Data refers to the large volume of structured and unstructured data that business giants use for analyzing insights to make better decisions. A massive amount of data is used in day-to-day applications and managing such a huge amount of data is possible because of Big Data.

    Big Data uses analytical techniques like Machine Learning, Statistics, Data Mining, etc. to perform multiple operations on a single platform. It allows storing, processing, analyzing, and visualizing data with the help of different tools.

    Big Data technologies provide meaning to the machine learning models that have been around for decades. The models now have access to a sufficient quantity of data that can be given as input to the Machine Learning algorithms so that they can come up with outputs useful to organizations.

    They have found applications in different sectors starting from Banking, Manufacturing, and to different Tech industries.

    Learn about the following concepts in Big Data to enrich your knowledge about the technologies used:

    Recommended exercise

    As an exercise, install a local version of Hadoop or Spark and upload data to run processes. Extract the results, study them, and find different ways to improve them.

    Finally, working on a Machine Learning project is very crucial as it helps to demonstrate your knowledge and skills on the subject. Since you are a beginner, start with a sample machine learning project like a social media sentiment analysis with the help of Facebook or Twitter.  
    Some of the topics you can cover under this section are:

    The steps you need to follow while working on a Machine Learning project are:

    The Internet has a plethora of different sources and materials where you can start learning Machine Learning. Some of the most popular courses on Machine Learning along with Certifications are:

    Machine Learning is an expanding field and having a set of skills on Machine Learning is an investment for the future.

    You can establish a firm foundation with the Machine Learning with Python course, where you will study machine learning techniques and algorithms, programming best practices, python coding, and more. This foundations course is intended to help developers of all skill levels get started with machine learning.

    Machine Learning is an area where learning will never stop and if you plan your journey of becoming a Machine Learning expert in a well-rounded manner, you will indeed realize the next steps to rapidly propel your learning curve.

  • What is Machine Learning?
  • What is the capability of Machine Learning?
  • What are the merits of learning Machine Learning?
  • What are the limitations of Machine Learning?
  • What are the applications of Machine Learning?
  • Significance of Statistics
  • Data Structures and Variables
  • Basic principles of Probability
  • Probability Distributions
  • Hypothesis Testing
  • Regression model
  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Data Preprocessing
  • Ensemble Learning
  • Model Evaluation
  • Sampling & Splitting
  • What is Clustering?
  • What are the types of Clustering?
  • What are Association rules?
  • What are Regressions?
  • What are Classification Trees?
  • What are Vector Machines?
  • Kaggle
  • International Data Analysis Olympiad (IDAHO)
  • Topcoder
  • DataHack and DSAT
  • Machine Hack
  • What are Neural Networks?
  • What is Natural Language Processing or NLP?
  • What is TensorFlow?
  • What is OpenCV?
  • What is Big Data and its ecosystem?
  • What is Hadoop?
  • What is Spark?
  • How to collect, clean, and prepare data?
  • What is Exploratory Data Analysis?
  • How to create and select a model?
  • Deciding what problem, you want to solve.
  • Deciding the required parameters.
  • Choosing the correct training data.
  • Deciding the right algorithms.
  • Writing the code.
  • Checking the results.
  • Stanford’s Machine Learning Course
  • Harvard’s Data Science Course
  • Machine Learning by fast.ai
  • Deep Learning Course by deeplearning.ai
  • Edx Machine Learning Course
  • Research & References of How to Get the Best Out of Your Machine Learning Course|A&C Accounting And Tax Services
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    How to concatenate strings using Python

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    How to concatenate strings using Python

    The string data type in Python is a very important building block of programming. It is basically a sequence of one or more characters that represent Unicode characters. The characters could be letters, numbers, or symbols. The strings are immutable in nature, which means they are unchanging.  

    You can implement string formatting techniques in Python like merging or splitting strings in Python. When you merge or combine two or more strings in Python, it is called string concatenation

    In this article, we will understand what concatenation is and its importance. We will delve into different ways of concatenating strings including the + operator, * operator and % operator and take you through various concatenation methods including the join() method, format() function, the f-string and StringIO methods. 

    String Concatenation is the operation of joining character strings end-to-end. If you have just started working on Python, you might come through a time when you will need to merge or combine the contents of two or more strings together. In technical terms, this merging or combining of strings together into a single string is called String concatenation. 

    The simplest way to explain concatenation in Python is when you take two separate strings stored in the Interpreter and combine them so that they become one single string. 

    For example, if you take one string as “foot” and another string as “ball” and then merge them into using concatenation technique it comes out to be a single string “football”. 

    There are several ways in which you can perform string concatenation in Python. However, the simplest method is using the “+” operator. 

    String formatting in Python is a robust and important part of the toolkit of any Python programmer. String formatting techniques have greatly evolved since the time Python was developed. Almost every piece of production software created has its advantage in some way or the other.  

    Formatted strings in Python are evaluated at run time which acts as a basic capability of any high-level language. At a basic level, String concatenation using the “+” operator might seem inefficient and also difficult to make expressive. This is where Python’s string formatting starting from the “%” formatting to the format() method comes into action. They exhibit great potential when it comes to crafting strings. 

    Python comprises of a number of ways when it comes to concatenate or combine strings together. Since Python is an object-oriented programming language, everything in Python is an object. So, the new string that is created after concatenation is also referred to as a string object in Python.  

    Let us see what are the different ways by which we can concatenate strings in Python. 

    The simplest and most common method of concatenating a string is using the plus symbol (“+”). Let us see an example to understand it better: 

    Pythoniscool

    Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the “+” operator and display the output using the print statement. The output is the combination of the three strings together.  

    You might use the “+” operator when you have few strings to concatenate. This is because strings are immutable i.e. they cannot be changed once created. So, for each concatenating statement, the interpreter creates a new object. Thus, it will be quite inefficient if you try to concatenate many strings using the “+” operator. 

    Another disadvantage of the “+” operator is that it does not allow any separator or delimiter between the strings. If you want to concatenate “Hello” and “World” with whitespace as a separator, you need to fo something like this “Hello” + “ ” + “World” and the output will be “Hello World”

    The asterisk (*) operator is used when you want to concatenate the same string repeatedly. For example, if you have a string “red” and you want the same string to be concatenated three times, you use the * operator. The result will be redredred.  

    An example to illustrate concatenation of string using “*” operator: 

    a = “Python” 

    print(a * 3) 

    PythonPythonPython 

    Here, we have declared a single string variable “a” with a string value. Then, we concatenate the string with the help of the “*” operator and display the output using the print statement. The output combines the string with the same string three times repeatedly. 

    The join() method is the most flexible way of concatenating strings in Python. If you have many strings and you want to combine them together, use thejoin(method. It is a string method and the most interesting thing about join() is that you can combine strings using a separator. It works on iterators like lists, tuples, string, dictionaries, etc.  

    An example to illustrate concatenation of string using “*” operator: 

    Welcome-to-Python 

    Here, we have declared three string variables “a”, “b” and “c” with three different string values. Then, we concatenate the three strings with the help of the join(method with “-” as a separator and display the output using the print statement. The output is the combination of the three strings together with dash (“-”) operator in between the strings. 

    The modulus operator (“%”) can be used for both string formatting and string concatenation. It is useful for cases in which you need to combine strings and also perform basic formatting. 

    An example to illustrate concatenation of string using “%” operator: 

    Apple Shake 

    Here, we have declared two string variables “a”, and b”with two different string values. Then, we concatenate the two strings with the help of the (“%”) and display the output using the print statement.  

    The “% s” denotes the string data type in Python and the modulus (“%”) operator combines the string stored in the two variables “a” and “b”. The string value in the variables is passed to the string data type and the output is displayed as the combination of two strings. 

    The str.format() function is a powerful function in Python which is also used for both String formatting and String Concatenation. This function combines different elements within a string through positional formatting.     

    An example to illustrate concatenation of string usingformat() function: 

    Virgin Mojito 

    Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the format(function and display the output using the print statement.  

    The curly braces (“{}”) used here are used to fix the string position. The first variable is stored in the first curly braces and the second one in the second curly braces. The job offormat(function is to concatenate the strings stored in variables “a” and “b” and display the combined string. 

    Formatted string literals or f-strings, in short, are string literals in Python. They contain an at the beginning and curly braces that contain the expressions. It calls the str() method when an object argument is used as field replacement. 

    Let us see an example to illustrate the concatenation of string using f-string

    Moscow Mule 

    Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the f-string and display the output using the print statement.  

    The f-string expressions are evaluated at runtime and they are being formatted using the __format__ protocol in Python. It is considered to be a cleaner and easier way of concatenating strings in Python when compared to the format() function.

    String concatenation using StringIOis also a very flexible way for combining different strings in Python. In this method, we have to import the StringIO() function from the IO module.  

    An example to illustrate the concatenation of string usingStringIO

    Machine Learning 

    Here, we have declared two string variables “a” and b”with two different string values. Then, we concatenate the two strings with the help of the StringIO() imported from the IO moduleand display the output using the print statement.  

    Here, the variable “a”acts as a file object in Python. The write() function is used here to write the string to the fileand the getvalue() function returns the entire content of the file. 

    We have covered all the ways by which we can concatenate different strings in Python. Let us see some few more miscellaneous examples to understand String Concatenation better. 

    There are various ways by which you can concatenate multiple strings in Python. The most common among them is using the plus (“+”) operator. You can combine both string variables and string literals using the “+” operator. 

    However, there’s another method that allows an easy way of concatenating multiple strings. It is using the in-place (+=) operator. The in-place operator concatenates the sequence with the right operand and the result gets assigned to that sequence. 

    Let us see an example of string concatenation using the (“+=”) operator: 

    Artificial Intelligence 

    Here, two string variables “a” and “b” are declaredwith two different string values. The string on the right side of the “+=” operator is combined with the string variable on the left side. Then, the output is displayed using the print statement.  

    You can also add a string to the end of a string variable using the “+=” operator: 

    Basketball 

    Another way of concatenating multiple strings in Python is just by writing string literals consecutively: 

    RedGreenBlue

    There are numerous ways of concatenating strings in Python. However, not all methods can concatenate strings and numbers. If you use the “+” operator to combine strings and numbers, it will raise errors. This is because strings can hold any recorded characters but numbers like  integers or floats are recorded number value. 

    The error shows that the interpreter can concatenate a string value with another string value but cannot concatenate a string value with an integer. Although, you can overcome this problem with the help of the str() function in Python. It converts any integer or floating-point number into a string.  

    Let us see the same example with the str() function: 

    Rolls Royce 1948 

    Thestr() function converts the integer value 1948 into a string and then it is concatenated with variable “a” and the output is displayed using the print statement. 

    You can also use theformat() function when you need to convert a number with decimal places or zero padding. 

    You can concatenate a list of strings into one string using the join(method. It takes a character as a delimiter string. If you use an empty string as the delimiter, the list of strings will be simply concatenated without any separator.  

    Let us see an example to concatenate a list of strings using the join() function: 

    Here, the variable “a” is a list declaredwith four different string values. We have used newline (“n”) as the delimiter in thejoin() method which inserts a newline for each of the strings. 

    The output is the four strings with each string in a newline. 

    You can use any other delimiter like comma (,) or hyphen (-) in the join() method and then perform concatenation. Also, note that thejoin(method can also concatenate other iterators like tuples, sets, dictionaries, etc

    Python does not allow the concatenation of strings with numbers or numbers with numbers. However, you can convert a numeric value into a string using the str() method and then perform concatenation. 

    If you want to combine a list of numbers into one string, the first thing you need to do is convert each integer in a list to a string using the str() function. Then, combine all the converted strings into a single string with thejoin(method. 

    Let us see an example to understand it better: 

    Here, the variable “a” is a list declaredwith five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter.

    Here, the variable “a” is a list declaredwith five integer values. We convert each of the integers into a string using the str() function and store it in variable “b”. Then, we combine them together using the join() method with a colon (;) as the delimiter. 

    Now let me give you some useful tips on String concatenation in Python: 

    Let us summarize what we have learned in this article so far –  

    Concatenation is a crucial part of String manipulation in Python. There are numerous ways to perform concatenation. However, some are more useful than others in some cases.  

    Now that you have quite an experience in concatenating strings, you can look out for other string formatting methods that Python provides or you can check out the PEP article on Advanced String Formatting on Python.org for more information. 

  • The string-formatting operator “%” is a potentially fast and suitable operator when you need to concatenate a few pieces of string. Also, you don’t need to call the str() function when combining numbers because this operator does it implicitly. It also enhances the readability of the code. 
  • Thejoin() method is the fastest, cleanest, and most elegant and readable method when you need to concatenate many small pieces of string into a larger string. 
  • When you have many small pieces of strings that come either from input or computation and are not in a sequence, always use a list to contain the strings. You can use list comprehension or append method in Python to arrange your list in a sequence.  
  • Concatenation and its importance. 
  • Different ways of concatenating strings. 
  • Some miscellaneous concatenation methods. 
  • Important tips on concatenating strings. 
  • Research & References of How to concatenate strings using Python|A&C Accounting And Tax Services
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    How to use Split in Python

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    How to use Split in Python

    There are times where we need to break down a large string into smaller strings. In such cases, the split function comes into action. Basically, it is a string manipulation tool in Python.A string is a collection or array of characters in a sequence that is written inside single quotes, double quotes, or triple quotes. A character ‘a’ in Python is also considered a string value with length 1.  

    Strings represent Unicode character values and are mutable in nature which means the value of a string cannot be altered after it has been declared.  

    An example of declaring and displaying a string in Python: 

    Although we cannot change a string after the declaration, we can split a string into different strings using a variety of different ways in Python.  

    In this article, we will take a deeper dive and understand how to use Split is in Python. We will begin by understanding what the Split function does, what the need is for such a function and how we work with this function. We will then take a look at Split parameters in Python and the different ways of using the Split function. 

    If you have worked on the concatenation of strings that are used to merge or combine different strings into one, the split function performs just the opposite of it. The function scans through a string and separates it when it encounters a separator which has been specified before.  

    However, if the function does not find any defined separator, it uses white space by default.  

    The syntax of the Split function is as follows: 

    The separator is a character that has been pre-defined and it gets placed between each variable in the output. The split function depends on the value of the separator variable. 

    The Split function returns a list of words after separating the string or line with the help of a delimiter string such as the comma ( , ) character. 

    Some of the merits of using Split function in Python are listed as follows: 

    Strings variables in Python contain numeric and alphanumeric data which are used to store data directories or display different messages. They are very useful tools for programmers working in Python.  

    The .split() method is a beneficial tool for manipulating strings. It returns a list of strings after the main string is separated by a delimiter. The method returns one or more new strings and the substrings also get returned in the list datatype.  

    A simple example of the split function is as follows: 

    Here, we have declared a string variable with three strings. When the split function is implemented with a comma ( , ) as a separator, the strings get separated with commas in between them.  

    The Split function analyses through a string and separates it whenever the program comes across a pre-defined separator. It depends on mainly three different parameters to optimize the execution of the program: 

    Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: 

    Python consists of several different ways by which we can implement the Split function. The different techniques are explained below: 

    The split() method in Python splits the string on whitespace if no argument is specified in the function. An example of splitting a string without an argument is shown below: 

    The output of the above code is as follows: 

    In the example above, we have declared variable str with a string value. You can see that we have not defined any arguments in the Split function, so the string gets split with whitespaces.  

    When we split a string based on the first occurrence of a character, it results in two substrings – the first substring contains the characters before the separator and the second substring contains the character after the separator.  

    An example of splitting a string on the first occurrence of a character is shown below: 

    The output of the above code is as follows: 

    Here, we have declared str with a string value abcabc. The split function is implemented with separator as “c” and maxsplit value is taken as 1. Whenever the program encounters “c” in the string, it separates the string into two substrings  – the first string contains characters before “c” and the second one contains characters after “c”.  

    When you want to split a file into a list, the result turns out to be another list wherein each of the elements is a line of your file. Consider you have a file that contains two lines “First linenSecond Line”. The resulting output of the split function will be [ “First Line”, “Second line”]. You can perform a file split using the Python in-built function splitlines()

    Consider you have a file named “sample.txt” which contains two lines with two strings in each line respectively – “Hi there”, “You are learning Python”

    An example of splitting “sample.txt” into a list is shown below: 

    The output of the above code is as follows: 

    We have a file “sample.txt” which is opened in read (“r”) mode using the open() function. Then, we have called f.read() which returns the entire file as a string. The splitlines() function is implemented and it splits the file into two different substrings which are the two lines contained in “sample.txt”

    You can split a string using the newline character (n) in Python. We will take a string which will be separated by the newline character and then split the string. The newline character will act as the separator in the Split function.  

    An example of splitting a string by newline character is shown below: 

    The output of the above code is as follows: 

    Here, we have declared a variable str with a string that contains newline characters (n) in between the original string.The Split function is implemented with n”  as the separator. Whenever the function sees a newline character, it separates the string into substrings.  

    You can also perform split by newline character with the help of the splitlines() function. 

    Tabs are considered as escape characters “t” in text (.txt) files. When we split a string by tabs, the Split function separates the string at each tab and the result is a list of substrings. The escape character “t” is used as the separator in the Split function. 

    An example of splitting a string by tab is shown below: 

    The output of the above code is as follows: 

    Here, the variable str is declared with a string with tabs (“t”). The Split function is executed with “t” as the separator. Whenever the function finds an escape character, it splits the string and the output comes out to be a list of substrings. 

    We can also split a string by commas (“,”) where commas act as the delimiter in the Split function. The result is a list of strings that are contained in between the commas in the original string.  

    An example of splitting a string by commas is shown below: 

    The output of the above code is as follows: 

    Here, the variable str is declared with a string with commas (“,”)  in between themThe Split function is implemented with ,”  as the separator. Whenever the function sees a comma character, it separates the string and the output is a list of substrings between the commas in str

    You can split a string using multiple delimiters by putting different characters as separator in the Split function. A delimiter is one or more characters in a sequence that are used to denote the bounds between regions in a text. A comma character (“,”) or a colon (“:”) is an example of a delimiter. A string with multiple delimiters can be split using the re.split() function. 

    An example of splitting a string with multiple delimiters is shown below: 

    The output of the above code is as follows: 

    In the example above, we import the built-in module re which imports the libraries and functions of Regular Expressions. The variable str is declared with a string with multiple delimiters like newline (n), semicolon (;), or an asterisk (*). There.split() function is implemented with different delimiters as separator and the output is a list of strings excluding the delimiters.  

    When you split a string into a list around a delimiter, the output comes out to be a partitioned list of substrings. You can take any delimiter as a separator in the Split function to separate the string into a list. 

    An example of splitting a string into a list is shown below: 

    The output of the above code is as follows: 

    The variable str is declared with a string with dash characters( – ) in between and the Split function is executed with a dash ( – )  as the separator. The function splits the string whenever it encounters a dash and the result is a list of substrings. 

    You can also split any string with a hash character (#) as the delimiter. The Split function takes a hash (#) as the separator and then splits the string at the point where a hash is found. The result is a list of substrings.  

    An example of splitting a string using a hash is shown below: 

    The output of the above code is as follows: 

    The variable str is declared with a string with hash characters( # ) in between them. The Split function is executed with a hash as the separator. The function splits the string wherever it finds a hash  ( # ) and the result is a list of substrings excluding the hash character. 

    The maxsplit parameter defines the maximum number of splits the function can do. You can perform split by defining a value to the maxsplit parameter. If you put whitespaces as separator and the maxsplit value to be 2, the Split function splits the string into a list with maximum two items.  

    An example of splitting a string using the maxsplit parameter is shown below: 

    The output of the above code is as follows: 

     Here, you can see the variable str is declared with a string of different subject names. The Split function takes whitespace (“ ”) as a separator and the maximum number of splits or maxsplit is 2. The first two strings Maths and “Science” are split and the rest of them are in a single string. 

    You can separate a string into an array of characters with the help of the list(function. The result is a list where each of the element is a specific character.  

    An example of splitting a string into an array of characters  is shown below: 

    The output of the above code is as follows: 

    Here, the variable str is a string. The string is separated into individual characters using the list(function and the result is a list of elements with each character of the string. 

    You can obtain a string after or before a specific substring with the split() function. A specific string is given as the separator in the Split function and the result comes out to be the strings before and after that particular string.   

    An example of splitting a string using substring  is shown below: 

    The output of the above code is as follows: 

    Here, the variable fruits is a string with names of different fruits. We take the string “Mango” as the separator in the Split function. Whenever the function finds the string “Mango”, it splits the whole string into two substrings – one substring before “Mango” and another substring after “Mango”.  

    Since we have now reached at the end of the article, let me give you some useful tips on the Split function: 

    The .split() function in Python is a very useful tool to split strings into chunks depending upon a delimiter which could be anything starting from characters or numbers or even text. You can also specify the number of splits you want the function to perform using maxsplit, which is used to extract a specific value or text from any given string using list or Arrays. 

    Here are the key areas you should have gained a good understanding on by reading this article: 

    You have learned about the Python split function and the different ways to implement in your program. With this, you can begin to work on any project which requires the use of the Split.  

    If you wish to extend your knowledge about Strings and Split function in Python, you can refer to the official documentation of Python. Also, don’t forget to check out the remaining tutorials made freely available to you. 

  • It is useful in situations where you need to break down a large string into smaller strings. 
  • If the separator is not present within the split function, the white spaces are considered as separators. 
  • The split function helps to analyze and deduce conclusions easily. 
  • It is also useful in decoding strings encrypted in some manner.  
  • Separator – It instructs Python where to break the string. It works as a delimiter and the string is separated depending upon the pre-defined separator. It is optional which means if the separator is not specified in split, the function uses white space as the default separator. However, if the separator is specified as a string value, the output will be an empty string. 
  • Maxsplit–  It specifies the number of times the string can be broken up. It is also optional and it’s default value is -1 which denotes that there are no limits on the number of times a string can be split. If the maxsplit is not defined in the split function, the entire string is scanned and Python separates it whenever a delimiter is encountered.    
  • Return – It returns a list of strings after the split function breaks the string by the specified separator. 
  • If the maxsplit is not defined in the function and there are enough delimiters in the string, the result will have a length of maxsplit +1
  • If you want to recombine a string that has been already split in Python, you can perform the concatenation of strings.  
  • The Python Split function only works on string variables. If you come across any problem with working with split, you can force the compiler to treat the variable as a string with str(x). 
  • What is a String. 
  • What is Split and why is it needed. 
  • How does a Python Split function work. 
  • What are the Split parameters. 
  • What are the many different ways of Splitting strings in Python 
  • Important tips on Split 
  • Research & References of How to use Split in Python|A&C Accounting And Tax Services
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    Essential Guide to Six Sigma Green Belt Certification

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    Essential Guide to Six Sigma Green Belt Certification

    A Six Sigma Green Belt is a professional who has a thorough understanding of the advanced elements of the Six Sigma Methodology. Green Belts lead improvement projects and also act as team members to be part of more complex improvement projects that are led by Black Belts.  

    Being well versed in all aspects of Six Sigma Methodology, a Six Sigma Green Belt is competent in subject matters of DMAIC (Define, Measure, Analyze, Improve and Control). A Green Belt certified professional is highly proficient in implementing, performing, interpreting and applying Lean Six Sigma. 

    Being a people-driven process, the performance levels of a Six Sigma project are dependent on the expertise, commitment, and persistence of individual team members. When considering the roles that provide a contribution to the project, only the higher roles like that of Black Belt are often considered. However, Green Belts also have a crucial role in improving the performance of projects. 

    Green Belts are efficient team members and their objective is improving process quality. They help in bridging the gap between the real-world application and theory behind Six Sigma. They play an important role in process improvement, project management and data inspection. A Green Belt certification training teaches the candidates about the basic tools that a project team uses and application of DMAIC skills relative to a Six Sigma project. 

    Six Sigma Green Belt certification is meant for candidates who have work experience in business management or supply chain and have an interest in terminologies like waste reduction and continuous improvement. Earning the certification improves the proficiency of the candidates, making them more desirable for employers. The certification is best suited for professionals who are responsible for improving outputs, controlling costs and helping in improving results. 

    Six Sigma certificate can be earned through different providers. Some major certification providers include ASQ, IASSC etc.  

    The following section explains the process for you to get certified in Six Sigma Green Belt,  

    You will first need to learn the relevant subject matter, appear for and pass an examination that tests proficiency and competency in a hands-on environment. You will then need to purchase learning materials to train online or enrol into a classroom training course provided by an accredited training school.  

    The next step is to pass a written examination consisting of multiple-choice questions and secure For the IASSC Certified Lean Six Sigma Green Belt Exam™, you will need to attempt a 100 question, closed book, proctored exam within a 3-hour period.  

    On securing a score of 70% or more, you must complete quality projects related to the concepts of Green Belt. You will then be awarded the professional designation of IASSC Certified Green Belt (IASSC-CGB™) from the International Association for Six Sigma Certification. 

    A Green Belt is an employee of an organization who has training on the improvement methodology of Six Sigma. The main aspect of their full-time job is leading the process improvement to get high-quality products or services. Green Belts spend a considerable amount of time on strategy building and decision-making components of the Six Sigma project planning process.  

    With a thorough knowledge of the overall process, they help deliver feedback and driving performance goals by working with centralized project managers. The main responsibilities of a Six Sigma Green Belt are:

    Earning Green Belt certification equips you with different skills that help the organization you are working for. These skills are useful in day-to-day interactions as well. The training for Six Sigma Green Belt makes you capable and confident enough to discuss complicated subjects, solve problems in an effective manner and provide helpful recommendations 

    When you earn Green Belt certification, you have the confidence of finishing projects effectively and reducing costs of operation for your organization. Generally, organizations need to pay reserve funds for every project for timely completion of project and getting positive output from the project. However, it might not be needed to use reserve funds if Six Sigma Green Belt principles are implemented properly. 

    A Green Belt expert can help in providing better products and services, thus ensuring the loyalty of the customers. A lot of clients look for companies making use of Six Sigma. Green Belt experts improve the reliability of an organization, attracting new customers with the assurance of best quality products or services. 

    Six Sigma can be applied in the sales pitch and advertisement of your business too. By getting a Six Sigma Green Belt certification and implementing Six Sigma, you can help maintain a performance level that is ahead of the competitors. Six Sigma provides you with the required confidence and expertise. It revolves around accumulating information and investigating, which is why the decision-making process won’t be that complex. 

    The requirements to get Six Sigma Green Belt certified are mentioned below: 

    Work experience 

    A Green Belt is an employee who spends some time as a part of process improvement teams. They perform the job of analysing and solving quality problems. They are usually involved with quality improvement projects like Six Sigma. To get Green Belt certified under ASQ, you need to have a work experience of at least three years in an area or more in the Body of Knowledge of Six Sigma Green Belt. The IASSC certification does not have any prerequisites. The job has to be a paid, full-time one. Co-op, paid intern or other course works are not applicable for the work experience requirement.For the CSSC certification, there are no prerequisites. 

    Education:

    There are no educational waivers granted. 

    Expectations 

    A certified Six Sigma Green Belt is expected to: 

    Exam format Six Sigma Green Belt 

    Here is the exam format for Six Sigma Green Belt for all the Accrediting bodies: 

    In the section to follow, we guide you through the certification process through the International Association for Six Sigma Certification (IASSC), the Council for Six Sigma Certification (CSSC) and the American Quality for Quality (AQS). 

    For the IASSC Certified Yellow Belt, you have to apply for the ICYB exam, take the exam, and achieve a passing score. To apply for the exam, you will be contacting your KnowledgeHut representative who will order the exam voucher. The certification fee is not included in the course fee. After this, you will be able to take the exam. 

    While applying for the ASQ Six Sigma Green Belt exam, you have two options: 

    This is a one-part exam is offered only in English and it consists of 110 questions and has a duration of four and a half hours. 

    This one-part exam consists of 100 questions and has a duration of four hours. This exam is offered in English, Mandarin and Spanish, based on specific locations. 

    Depending on what you prefer, you can visit your exam provider’s website and apply for the exam by following relevant steps. Prometric is one such testing partner. Here is how you can schedule your exam. 

    For the CSSC Six Sigma Green Belt exam, follow the below-mentioned steps: 

    It is always important to be promptly notified of the exam results. The results are usually provided in quick time. If you have taken the IASSC Certified Lean Six Sigma Green Belt Exam™, you will be provided your results through email within 1 to 2 days after giving the exam. If you pass the exam, you will be getting a certification from the IASSC. 

    The Green Belt certification is for a mid-managerial role that involves implementation of Six Sigma methodologies. Positions that a Green Belt may undertake include: 

    According to a study revealed by Indeed.com over 12 months from more than 222,000 data points, the range of average annual salary for a Lean Six Sigma Green Belt certified professional is from $71,381 per annum to $101,869 per annum. A Six Sigma Green Belt earns around $72,000 per year on an average. 

    According to PayScale.com the average annual salary for a Green Belt certified individual is approximately between $65,000 per annum and $79,000 per annum. 

    The annual salary also tends to vary based on the Green Belt role. Average salary for a Process Engineer is $73,166, Quality Manager is $79,141, Continuous Improvement Manager is $77,141, and a Quality Assurance or Quality Control Engineer is $66,030 per annum. 

  • Learn the relevant subject matter 

  • Pass a written examination 

  • Complete Quality projects 

  • Lead the project team of Six Sigma improvement 
  • Work under a Six Sigma Black Belt’s supervision. 
  • Analyze quality problems and solve them 
  • Work in coordination with the project’s data collection process team and provide validation to the measurement system 
  • Green Belts operate within the limits of their functional area 
  • Assist in the improvement of team facilitation skills 
  • Develop Project charter along with Supplier, Input, Process, and Output (SIPOC) Diagram for the project 
  • Professional development 

  • Financial advantages 

  • Benefits to the customer 

  • Competitive advantage 

  • Operate in the supervision of or in support of a Six Sigma Black Belt 
  • Solve and analyze quality problems 
  • Be involved in quality improvement projects 
  • Has a work experience of minimum three years 
  • Be capable of demonstrating their understanding of tools and processes of Six Sigma. 
  • 110 for computer 
  • 100 for paper-based 
  • Four and a half hour 
  • Four hours
  • Certification from the International Association for Six Sigma Certification (IASSC)  

  • Certification from the American Society for Quality (ASQ) 

  • Computer-based exam 

  • Paper-based exam 

  • Go to Prometric’s official website. 
  • Click on Schedule Your Exam present under the Test Takers section. 
  • Search for ASQ in the search bar. 
  • Click on Read More. 
  • Under the Actions table, select the Schedule option. 
  • Select your Country. 
  • On the next page, Read Information Review and hit Next. 
  • Agree to the Privacy policy and click on Next. 
  • Enter your Eligibility ID and other details. 
  • Next, select Yellow Belt in the appointments section. 
  • Select a suitable date and time for the exam. 
  • Your exam will be scheduled. 
  • Visit the link
  • Click on ‘Add to cart’. 
  • A pop-up will appear. You will see the options ‘View Cart’ or ‘Checkout’. Select the Checkout option. 
  • Fill in the details.  
  • Complete the payment. Your order will be placed. 
  • Process Engineer 
  • Quality Assurance Manager 
  • Quality Manager 
  • Supplier Quality Engineer 
  • Continuous Improvement Manager. 
  • Research & References of Essential Guide to Six Sigma Green Belt Certification|A&C Accounting And Tax Services
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    Top 10 Six Sigma Tools

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    Top 10 Six Sigma Tools

    Six Sigma is a fact-based, data driven philosophy of quality improvement used to drive business process improvement, customer satisfaction and bottom-line results. 

    The Six Sigma expert uses qualitative and quantitative techniques to drive process improvement. With the right tools, not only will there be an elimination of waste, but also an increase in profits and productivity of employees. Although the tools themselves are not unique, the way they are applied and integrated as part of a system is.  

    In this article, we present the top ten statistical and graphical tools commonly used in improvement projects. 

    Value stream mapping

    This tool, part of the lean six sigma methodology performs outlining of the materials and information neededto take the product to market, all the way to the customer. It is useful to streamline the process of production. While lean manufacturing is the predominant industry where value-stream mapping is used, it finds application across other several other industries as well. 

    The primary focus of this tool is to visualize the information as error rate, time period, and unnecessary delays that occur within the process. The value-stream map has three parts: 

    One of the most common six sigma tools, cause and effect analysis allows for brainstorming the different causes of a problem. The cause-and-effect analysis diagram is also referred to as the fishbone diagram due to its resemblance to the fish’s skeleton.  

    The first step in this is to analyze and identify the problem for which the solution is being sought. Make sure that you note down where and when a process is occurring and who is working on it. In the next step, write down the issue on the left side in a box. Now, make a horizontal line that extends to the other side. From there, you will be making vertical lines that will be extending off giving a view like a spine. On each of these lines, you will be writing the major cause that can be behind this problem and the reason behind that cause. Next, you will be breaking down these causes into sub-causes. Once you are done with the diagram, you will have all the possible causes of your issue. You need to test and analyze this result. 

    Sometimes, organizations mayfind an issue occurring repeatedly and no matter how many times it is fixed, it bounces back after some time. These problems are the result of a deeper underlying issue at the core. This is where a tool such as the Five Whys is useful. 

    Uncover Deep-Rooted Business Problems With Five Whys

    The Five Whys method  

    Created in the 1930s during the Japanese Industrial Revolution, this method involves asking the question ‘why’ five times. It is best suited in dealing with moderately difficult issues. Although it sounds like an unsophisticated method, when used in conjunction with other six sigma tools, the method yields impressive results.  

    Onedoes not necessarily need to ask ‘Why’ five times and may reach the root cause sometimes in one or two Whys itself. However, one may also end up going up to seven to eight Whys. In general, however, five Whys are observed. As a caveat, you are not to proceed further if you find the root cause going beyond your control. In these cases, you must take a step back and work on the root cause. 

    This is a supply chain control system focusing on cost reduction. This is done through the implementation of the inventory control system. Because of its potential benefits and ease of use, Kanban is one of the most famous six sigma tools.   

    Kanban method

    Let us take the example of a supermarket. When the refrigerator at the supermarket is stocked, the store does not stock it up for months or years.Neither does the store keep products that it doesnot expect to sell right away. Similarly, in Kanban, you create a shopping list of products that you need, based on the customer’s demand. Kanban uses this arrangement and makes the firm’s output controlled by its inventory supply.  

    The Kanban system is known for limiting the inventory-holding for all the business processes. This frees up additional resources which can be used better. There is a simple idea behind the Kanban system: activate the supply chain only when there is a demand for it. This brings focus to the business process and improves its efficiency.  

    This is a graphical representation of the Pareto Principle. It states that in any given situation, 20 percent of input will produce 80 percent of output. There are a line graph and a bar graph on the chart. The bar graph is representing the different metrics of the different components of the business processes. The line graph is representing the cumulative total of the metrics.  

     What is a Pareto Chart? Definition and Examples - Tulip

    Example of a Pareto chart 

    With this tool, you will be able to visualize which part of the process will be influencing the output most. For creating such charts, the first step is figuring out all the processes’ components and measuring them. After you have completed this, you put the data into the Pareto chart. Every component has a big influence on the outcome. You will also be able to get an idea of what requires immediate attention. 

    This is a method of visualizing the business process and having a better understanding of how it works. A map will be outlining the standard, roles, and responsibilities that are involved in the process. All the data is presented in a structured way displaying the steps of the process. This also includes what the inputs/outputs are, who is responsible for what, and what is the relevant information for the processes.  

    When it comes to process solving, business process mapping helps a lot. With this, you visualize the complete process which makes it easy to identify the issue, thus allowing you to get directly to the cause. Also, you will be visualizing the roles of the people that are involved in the process. With business process mapping, you will be able to find the potential risks created by the processes. While creating the map, you will have to rethink every step and look for any hidden liabilities. 

    High-Level-Process-Map_BillEureka

    Example of a high-level Process map

    There are several maps from which the most appropriate one can be chosen for your business processes:  

    This is a document defining the scope and purpose of the project. You can use this as a blueprint of the business process and the project’s legal authorization. It contains the project’s scope and overview, details of the resources and the team, and its timeline. With this, you will have all the basic information regarding the project.  

    The best thing about the project charter is that it keeps everything less chaotic. Once the team starts working on the project, it is easy to forget who is responsible for what, what deadlines need to be met first, etc. Things will get messier if the company doesn’t have a better managerial hierarchy.  

    With the project charter, you will be able to focus on what the project is all about. You will get a clear understanding of the structure of the project and what is the relationship among the people involved in it. It will help you bring order to your project and firm. This document is dynamic and undergoes evolution as and when project progresses. 

    RACI Matrix or Responsibility Assignment Matrix is a table describing the responsibilities of every member of the team regarding all the tasks involved in the business process. The full form of RACI is Responsible, Accountable, Consulted, and Informed.  Responsible is referring to who is going to achieve a specific task. Accountable is the one who assigns the tasks to everyone and monitors their progress. There is one accountable for every task. Consulted are a group of experts who will be providing their opinion to those who are working on the task. Informed are the people the team will be notified to after completing the task. 

    RACI Matrix | Continuous Improvement Toolkit

     Example of a RACI matrix  

    The RACI matrix contains tasks and team members on the left side and the top row of the table respectively. The intersecting cells will contain a letter about what a person is handling in the task. This matrix helps all the team members to understand their role in the process. Also, it makes seeing the gaps in the structure of the team easy and what roles must be filled. 

    This is a statistical process used to eliminate and understand the relationship between the different variables. You can use it for defining the relationship between the input and the output variable. With this tool, one can visualize patterns or deviation from the patterns in the workflow. 

    Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other. 

    While working on regression analysis, one needs to be cautious in avoiding statistical illusions. 

    Failure Mode and Effects Analysis (FMEA) is a structured approach to discovering potential failures that may exist within the design of a product or process.Failure modes are the ways in which a process can fail. 

    Effects are the ways that failures can lead to waste, defects, or harmful outcomes for the customer. The Failure mode and effects analysis is designed to identify, prioritize, and limit these failure modes. 

    Lean Six Sigma practitioners often use this method to improve the quality of their services, processes, and products to detect and fix their problems even before they have occurred. 

    In conclusion, of the many tools available to green belts, black belts, and master black belts, these ten tools are the most common. While every project is different and may require a unique application for its own purposes, you will be hard pressed to find a project managed by a six sigma professional that does not use at least four of these ten effective six sigma tools. 

  • Process MapThis contains all the steps included in the business process. 
  • Timeline: It comes from the process map for summarizing all the data that was used during the process. 
  • Information Flow: It explains the interaction that every step has with one another. 
  • A flowchart is a less flexible process map. You can either draw it by your hand or use a software like MS Office. It is used for creating workflow diagrams. 
  • Swimlane diagram is just like generic flowcharts, only it has a better structure. 
  • The value stream map provides an in-depth option. It is hard to analyze it at first glance. 
  • Supplier Inputs Processes Outputs Customer (SIPOC) diagram is a simplified map focusing on the important aspects of the people and the process involved. It strips the extra information.  
  • Research & References of Top 10 Six Sigma Tools|A&C Accounting And Tax Services
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