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Why hire from Coding Ninjas?

“You can have the best strategy and the best building in the world, but if you don’t have the hearts and minds of the people who work with you, none of it comes to life”.–Renee West

The present workforce of a company says a lot about the future the company holds. Nowadays, companies are known to spend a lot on the talent acquisition, and the training of the new recruits, and it makes sense for the companies to invest so much, given the fact that this human resource is going to be the vision holders of their future.

Recruiting fresh talent as interns or as full-time employees has always been the hardest challenge for the recruiters and campus placement requires a lot of time, energy and investments. Most of the companies hunt for talent from campus to campus, picking out the finest ones from the crowd by an initial screening test, followed by a couple of interviews, which is grilling for the interviewee and exhausting for the interviewer- and this same process in colleges throughout the country, OVER AND OVER AGAIN!

And, this is how much a company puts in to acquire new talent on board!
So, why recruit from Coding Ninjas?

The answer is simple- TO SAVE ALL THAT EFFORT!

Coding Ninjas has been mentoring the most excellent lot of students from various colleges for years! The training provided here, at Coding Ninjas is aimed to shape programmers who can think, explore and execute.

They are evaluated at every step, they are guided at every level, and they are taught to challenge themselves at every step. The students here are grilled at every stage to ensure that they fit in well to the industry standards.

So, the company gets to choose from the finest of the fresh talent out there!

Instead of choosing selected colleges to hire from, the company gets to choose the selected ones from a broader set of colleges!

And let’s not forget the cost cutting in the talent acquisition department! 😉

So, this is safe to establish now that Coding Ninjas is a one-stop platform between the recruiters and the talent!

You can explore more about the courses on our website.

Data Science and all that it encompasses

To understand what all does data science encompass, we’ll have to dive a bit deeper from the surface. First of all, let’s keep some of the facts clear that Data Science is not a part of academia nor it is mostly statistics/analytics. The major part of being a data scientist is to extract information from the data, analyze it, and if a problem arises, you build a tool accordingly and solve it. It mainly includes a little programming skill, some statistical readiness, some visualization techniques and of course a major sense of how a business works.

It is a kind of investigation in which the Data Scientist peeks into the data transmitted in the past, analyses it and then comes up with a tool/model to rid of the problem permanently.

So, here are a few things you have to do if you are thinking to enter the world of data, statistics, and information:

Data cleaning: Typically when you start gathering data and extract all the information you can get, there can be a huge amount of data that is not usable at all. It means, even if you keep all of the correct information with you, it creates a lot of mess. This is the part where you have to sort all the information in such a manner that the resulting combination makes sense. Fixing them and making sure that the problem will be fixed automatically in the future is what the Data Scientist usually does.

Data Analysis: This is not the part where you prepare tables or charts to analyze the situation or the problem. This is where visualization takes place. This is the creative part where you get a chance to show your abilities to detect why the particular problem is arising or how can you prepare a model to solve it universally. Since Data Science is one of the most discussed topics nowadays, people are always trying to find newer solutions to business issues. You will be playing a major role in making decisions more scientific and helping business achieve effective operation.

Modeling: After you have extracted and analyzed all the data, now is the time to evaluate and tweak models. However, there are a lot of tools introduced in the market to solve regular issues faced by business organizations. Still, there is a huge possibility of a problem to arise that only a Data Scientist can foresee. We can say that this step is one of the most complicated one until now but here is the point where Data Scientists can go back to all the data they have put together and work on new features to create an out of the box model. Besides having powerful algorithms to solve any issue, there are times when nothing works. Only a Data Scientist can keep working on it to find out new solutions.

Working with Data Science

Working with Data Science might sound really interesting, but there are certain qualities that you should possess to work in this industry. You should have proper knowledge of statistics and mathematics including various databases and the use of different tools accordingly. A proper skill set of data munging, data cleansing and data transformation is needed. Finally, visualization comes through which you definitely need to have to be a good Data Scientist.

Below is the list of a few Data Science tools you must know from the very beginning:

R Programming: R is a statistical programming language that comes with an array of features specific to data scientists. It is one of the strongest and most prominent languages when it comes to data analytics and machine learning.

SQL: SQL, short for structured query language, is a structured programming for working with relational database management systems. SQL follows a certain format of rows and columns that depict a huge amount of data. While many of the operations are shifting to the NoSQL databases, SQL still manages to be one of the widely used tools for data manipulation and interpretation. SQL is extensively used by database administrators and developers alike.

Python: Python is an object-oriented programming language that is extremely high-level and versatile. Its use cases include a variety of applications, especially in the domain of machine learning and data science. Python comes with a huge set of readymade libraries which makes it a promising choice for data scientists.

Hadoop: Hadoop is used to process huge amounts of data and is one of the most powerful tools for that. Being open-sourced, Hadoop has an extremely vast and active community of developers. It helps you store, computer, deploy real-time analytics among things on big data through its ecosystem of tools.

SAS: SAS is one of the most powerful business analytics and intelligence tools. It is a software suite useful for extracting, analyzing, and reporting data to derive valuable business insights from it. SAS includes a whole set of tools required for working across different steps in converting raw data into actionable insights.

Tableau: Tableau is by far the most powerful data visualization tool. With analytical and reporting capabilities, Tableau is for you even if you don’t have a lot of technical knowledge.

Those were the basics of everything that comes under the umbrella of Data Science — including most of the important tools used in the industry. Also, did you know that Data Science is branded as the sexiest job of the 21st century? Yup.

So, if you feel data science is your calling and all you’re lacking is good supervision, let’s tell you — we’ve got you covered. Come over to Coding Ninjas where our online course on Data Science takes care of everything you need in order to start on the correct track!

Node.js vs. Python for backend development

It is time; at last, to discuss the fact about the two most deliberated and used backend programming languages — Node.js and Python. As they both play a crucial role in the same area of development sometimes it becomes confusing for the programmers to choose with which language to start with. Well, we are here to solve that one for you. Let’s start with understanding the pattern both languages use to make things easy for the client as well as the server.

What is Node.js?

Node.js is an open source runtime environment to write server side applications that is based on JavaScript and runs on the V8 engine. It uses a non-blocking, event-driven I/O model. It allows programmers to create real-time applications and was designed with push-based architecture in mind. Node.js is mostly preferred because of the fact that the language works on JavaScript- an already extensive tool but also the efficiency that allows you to build highly scalable web applications. It can be run on OS X, Microsoft Windows, and Linux operating systems.

Pros of Node.js

· It ensures the use of the same language both on the client and server side.

· Faster web page loading time

· Uses a single threaded model with event looping. This type of event mechanism benefits the server to respond in a non-blocking way.

· There is no buffering in Node,js as applications output the data in pieces.

· Easy to monitor.

· The Node.js developer’s community is a very active and huge group of developers who are constantly contributing to develop the particular language.

We are grateful to the JavaScript developer’s community for their input because we have the access to a ton of ready solutions/codes.

Cons of Node.js

· Debugging

· The fact that it is very similar to JavaScript, so the developers who are already working with JavaScript finds it a bit of a overwork.

· Node.js’ API changes frequently which makes it unstable. Sometimes programmers have to change the existing code to make it friendly with the updated version of Node.js.

· It still doesn’t support multi-threaded programming yet. Heavy computations might lead to decrease the level of performance.

What is Python?

Python is an object oriented, high level programming language with active semantics. The interesting fact about Python is, it works as a glue language to connect existing components together. It supports modules and packages, which encourages program modularity and code reuse.

It supports multiple programming patterns, including object-oriented programming, functional programming, or procedural styles. Unlike Node.js, Python cannot translate to computer-readable code before its runtime.

Pros of Python

· Python gets the job done in fewer lines of codes than other similar programming languages.

· It can give the same output as PHP but much faster.

· Developers have access to high functionalities and extensive library support.

· Python offers a very easy maintenance as any kind of errors can be resolved within minutes.

· As it has been around for a really long time, programmers have the access to high functionalities and extensive library support.

Cons of python

  • Since python is interpreted, it often results in slow performance. This problem can be avoided if high speed is not the actual requirement of the project.
  • Python has design restrictions. Although this is easy for programmers while coding, it still can raise runtime errors.
  • Python database access layers are underdeveloped as compared to mostly used Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC). Accordingly it is less used in huge enterprises.
  • This particular language can take some time to catch up if some of the developers on your team aren’t familiar with it.

Here are a few points for you to actually understand the difference between the two languages for backend development:

· Right at this moment Node.js is the ideal language to perform real time web applications and on the other hand Python is not suitable for real-time environment.

· Node.js is best suited for smaller projects with a lesser memory while Python is one of the best options for developing larger projects.

· Node.js uses JavaScript as its base language and Python uses PyPy as interpreter.

· For asynchronous programming, Node.js is preferred as Python is not exactly the best option for it.

· As Node.js is pure JavaScript, its basic remain simple for the programming team. Here Python takes over Node.js as the developers need to write lesser lines of code and can get the exact result.

· Also Node.js lacks the clean coding standards while Python is subtle in its way and can do the same thing PHP code can.

· Node.js supports callback while Python doesn’t which makes it way simpler.

This should help you make a well informed choice. And now that you’re on track with what both of these languages entail, let’s present to you the Python and Node.JS courses offered at Coding Ninjas. These courses start from ground zero and ensure that you’re on the correct path for the rest of your career!

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A place which can make you Ninja in just a few months!

 

His amazing experience and what he loved!

It was an outstanding experience with Coding Ninjas. Wherever I am today, it is because of Coding Ninjas. I was nothing before I joined Coding Ninjas. When I completed the interview preparation course, I got the confidence to crack any interviews. Your courses are just amazing; it boosts coding skills to another level.

Interview experience

It consisted of 2 rounds. If you are going to interview in a company on the base of coding, then data structures must be strong. Companies like Paytm, Oyo, Chegg, like that kind of company also focused on trees, graph and dynamic programming. You should also have Knowledge about OOPS.

Advice to Current Students!

Your data structures must be strong. OOPS is also essential and be good with any programming languages if possible be good with Java.

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An insight into the role of a Data Scientist

Data science is the hottest selling controversy in the IT industry right now. This has been one of the most necessary skills, and the sexiest job of the 21st century.

The question to follow is: why?

Given the demand-driven nature of this industry, you need to keep evolving with it. Every day, so much data is being produced, and this data can be used to change the structure of every industry in this world, and this is where they need a data scientist, which thus, makes this the sexiest job of the 21st century.

But, what does a data scientist really do?

With the introduction of today’s world to big data, the focus shifted to its storage and the processing of this data. While the tools like Apache Hadoop, MicrosoftHD insight, etc. solved the problem of storage of the data, the focus shifted to processing and working with this data. And that is what a data scientist has to do- analyse the data and interpret it to produce meaningful insights with it.

Sounds really simple, doesn’t it?

WELL, not so much!

The whole process of collecting the data, cleaning it, applying algorithms for mining of this data, analysis of this data and it’s interpretation, to develop an insight which actually answers the problem- is precisely what data scientists have to do!

Good command over programming and a high data intuition is the primary required weapon. While being acquainted with Hadoop or hive is not a necessary skill, a data scientist, in all probability, will end up acquainted with this skill set.

To put it simply:
“A Data Scientist is better at statistics than any software engineer and better at software engineering than any statistician.” ― Josh Wills, Director of Data Engineering at Slack

IMAGINE!

They have to code their way to fetch and visualise the data, and once that is done- they have to do ALL THAT MATH!

To break this whole process into categories, we have:

Data collection: Data collection is the process of gathering and measuring data, information or any variables of interest in a standardised and established manner that enables the collector to answer or test hypothesis and evaluate outcomes of the particular collection

Data Visualization: To communicate information clearly and efficiently, data visualisation uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Useful visualisation helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. This uses tools like Tableau, plotly, RAW etc.

Data Analysis: Data analytics focuses on processing and performing statistical analysis of existing data sets. Analysts concentrate on creating methods to capture, process, and organise data to uncover actionable insights for current problems, and establishing the best way to present this data.

And if this doesn’t explain why data scientists have been labelled as the Unicorns of the IT industry, probably nothing will!


So, that is all you need to know about data science as a career. To put all of this into perspective, Coding ninjas has curated a course which covers the A to Z of data science, starting from the fundamentals, covering up all the concepts involved in visualisation, gathering and analysis of the data.

For further details on this course, you can check out the course curriculum here.

A day in the life of a data scientist

 

Data science is a fantastic mix of art and skill. The skill here we are referring to is digging (not literally) — it’s pure exploration. For a business organization that knows how to analyse the data and work on the problems, an explosive growth is on the way. That’s why there is an ever-increasing demand for data scientists these days. Glassdoor names data scientist as the number one job in the U.S., with a job score of 4.8 out of 5. Also, Harvard business review has declared data science as one of the sexiest job of the 21st century.

To begin your career in data science, you need to be familiar with statistics, data science, Big Data, R programming, Python, and SAS. Hands-on experience with these tools/technologies will set you on the right track.

Talking about a typical day in the life of a data scientist, it won’t be like everyone else, spent doing the same job every day. If you are someone who is flexible and likes daily exploring, then this is the job for you. Every information that travels in the company is valuable and is considered as data with which a data scientist can foresee the future problems and should have the ability to solve it before it even arises. For data scientists, it’s crucial to see the bigger picture from the company’s point of view and build a strategy to leverage the data available for the benefit of the organization.

The whole day of a data scientist revolves around data, which is obviously mentioned in the title. Working with what you already have and bringing out your creative side to solve an unknown issue can be highly rewarding. Data sets are often puzzling and can be mysterious enough to surprise you but finding the actual meaning and getting answers is another one of the valuable returns you can get. It’s about finding out the solution to a problem or just finding something useful which can later benefit the company.

Going with the trends:

Other than spending time with pulling, merging and analysing data and developing or testing new algorithms, a major part of the day of a data scientists includes reading industry related blogs, getting together concepts, building data visualizations, writing different conclusion to share, attending seminars/conferences and basically keep up with the trends. New information comes out every day as data science is one of the most researched fields today. And if it is useful you definitely would want to know.

Here’s what an actual day of a data scientists look like:

Almost 40% of the time is spent on research and development. It also includes checking the social media blogs/vlogs on data science. After knowing all the revised and updated trends, now is the time to focus on developing and testing new algorithms with proofs to solve actual statistical problems. Generally, if a new problem related to data has been solved, it is shared or published in webinars and conferences.

A large chunk of the day will be spent building relationships within the company in order to be aware of all the new projects to come your way. This can lead you to discovering newer problems and being ready with predictive models of the solution. This might just be the most important part of the day as it opens a whole new door to communicate with all the employees and come to the conclusion sooner. As a data scientist, proper communication with a wide range of stakeholders is a very important aspect to simplify the explanations of algorithms to a layman level.

Each project is unique so you can try the project’s initial discoveries to lead you to the next step. Projects can usually be simplified using tools from topology, real analysis and graph theory, which really helps you to speed up rather than coding from scratch. If you don’t have a big team with you, this hack can help you level up and cover more projects than usual.

At last you can always help your team to develop or improve new models by testing the previous ones. Identify the false positives/negatives and emerge new examples to fix the problem. For some cases you should know when to exactly stop as it totally depends on the project for how long you should work on it.

In conclusion…

If the job role we discuss above excites you and makes you want to build a career in the same — bravo! The demand for data scientists is only increasing, like we said earlier, and there couldn’t have been a better time than now to learn and explore. If you wish to start, we recommend you check out our data science course.

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Node.js projects for beginners

If you’re just starting out with Web Development, you’d have found yourself confused between the backend language to choose. With so many options, each having their own set of pros and cons, it often becomes a challenging task to pick the one perfect language.

But here’s a catch — no language is perfect, and each have their set of use cases.

Having said that, there’s one particular language that’s gained a lot of fandom in the past few years. That’s especially because the language works on JavaScript — an already extensive tool for building website and web apps. Yes, we’re talking about Node.JS.

Node.JS is an open source runtime environment to write server side applications. You can use it for traditional websites and back-end services. Also, it is efficient and lightweight, and allows you to build highly scalable web applications. It is used for traditional web sites and back-end API services; but was designed with real-time, push-based architecture in mind.

If Node.JS is the language you’ve decided to go ahead with (congratulations!), here are some projects you can work on to get a stronger grip on the language:

1. To-Do list:

Creating a to-do list is a much easier way to understand the basic concepts of a programming language. Create an empty page where the user can record all the task they have to complete during the day. Store the new and completed tasks in a different array. For this application put in very minimal CSS styles with a neat appearance.

To get your application running use express framework. Express is one of the minimalist frameworks which will be very easy to work with a server like node.

2. Portfolio app:

To create a portfolio application using Node.JS, you can first concentrate on the outlook of the application which means how it looks and how are the sample projects working out. It can reveal the sense of style you particularly have. There are multiple elements you can use to give a good experience to your user like presenting the application and the output with a good appearance.

The next thing you have to work on is the architecture of the whole project. It includes the code you are writing to make the application lightweight and easy to use. You have to first define separate routes for each project. Node.js has different set of controllers for each route to manage the views. If you have the same code for the header and footer then you don’t have to repeat them which come as a benefit for the programmers.

3. Chat app:

Building a chat application gives you a very good idea of working with real-time systems. At first, you have to separate the application into two parts i.e., the client part and the server part. With the help of web socket you and the client can send data to each other directly at any time. It works like a virtual handshake. The process starts with the client sending a regular HTTP request to the server. This particular application is very easy to code with the knowledge of web sockets and socket.io.

The following things can help you to enhance your application:

· Keeping a record of all conversations.

· Online/offline labels.

· Take references from the features of whatsapp

· A registration system for one on one chats

4. Web security:

This is one of the most interesting project you can get your hands on if you are about to start working with Node.JS. You can create a spoof login page like Facebook’s to know the passwords of your family and friends. This can be executed if you are able to host it on your LAN. Things get even more interesting when you can host it on the web. Then you can peak into almost anyone’s password.

There is a generous amount of population who usually ignore the links and get caught in these codes.

5. Gaming:

People who are into gaming or at least fancy the idea of how the game works, node.js is one of the languages you can work with. For beginners, the starting point is to code on Node.JS by using web sockets to provide a real time conversation between the clients and the server. If you are an amateur, start with making an applet which collects statistics from multiple clients and put it on a single platform. For larger applications, you can put more effort into CSS stylesheet and have more interface elements. Try to keep all the logic to the server side so the client only has to give input to render the information from the server’s end.

So, that’s pretty much it! Get going with these projects and find yourself in a much better place in terms of coding in Node.JS. If you feel stuck anytime — we’re there! Just hop in to Coding Ninjas where our full stack development course takes care of everything you need to know about Node.JS to start well and go big.