Competitive Programming And The Edge It Offers


Competitive programming is a sport in which aspiring programmers compete with participants from around the world or from a local area to try to solve complex algorithmic problems according to a specified set of rules. It has gained a lot of attention over the years, and today, competitive programming is hosted and supported by the magnates of the IT industry such as Apple, Google, and Facebook.

There are many benefits of competitive programming. Developers across the globe unanimously agree that competitive programming helps you become a better developer and provides you with an edge over your competitors. Let’s look at the benefits of competitive programming.

  1. Helps You Stay Focused To Find Solutions Faster

In a competition, you have to compete against several participants, and hence, there remains a significant amount of pressure on you. As you actively take part in such contests, you gradually tend to improve your performance under stressful conditions by learning how to stay focused and concentrated on your tasks. You become more disciplined, and ultimately you can find faster and accurate solutions.

  1. Developing Bug-Free Codes

As you participate in competitive programming contests, over time, you become familiar with specific bugs that hinder the efficacy of your codes. For instance, issues like array indices growing out of bounds, floating point precision, and so on. The more you solve, the better you become at overcoming these obstacles. Since you are faced with complex algorithmic problems in these competitions, you learn how to optimize your code for the best outcomes.

  1. Prepares You For A Career In Coding

Participating in coding contests introduces you to a whole new world of programming, complex problems, and puts your programming skills to test. These competitions are an excellent way of upskilling. When you participate in contests, you get to know your key strengths and weaknesses. So, you get a chance to work on your weaknesses and come back with confidence the next time. Eventually, you’ll realize that you’ve already mastered the things that are essential for building a successful career in coding.

  1. Inculcates The Virtue Of Teamwork

In competitive programming, you are required to collaborate with your team members to solve the tasks handed to you. When working as a team, you learn how to open up to the ideas of your teammates, you learn new approaches to solve problems, and most importantly, you learn how to function as one unit by compensating for one another’s weaknesses. As a team, you learn to divide the responsibilities among yourselves and help find the most efficient way to complete your task. Companies today highly value the virtue of team spirit. Thus, if you know how to function efficiently within a team, chances are that you’ll land a good job at a reputed company.

  1. You Become A Desirable Candidate

Our world is increasingly becoming a technology-driven world where the demand for skilled programmers and software developers is ever on the rise. Competitive programming is the perfect hunting ground for large software MNCs to find talented and versatile coders. As we’ve mentioned before, companies like IBM, Apple, Facebook, and Google actively follow these contests. So, participating in them will bring you under their radar. And if you emerge as a winner, your chances of being hired by a good company increases manifold.

Other than the benefits discussed above, competitive programming also gives you an edge over your competitors in any coding interview you can think of. Being quick to come up with the most efficient solution is something that will set you apart from your peers and it can be mastered through thorough competitive programming!

So, how about giving competitive programming a try? If you find yourself struggling, hop right in at Coding Ninjas where our specialized online course on Competitive Programming will get you up and running!

8 reasons why Ruby should be your first language!


Every aspiring programmer is faced with one fundamental question – “Which programming language should I begin with?”

While Java – one of the most widely used programming languages in the world – is backed by a massive developer community, Ruby on Rails (RoR) is rapidly gaining traction among the developer community. But first, don’t harbor the misconception that Ruby on Rails is the same as Ruby. RoR is a software development framework specially designed to support Ruby. Essentially, RoR is an assortment of unique shortcuts written in Ruby that allow developers to build web applications much more quickly and efficiently. Websites such as Basecamp, Airbnb, Twitter, Scribd, Groupon, Kickstarter, Soundcloud, Shopify, Crunchbase, and Slideshare are all built on Ruby!

Here are ten reasons why Ruby on Rails should be your preferred choice of programming language.

  1. Open Source

RoR is an open source framework that offers perks like free licensing and free downloading of source codes. Furthermore, RoR is compatible with all operating systems and browser.

And if that’s not enough for you, RoR is backed by an enormous and active developer community. From conferences and meetups to online forums like Stack Overflow, you are sure to find RoR developers at every major tech event and hackathons you visit. So, if you are ever stuck anywhere, you could always reach out to the RoR community.

  1. Beginner-Friendly

The fact that Ruby has a pretty simple syntax makes it the ideal programming language for beginners. Not only is it very logical, it also comes with an excellent readability quotient. It enables you to write codes without adding separate comments or help texts within your code. Thus, it is undoubtedly one of the most beginner-friendly programming languages one can ever come across.

  1. Enhances Productivity

RoR is a self-documenting software framework, and hence, developers belonging from any programming platform can choose a RoR project and easily understand its content. Also, the code readability of RoR is excellent. It allows web developers to concentrate on specific issues rather than altering the framework, thereby promoting speedy development solutions.

Overall, RoR increases the productivity of developers, allowing them to launch web applications way more conveniently within a short span of time.

  1. Flexible With Full-Stack Tools

RoR is well-integrated with tools that allow developers to modify codes of existing web apps. Whether you want to add new features or make alterations in data models, RoR makes everything easy. Furthermore, RoR framework facilitates both front and back-end development. Thus, with RoR you can design a complete web application without having to delegate the front-end or back-end development task to anyone else.

Additional bonus – when you learn to code with RoR, you are introduced to HTML/CSS, Javascript, and of course Ruby.

  1. Fast

Apart from being a beginner-friendly platform, RoR is comparatively faster than other development frameworks because:

It allows web developers to reuse existing code thereby eliminating the need to developing a code from scratch.

RoR strictly follows the motto of ‘Convention over Configuration.’ It offers a plethora of resourceful libraries having pre-designed templates that allow developers to add any functionality within their codes. This way it saves a significant amount of time of developers that they would end up spending on configuring setup files.

Ruby is an open source OOP language that allows developers to utilize the vast library of source code templates of the RoR community framework.

  1. Cost Efficient

As mentioned before, RoR is an open source framework with no license charges. It consists of all automation tools that can help eliminate a lot of tasks associated with development projects. Thus, it a very cost-efficient and powerful tool not only for web developers but also for SMB projects.

  1. Facilitates Good Development

RoR is founded on the principles of proper web development. It encourages responsible web designing and development practices, that is the three ‘C’s – Compact, Clean, and Comprehensive coding practices. Moreover, RoR fosters Representational State Transfer (REST). Hence, developers can use RoR to organize their web applications based on standard resources and HTTP texts.

  1. It Scales Seamlessly

Given that the ultimate aim of every website is to gain visitors, RoR framework is perfect for accomplishing this. Since REST architecture is deeply ingrained in the Rails framework, scalability is not an issue with RoR. REST ensures that your website connects seamlessly with your potential audience. Dispelling all misconceptions about scalability on RoR framework, Tobias Lutke, CEO of Shopify states:

“It’s crazy that people are suggesting Shopify has been successful despite rails. Shopify has been successful because of rails!”

Come and aboard the Ruby train with us! We offer comprehensive programmes around Web Development using Ruby on Rails. Our courses are designed to start from scratch and take you all the way to the top. Still confused?! Check out our courses now!

A beginner’s Guide to Analysis of Algorithms


What is an algorithm?

An algorithm is defined as the series of steps or the set of rules followed in order to solve a particular problem and execute the operations related to it.  In the context of computers, these algorithms are also called as “pseudo-codes”, meaning they are not actually codes but are a shadow of codes. That is, they tell us how to form a code to solve a problem for which the algorithm has been written!

Why analyze the algorithms?

Analyzing algorithms is very basic and very important step in the field of computer science. Any algorithm can be evaluated by looking at how much time it takes to execute or how much space it consumes to get executed properly. Another term used to define the performance of an algorithm is complexity. This complexity is of two types:

  • Time Complexity
  • Space Complexity

Time Complexity is defined as how much time an algorithm takes in getting executed for n number of inputs. That is, by keeping input size same, we can compare how the performance of two algorithms by looking at their time complexities. Same fundamental is followed for space complexity, the only change is that space or memory takes the place of time.  

To understand it better, let us take the example of search algorithms. There are two types of searches we all are generally aware of: One is the linear search and other one is the binary search.

The order of growth for linear search is linear, i.e., as n increases, the time taken to execute increases proportionally. For binary search, this order is logarithmic. Let the time complexity be represented by Θ(n)

For linear search: Θ(n)=an or Θ(n) ⍺ n

For binary search: Θ(n)=alog(n) or Θ(n) ⍺ log⁡(n)

For small number of inputs, linear search may take less time but as the input size increases, binary search starts taking less time as compared to linear search. So, this is the magic that analysis of algorithms has! It makes us capable of coming out with better approaches day-by-day.

Three Cases to analyze an algorithm

To analyze an algorithm, we make use of the following three cases:

  • Worst Case: Every algorithm usually has an upper bound on how much time it takes to get executed. In worst case analysis of time-complexity, we make use of this upper bound to describe its performance. Therefore, we must be having an idea of what is that series of steps in which the algorithm can get stuck which can make it take the longest duration to get fully executed.  Thus, we can say that the worst-case complexity for linear search algorithm is Θ(n).
  • Average Case: In case of average case, as the title suggests, we take the average of the computation time of all the inputs. We take different set of inputs, find the computation time the algorithm takes for them, sum them over and then find their average. The formula for which is given below:

Average Case Time= i=1n+1θ(i)n+1=θ((n+1)*(n+2)2)n+1=Θ(n)

  • Best Case: Every algorithm also has the lower bound on the time it takes to complete its execution. It is when the algorithm takes minimum number of steps in getting executed. This is defined as the best case complexity of the algorithm. For example, the best case complexity for linear regression is Θ(1).

Asymptotic notations for representing time complexity

There are three asymptotic notations for representing time complexity. They are:

  • Θ notation: This notation defines the upper bound as well as the lower bound of the algorithm.

Θ(g(n)) = {f(n): where c1, c2 and n0 are positive constants such that 0 <= c1*g(n) <= f(n) <= c2*g(n) for all n >= n0}

  • Big O notation: This notation defines only the upper bound of the algorithm. It’s representation is given below:

O(g(n)) = {f(n): where c and n0 are positive constants such that 0 <= f(n) <= cg(n) for all n >= n0}

  • ῼ notation: This notation defines only the lower bound of the algorithm. It’s representation is given below:

ῼ(g(n)) = {f(n): c and n0 are positive constants such that 0 <= cg(n) <= f(n) for all n >= n0}.

Analysis of algorithms goes a long way in making you a better programmer and a successful developer. At the end of the day, programming is not about the language you choose, but your comfort with writing neat and optimized algorithms, and improving them whenever the time comes. If you wish to dig deeper into the magic of algorithms, drop by at Coding Ninjas and check the array of courses we offer. Our teaching methodology focuses on helping the student develop a logical mindset – the one needed for successfully analyzing the trickiest of the algorithms. Come on, in!

Here’s why programming is more about the logic/algorithms and less about the language.


Let us begin with the story of Raj.

Raj had just passed his high school exams with flying colours, and had taken admission in Computer Science in one of the most prestigious institutions in the country. Now, Raj hadn’t done much of coding in his previous years, yet he still took up CS. Was this a bad decision? By no means. What Raj lacked was the knowledge of syntax and rules of programming languages. What he had in abundance, however, was the ability to think, to analyse problems, and to craft creative solutions to these problems. This led to him being able to understand more, and in turn, score more than his peers who were supposedly better programmers than he was.

Programming is more or less based on this very same concept.

“One must not only learn how to code. One must learn how to think and analyse.”

Freshers in the field of programming almost certainly believe that once they master the rules and syntax elements of a particular programming language, they would become good programmers. This is true to an extent, as it would definitely make them good coders, but it will not make them good problem solvers.

The Sudoku Puzzle.

Taking the example of a Sudoku puzzle, you know how and where to write numbers in boxes. But how would you solve the puzzle if you do not know the underlying logic behind the game? How would you complete a computer game successfully if you know just the control bindings, but not the hows and whys of the tasks to be performed?

Problem Solving “using” computers.

Computer Science and programming is all about solving our day to day problems with the help of computers. If you focus a little more on the latter part of the previous line, you would certainly realise the essence of this article- “with the help of computers”, the key words being with the help of. Computers and programming languages are just tools to aid us in applying our thinking and logic towards solving particular problems. Coding is not a method to achieve a particular task, it is simply a tool to make it quicker.

Learning new ways to solve problems, studying new algorithms, applying various forms of logic, and developing an innate way of thinking is what programming is all about. It is not about the language, the rules, and certainly not about putting a semicolon at the end of each line of code. Logic matters more than the programming language that you choose.

Tweaking with the languages.

The logic behind the method of finding the largest number in a given set of numbers will always remain the same, irrespective of whether you code it in C++ or in Python. If you are clear with your core programming concepts, you can easily migrate from one language to another. All that will take is memorizing the syntax of the new language, and voila! You will now be able to write code in both R and Python!

Remember- the underlying logic behind performing a task is of far greater importance than performing the task itself. If you don’t know how to do a task, how in the world of all that’s holy will you finish that task?

Learn to think. Coding comes naturally to good thinkers. Bon voyage on your journey through the programming realm!

Five tips for front-end web development


Don’t you just love exploring beautiful and neat sites with a clean user interface? While most of us would reply with an assertive ‘YES,’ little, do we know the kind of effort and skill that goes into making a website attractive and user-friendly. The secret to creating an impressive site is to master the art of front-end development, and no, it is not as easy as it seems!

However, it is not impossible either.  Here are five tips that’ll help you get better in front-end design and web development.

  1. Automate!

As a front-end developer, you already have to take care of minute little details that can get overwhelming at times. You have to invest your time on things like Boilerplate, testing, workflow, dependency management, performance, optimization, build, deployment, and so on. Doesn’t sound easy, right?

Take some steam off yourself and incorporate automation into your workflow. While automation can take care of things such as optimization, testing, etc., you can focus on the core areas of front-end development such as HTML, CSS, creating the client-side software, enhancing the user-experience, and so on. By doing so, not only will your productivity increase, but you will also learn to use your time to focus on the areas that’ll improve the overall functionality of your site.  Grunt, Gulp, and Broccoli are some very efficient automation tools.

  1. Refactor Your Code From Time To Time.

By “refactoring” your code, you’re only enhancing the code without tampering with its functionality. This will improve the quality and readability quotient of your code and the more often you do it, your code will continually be updated into a cleaner and fresher version of what it was before. Apart from that, one of the most significant advantages that refactoring offers is that it ensures your code remains free from plagiarism.

  1. Learning Command Line Is The Way To Go.

It is a standard convention among developers to use GUI tools to power the terminal. What most of them don’t realize is that by doing so, they end up spending a significant portion of their valuable time in handling GUI tools than making progress on the terminal. The right way to go about it is learning the command line. Start with the basics of the command line and advance to higher levels as you go, and you’ll find that you can complete several tasks with much more ease and efficiency with command line tools than with GUI tools. Moreover, automating the terminal with appropriate commands is a great way to save time and energy.

  1. Invest In Productive Tools.

The Internet is teeming with a host of web development tools, from browser add-ons to smart plugins, the amount of choices available now is massive! So, why not invest in some really productive web tools that’ll help you improve your front-end designing skills? Tools like Sublime Text, jQuery, Emmet, GitHub, Bootstrap, and Sass are nothing short of a godsend for web developers.

  1. Always Be Curious.

A front-end developer has to keep himself/herself updated continuously with the latest news and innovations in the field. You need to take a proactive stand and learn new things about front-end development from informative blogs and videos. CSS Weekly, HTML5 Weekly, JavaScript Weekly, Web Design Weekly, Codrops, and ShopTalk Podcast are some of the most informative and useful learning sources for front-end developers. Also, make it a point to attend conferences and webinars. These meet-ups provide excellent opportunities to expand your network and get acquainted with talented people.
While these tips will surely help you become a better front-end designer, in the long run, you must always remember two things while designing your platform – keep it simple and neat, and don’t forget to create your signature style. And for all you peeps interested in making it big in front-end development, Coding Ninjas has the perfect course for you! To know more, drop by at our website.

Getting Started With ML Using Python


With Big Data, Machine Learning (ML), and Artificial Intelligence (AI) rapidly becoming the order of the day, an increasing number of people are diving into these trending fields. Today, we’re going to focus on ML and show you how you can step into the world of machine learning using one of the most powerful programming languages in the world right now – Python.

If you are a beginner, this guide to using Python for ML is just what you need.

Let’s get started without further ado!

  1. Developing Basic Knowledge of Python

This is a no-brainer. To start off with ML using Python, one must have some ground knowledge about the programming language. You can begin by installing Anaconda, an industrial-strength Python implementation for Linux, Windows, and OSX, replete with all the necessary tools required for ML.

Get your hands on useful study material on the Internet. Here are some excellent picks:

  1. Acquire Foundational Machine Learning Skills

No, you do not need an extensive and in-depth knowledge of ML to be able to practice it. However, you must have basic knowledge about machine learning to get started in the field. Having a strong background in Mathematics and programming skills will come in very handy here. So, brushing up on your statistical and programming skills (in C, C++, Java, Python) is highly recommended.

Also, you need to be familiar with popular ML algorithms like linear and logistic regression, neural networks, decision trees, random forest, and clustering, to name a few. Try to get accustomed to trending ML frameworks like TensorFlow and Azure.

  1. Scientific Python Packages

Not many are aware of the fact that there exist open source Python libraries that can be efficiently put to use for practical machine learning applications. These libraries are known as scientific Python libraries, primarily used for performing basic ML tasks. Below are the most popular Python libraries:

  • Scikit-learn – Includes all the tools used for ML and data mining. It is considered to be the de facto standard library for ML in Python.
  • Matplotlib – It is a 2D plotting library that can be used in Python scripts and iPython shells, to create publication quality figures.
  • NumPy – It is the most suitable package for scientific computing using Python. It can also be used as a multi-dimensional container of generic data.
  • Pandas – This is great for accessing high-performance, handy data structures and data analysis tools for Python.
  1. Explore ML Topics With Python

After you’ve thoroughly explored the Python libraries, it’s time to move on to learning the useful machine learning algorithms. You can start with Jake VanderPlas’ K-means Clustering and then move onto Decision Trees (The Grimm Scientist). Linear Regression by Jake VanderPlas is also great for getting acquainted with ML linear regression algorithms.

  1. Deep Learning With Python

Deep learning techniques and deep neural networks are increasingly becoming the buzzwords in the industry. If you are a stranger to deep learning, start off with Michael Nielsen’s book, Neural Networks, and Deep Learning.

Python has two very resourceful deep learning libraries – Theano and Caffe. While Theano efficiently allows you to function with mathematical expressions involving multi-dimensional arrays, all the while allowing you to define, optimize, and evaluate them, Caffe’s deep learning infrastructure focuses on speed, modularity, and expression.

Python is a versatile programming language extensively used for scientific computing and machine learning. It is indeed an excellent choice for Machine Learning because of three primary reasons – first, it is a simple language; second, it is backed by a strong community, and third, it has impressive stack of useful libraries. And with so many tutorials, informative content, and online study materials, now is the best time to get started in ML with Python.  Also, if you need expert guidance, you can always drop by at Coding Ninjas, where our courses on Machine Learning help you understand the nitty-grittys of ML using Python.

Approaching Data Structures And Algorithms To Rock Your Next Coding Interview


When it comes to coding interviews, most people tend to focus on one thing – programming – while forsaking all others. They dedicate a lot of their time and effort in acing the art of programming, but surprisingly enough, not many can make it through the end of the tunnel.

No, we do not discourage practicing! We’re only asking you to focus on the basics first – data structures and algorithms. Without an in-depth knowledge of these two core concepts of Computer Science, you’re not going to make any real progress. So, let’s get started on how to approach data structures and algorithms to ace your next coding interview!

Data Structures And Algorithms

The first thing that you’ve got to remember while approaching data structures and algorithms is that you DON’T need to learn each data structure and its sub-structures by heart. If you can, great for you! But such an extensive knowledge is not required in practical applications. For instance, there are high chances that in you will never have to implement a red-black tree node removal algorithm ever in your career, but you MUST be able to identify when you can use a binary tree to solve a particular issue.

Instead of focusing on everything at once, take baby steps and focus on learning the core data structures and algorithms such as hash tables, BackTracking, brute force, linked lists, array and strings, and binary search trees. And while you’re at it, strive to learn two things:

  1. Visualizing data structures

The mark of a good coder is the ability to visualize data structures, to intuitively picture what a data structure looks like, how can it be implemented, and the patterns in which it is stored in the abstract as well as in the memory of your computer. The best way to start is by drawing it and materializing your vision on paper. If you can master this, it will help you solve both simple queues and stacks and complex self-balancing trees.

  1. Learn to incorporate data structures and algorithms in your codes

Although it is true that you won’t be able to master data structures fully until you are getting hands-on experience with practical issues, you have to keep practicing. You have to understand the intricacies of algorithms and data structures and then only you will learn when to use a hash, when to implement a tree, and when to use min-heap.

How To Approach Data Structures And Algorithms?

As we’ve mentioned earlier, the best way to study data structures and algorithms is to implement them in your codes. Even if you don’t have time to code up every single bit, at least try to do the tricky ones. When you get comfortable with the crafty bits of algorithms and data structures, you can easily modify them in your interview according to the problem you’re asked to solve.

To get started off we suggest you go through these books:

Introduction to Algorithms

Data Structures and Algorithms in Java

Data Structures and Algorithms in C++

Algorithms and Data Structures: The Basic Toolbox

If you’d rather learn from tutorials, we have listed that out for you too:

The Coding Interview Bootcamp: Algorithms + Data Structures

Introduction to Data Structures & Algorithms in Java

Data Structures and Algorithms: Deep Dive Using Java

If you try to learn by focusing on at least two or three of these books and online tutorials, by the time you are done with them, you’ll be ready to face your interview. And if you’re interested in sharpening your coding skills with data structures and algorithms, you can opt for Coding Ninja’s Interview Preparation Course – Triumph.

We’re always here to help!

Improve Your Coding Skills This Semester Break


Coding is the secret sauce behind the marvels of technology. Every software behind the major technological innovations such as smart devices, IoT, web apps and mobile apps, AI, ML, Deep Learning, is powered by robust codes. In fact, coding is heavily influencing all the major industries in the world today. Stressing the importance of coding, Steve Jobs had once stated:

“Everybody in this country should learn how to program a computer… because it teaches you how to think.”

Apart from teaching you how to think, coding is also an excellent career option. There is always a great demand for skilled and trained professionals who have a flair in coding and are well-versed with the major programming languages. If you know how to code, the world of IT will provide you with an array of options such as data developer, data analyst, data architect, data engineer, and so on. Also, today there are many coding internships offered by reputed companies, so, even freshers can get their career started in coding.  

So, why not utilize this semester break wisely by strengthening your coding skills?

Online platforms are a great way to get introduced to the world of coding. They allow you to learn according to your own pace and convenience. Coding Ninjas has some of the most well-crafted online coding courses. There are both Foundation Courses for beginners as well as Advanced Courses. Let’s have a look at them!

Foundation Courses:

  1. Inception – C++ Foundation with Data Structures

Course duration: 2-3 months

Dedication time: Minimum 6-7 hours per week

Topics covered: Flow Charts; Conditionals and Loops; Operators and Patterns; Functions; Arrays; Strings; Pointers, and Dynamic Allocation.

C++ is a highly efficient and powerful Object Oriented Programming Language including concepts such as Data hiding, Dynamic Binding, Polymorphism, Operator Encapsulation, and Inheritance. In this foundation course module, our expert instructors will walk you through the basics of the programming language and move on to more complex concepts like Pointers and Dynamic Allocation. In the course of thirty lectures, students will learn how to solve over 300 problems.

  1. Nucleus – JAVA Foundation with Data Structures

Course duration: 2-3 months

Dedication time: Minimum 6-7 hours per week

Topics covered: Flow Charts; Conditionals and Loops; Operators and Patterns; Functions; Arrays; Strings, and Object Oriented Programming

Java is one of the most popular programming languages in the world today that has found its applications across various industries. In this course, students will receive around twenty-eight lectures from our expert instructors and will solve more than 300 coding problems. First, students will be introduced to the preliminary fundamentals of software development, and then gradually the focus will shift towards complex coding approaches.

Advanced Courses

  1. Eminence – Competitive Programming Course

Course duration: 2-3 months

Dedication time: Minimum 6-7 hours per week

Topics covered: Introduction To Competitive Programming; Recursion, Variations Of Binary Search, Variations Of Merge Sort; Number Theory; Game Theory, Probability; Dynamic Programming; etc.

This course has been specially designed to introduce the students and prepare them for solving computational problems efficiently in the field of competitive programming. By the end of the course, with almost thirty topics covered, you’ll be all ready to take part in programming competitions and face tough challenges easily.

  1. Cognizance – Machine Learning

Course duration: 2-3 months

Dedication time: Minimum 6-7 hours per week

Topics covered: Supervised Learning; Unsupervised Learning; Linear Regression; K-Mean Algorithm; Data Visualisation; Deep Learning; Python; Github, and TensorFlow.

Machine Learning (ML) is one of the hottest topics in the world of Computer Science. ML algorithms are everywhere, from online shopping sites and music platforms to self-driving cars. With endless possibilities, ML is something you should definitely consider learning. This course will not only help you brush up on the basic concepts of ML but also help you explore new areas of research and applications of ML.

If you feel like getting hands-on training from instructors, Coding Ninjas also has Classroom Programs. In addition to the online courses mentioned above, the classroom courses have two additional programs – Alchemy and Envision. While Alchemy focuses on teaching web development with Ruby on Rails and the tools used,  Envision trains students in android app development.

So, are you ready to make the best use of your semester break with coding?

Python vs Java: How Do They Compare?

When it comes to programming and coding, the programming language only plays half the role. Most of the heavyweight work is done by the ability of the coder to think on his toes – irrespective of the language. Having said that, it’s still a pickle of a situation if you’re new to the world of programming but know little about the differences in various programming languages. Today, we’ll look at a comparison between two of the most popular languages among developers far and wide – Java and Python,

Both these programming languages have their similarities and unique features that set them apart. The most striking similarity between Python and Java is their “object-oriented” design. However, they have prominent differences too.

Let’s us take a closer look at the areas where Python and Java differ from each other.

Dynamic vs. Static Typing

The key difference between Python and Java is that while the former is dynamically typed, the latter is statically typed.

When we say that Python is dynamically typed, we mean to say Python allows you to alter the type of variables used. For instance, you can replace an integer in your code with a string. However, you need to ensure that the object that a name refers to supports the operations necessary for running that particular object instances in the code. For example, a code may have two types of object – Cat, and Dog. While Cat and Dog may both support the operation “run,” Dog also corresponds to “friend.” As long as the code only refers to the common operation“ run,” it won’t matter if it pertains to Cat or Dog. This is known as “Duck Typing.”

Contrary to this, Java requires you to strictly define the type of a variable the instant you first feed it in the code. You cannot change the variable type later in the program. This is known as static typing.

While many coders argue that dynamic typing is convenient and more comfortable to work with, others would argue that static typing helps reduce the risk of undetected errors in the code.

Indentation vs. Braces

Like most programming languages, Java uses curly braces to define the beginning and end of every function and class in the code. Python, on the other hand, uses indentation to segregate the code into blocks. An advantage of indentation is that it allows you to build your program in a way that’s easier to read and understand. Also, there remains no possibility of errors in the code due to missing braces. However, while working with Python, you need to make sure to end the indentation with a colon.

Compatibility vs. Speed

When it comes to portability and compatibility, Java has the upper hand over Python – it can be used to design platform-independent apps. If any device (PC or mobile) can run JVM, it can support Java applications without any hassle. Considering how popular Java is for web apps and desktop programs, it can be safely assumed that most devices are JVM integrated. Hence, they can run any Java program efficiently. Contrary to this, to run Python programs, you need a compiler to convert the Python code into the version that’s compatible with your operating system. However, when a Java code runs on JVM, it usually runs slower than Python codes.

When these features are compared, Python would be recommended by most programmers as the easier language of the two, ideal for rookies. However, Java holds a prominent place among programming languages and is widely used for developing Android apps and web apps. So, no matter which language you choose, you need to remember that programming is fundamentally about logic. It is about building your computational skills. The key is to be versatile. When you master one programming language and have the basic understanding of programming concepts, you can effortlessly switch to any other programming language.

All of these differences will help you in choosing a language to begin with. But, like we said earlier, at the end of the day, it’s all about logical reasoning and the ability to think on your toes. And we’re here exactly for that. Drop by at Coding Ninjas and set your first steps right. We help you develop the required knowledge to tackle any programming language.

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5 essential tools for front-end web development:


If you just decided to continue reading what’s under this headline, then you must be having basic knowledge of HTML and CSS. If you don’t, though, don’t fret too much. Keep this tab open, and read our article on building your first HTML page. You’ll be good to go.

However, the world of front-end web development is extremely wide and goes beyond just notepad or a simple text editor. Let’s look at five essential tools that must be in your toolkit to help you better:

Sublime Text:

Sublime is a sophisticated, cross-platform text editor, found invaluable to its users. It provides quick navigation to files, instant jumps to symbols and allows moving around lines of text very quickly with only a few keystrokes. It eases writing the code by allowing multiple selections for manipulating the lines of text. Its ‘Split Editing’ feature edits multiple files side by side, saving time to its best. This editor offers power tools for code indentation and everything in Sublime Text is customizable with the help of simple JSON files. It is a project-specific editor which allows you to switch between projects instantly. With its numerous useful features and unmatched responsiveness, Sublime Text sets the bar for performance.


Bootstrap is a flexible, open source, HTML, CSS, and JavaScript framework for faster and easier web development. It offers ready-made blocks of code that are free to use and save a lot of time as there is no need of writing code from scratch. It follows Mobile First Approach and is used for creating responsive web design where the layout of the page is dynamically adjustable according to the specifications of the devices it is viewed on. It is highly customizable and provides cross-browser compatibility.


Sass (Syntactically Awesome Style Sheets) is a preprocessor scripting language used to write code which is compiled into Cascading Style Sheets, CSS. One should think of Sass as “CSS with superpowers” because of its various added advantages, one of them being the high degree of code reusability that it offers. Code reusability is performed via ‘sass variables’ and ‘mixins’. Mixins are used to group CSS descriptions and reuse them wherever required throughout the file. Sass breaks down the complex code into small code snippets, making the CSS even more modular and less complicated. It allows nesting in coding, making development a more pleasant experience.


React is the JavaScript library which is used in the development of the single-page applications. This toolkit follows the component-centric approach, i.e., it breaks apart the large complex user interface into small components which can be used for every web developer. We use React to describe the Web User Interfaces. We tell it what do we need in our User Interface, and it translates our declarative description into actual user interfaces in the browser. React uses Virtual Document Object Model (Virtual DOM). It gives a virtual representation of the updates if the entire page is rendered on each change, while changes are only rendered in the subcomponents that change. The motive of the React library is to provide speed, scalability, and simplicity to the web developers for better designing at the front-end.


Git is a version control system, or you can think of it as the software used by the web developers to keep track of all the activities involved in their project. As Git maintains a complete history of all the changes made in all the web files, you can go back and restore all the details of any version. If you’re continually making changes in your HTML, CSS or any other data, and you want to back up every version of every changed file in your project, Git is strongly recommended for you.  
Our specialized course on web-development takes you through the world of HTML, CSS, JavaScript, Ruby, and a lot more. Get yourself enrolled ASAP and build an awesome web-app for yourself!