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?