Learn To Code for Data Science - part I

This blog post is not a bookish guide to tell you an unrealistic way to learn to code for Data Science in a month. So, this is the right place to learn easy and realistic ways to take your Data Science skills to next level! It took me months or probably years to be able to program a Data Science solution on my own because I didn't know where to start and where to go next. But now as I have figured out all the dos and don'ts of learning to code for Data Science, I am putting it right here for you. 

Since we are concerned about learning to code for Data Science, we have to pick one language from Python and R. I recommend you to start from Python as it is easy to learn and there are comparatively more resources available in Python on the internet for FREE. Now as you have decided to learn Python, where should you start from? Follow the steps below to start from the very beginning.

1. Learn the basics

First things first, before writing your first program in Python you need to understand what Python is and how to set it up. Explore things like is Python a general-purpose language or a special-purpose language? Is it a High-level language or a low-level language? What are the applications of Python and how can you install it. 

Now that you know what Python is and how it works, you are ready to write your first program in Python. Write your very first 'Hello World!' program and woohoo! You have already used a Python built-in function. 

2. Data types and variables

What are data types and variables? What are the different data types in Python, How do you convert data types and assign variables in Python? Learn to manipulate all data types and discover which data types are mutable and which are immutable. Solve relevant exercises to practice what you have learned and build a concrete understanding of data types and variables in Python.

3. On to the intermediate level

Congratulations! You are done with the basics and all ready to learn harder concepts. At this stage, you will learn conditional statements (if-else), functions (including built-in functions and lambda) and loops to solve more difficult programming problems.

4. Data structures and algorithms

A data structure is a way of organizing data to be processed by programs and an algorithm is a sequence of solving a programming problem. Learning data structures and algorithms will help you build an understanding of breaking a larger problem into many small ones and solving them one by one. It makes solving complex problems easy and faster.

5. Learn useful libraries for Data Science

Now that you know you to write meaningful programs, you are all set to learn data science specific libraries. These libraries will help you explore, manipulate, visualize and model the data. You need not hurry at this step at all. Take your time to build an understanding of each of the following libraries one after another.

• Pandas and Numpy for data manipulation
• Matplotlib and Seaborn for data visualization
• Scikit-learn for Machine Learning

6. Final words

Solve Kaggle tasks and have fun with different datasets, explore them to apply what you are learning. You can also find datasets from other sources, explore them and answer questions using data. This is the only way to get yourself ready to solve real-world problems. Don't worry if you can't memorize Python syntax at once, it literally takes time and even professional programmers need google to find the right syntax for their requirements. If you are stuck on a problem for too long, take a break, have a rest, think about the problem after refreshing your mind and then give it another shot. Becoming an expert takes time and if you are committed you are surely gonna hit your goal!