How do I start a career in Data Science?

How do I start a career in Data Science?

data science startpoint

Beginning a career in data science can be very challenging. This is largely due to the fact that data science is a relatively emerging field. Also, data science is not currently offered as a course in most universities and you are likely not going to easily find a coach to teach or guide you in your early journey to become a data scientist.

The difficult part is that you will see tones of courses, articles, Youtube videos & other resources online on how to become a data scientist and what tools to learn. This makes the journey seem like a nightmare. You get trapped in the rat-race of deciding what tool to learn (Python or R).

data science

I was trapped in this rat race for several months in my journey into the field of data science. The more articles I read on medium and quora on getting started, the more I get confused on where to start and what tool to start with. Do I learn Python or R? how much of these language do I need to know before delving into using them for data science? Which online platform is the best to start learning? These and many more where questions I struggle to get answers to.

I finally decided to get started anyway with whatever tool or resource that was at my disposal. Yes, I made lots of mistakes learning what I don’t really need and re-learning the things that gave me a push in data science. In this article I put together the key resource and tools an aspiring data scientist need to build a successful career in the field of data science. Below are the top resource and tools you need to kick-start your journey in data science:

Start with learning Python programming:
Python is one of the most widely used programming language for web programming as well as for data science. Undoubtedly, Python has the richest data science libraries (pandas, numpy, BeautifulSoup, etc.) for manipulating large and complex data sets. The large open source community constantly working to build more robust data science libraries and optimize the existing ones is another huge advantage of learning python for data science over R. The preference of Python for data science over R has also reflected in the high demand for expertise in python programming in data science jobs postings.

For those completely new to Python programming, I always advice this Python Beginners course on Codecademy.

This course is free and gives a good start with Python programming. It uses Codecademy’s web-based interactive IDE. So you may not even need to install Python to get started.
For a more detailed explanation of python basic concepts and implementations, I advise taking the introduction to python course on edx.

Learn Python for data science – Exploratory data Analysis

While python is a general purpose programming language, it was also designed specifically to be used for data science. The is because python has a rich library for carrying out exploratory and predictive data analysis. It also has libraries for doing insightful data visualizations. For a good start with exploratory data Analysis, I will recommend starting with the Introduction to Python for data science course on Datacamp. This course is free and it comes with an interactive web-based shell where you can do all your learning & coding without having to install python on your machine.

Predictive data analysis:

Taking your game further? then jump on predictive data analysis by learning how to use machine learning algorithms for building predictive models. This is the icing of the cake in your data science journey. I find this phase particularly interesting as it officially makes you a ” Data Prophet” (lol) – One that Predicts future occurrence using insight from historic data.

This machine learning course on Kaggle will give you a good start in predictive data analysis. You can also use Kaggle’s web-based Kernnel for building your predictive models. It comes with a production-ready Jupyter Notebook for doing all your predictions & a large space for cloud data storage.

After building expertise on these fundamentals, you can start your exploration the core areas of understanding the statistics and maths for data science.

All the best on your journey to building a successful career as a data scientist.

Have you got any thought to add to this article? I will be glad to read about it in the comments.