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Data Science… Where to START ?!

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This is a question that I get almost every day from people, especially students, who want to start their journey into Data Science. There is so much to learn and so much to do. The overwhelming amount of resources available on the internet today makes it messy for the beginners. There is no prescribed path to follow and they are afraid of getting lost in the woods.

So here is how I started my adventure a year ago. I’m not going to tell which courses to take up, because there are so many of them that you can choose the ones that suit you the best.

1. Introduction to Data Science

First and foremost, get to know what data science is all about, what a data scientist does, how it helps the businesses. Don’t dive right into the technical concepts. Familiarise yourself with descriptive, inferential, predictive, prescriptive statistics and how they drive business decisions.

2. Learning Python

The next step is to learn a programming language. There has always been a debate about Python vs. R. I chose the former, because it’s a general purpose programming language and can be used for anything like data science, web development, building games, etc.

3. Data Science Libraries

Next is to learn the data science libraries in Python like NumPy, Pandas, Matpotlib, Scipy, etc. and practice some data analysis and visualizations.

4. Machine Learning Basics

Then move on to getting comfortable with Machine Learning basics, including the theory and mathematics behind common algorithms.

5. Applied Machine Learning

Finally, get down to some coding in Python to build ML models and solve some real problems.

6. Explore, explore, and EXPLORE!

After the above steps, you will have a strong foundation in Python and Machine Learning. Now is the time to explore different sub-domains like Natural Language Processing, Computer Vision, Business Intelligence, etc. Pick up any project you like and study about it in detail. Keep your projects as vast as possible.

The sources I utilized in the same order as above:

1. EdX

2. Codecademy

3. DataCamp

4. Coursera

5. Udemy

6. For exploring, Kaggle is the one-stop solution. Also, you can explore data sets available on QuandlUCI ML Repository, Open Govt Data – India or USA, etc. and build your own projects.

This field is so vast and fast-paced, you will never get bored!

Hope this helps 🙂

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Pranav Gupta

Pranav Gupta

Co-Founder at DataScribble
An always cheerful and optimistic guy, with a knack for achieving the set target at any cost.
I am an avid learner and never shy off from working hard or working till late. I am also a passionate reader, and love to read thriller novels, Jeffrey Archer being the favorite writer.
LinkedIn: https://www.linkedin.com/in/prnvg/
Pranav Gupta

Pranav Gupta

An always cheerful and optimistic guy, with a knack for achieving the set target at any cost. I am an avid learner and never shy off from working hard or working till late. I am also a passionate reader, and love to read thriller novels, Jeffrey Archer being the favorite writer. LinkedIn: https://www.linkedin.com/in/prnvg/

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