So You Want To Learn Python For Data Science?

This post is mainly geared towards folks who want to learn more about data science with python on their own.

This post is part of an article that was Originally published here.

Why Python for Data Science?

Python is mature, and there's plenty of resources available from books to online courses. It has a significant set of data science libraries one can use. It is a ready-to-use programming language with different packages for loading and playing around with data, visualizing the data, transforming inputs into a numerical matrix, or actual machine learning and assessment.

Here's detailed list of 5 Critical Skills in Python for Data Science

If you want to learn R for Data Science, check this guide

---

How (and Where) To Learn Python For Data Science❓

Python has an intuitive coding style, its ease of use and clean syntax have led it to be embraced by beginners and experts alike. I have listed some of the best (and free!!!) available resources in the following sections to help you bootstrap your career in the field of Data Science using Python.

Python For Data Science - Courses

Start with a Course or a book and study all the important topics for doing data science with Python. Our brain is similar to a muscle, Keeping your brain “fit” with deliberate practice almost every day will help you find a sweet spot for Python.

1. Python Programming Track - DataCamp

3. IBM Python for Data Science - IBM

3. IBM Data Science Professional Certificate - IBM 😎

---

Get Good at Stats and Maths 📊

It's easy to fall into a state of depression when you don't have the know-how-to of Statistics and Maths when learning Numpy, Pandas or Scikit-learn. I hope that the following resources will help you to start building the Data Science skills required today.

---

Statistics for Data Science Courses

If you need an introduction to Statistics, start with any of the beginner level course listed below. Try and integrate some of these online courses into your schedule while learning python. You'll feel very confident while learning to work with analytical libraries for Python.

1. Introduction to Probability and Data - Duke University

2. Inferential Statistics - University of Amsterdam

3. Bayesian Statistics: From Concept to Data Analysis - University of California

4. Statistics Foundations: Understanding Probability and Distributions - Dmitri Nesteruk

5. MicroMasters Program in Statistics and Data Science - Massachusetts Institute of Technology

---

Maths for Data Science Courses

You don’t need a math degree to succeed in data science. Yet, if you do have a math background, you’ll definitely get ahead. Here are some best online classes to master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

1. Introduction to Mathematical Thinking - Stanford University

2. Data Science Math Skills - Duke University

3. Introduction to Algebra - SchoolYourself

4. Algebra I - Khan Academy

Also, If you have little to no background in Maths or need a refresher, I'd suggest that get a copy of All the Mathematics You Missed: But Need to Know for Graduate School for an overview of mathematics that one should have been exposed to upon reaching Graduate School.

---

Data Science Networking for Nerds 🤓

If you are in the right group of people, you'll get the right kind of support. Find people who you could learn from and create some positive reinforcement. Here are some resources to help you get connected and understand your in-group.

1. PyData

2. Data Science Meetups

3. The Data Science Conference

4. KDNuggets Meetings

5. Machine Learning Meetups

Before You Go 🍻

If you liked this article enough, do share it with your friends and If there is anything you feel I should have included? Let me know in the comments below!