This post is mainly geared towards folks who want to learn more about data science with python on their own.
If your understanding about Data Science is a big question mark, i’ve got a one practical read for you about Learning How to Learn Data Science with Python (Statistics and Maths).
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 programming, check this article about R for Data Science Classes.
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.
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. Data Scientist with Python Track - DataCamp
3. IBM Data Science Professional Certificate - IBM 😎
4. Data Science Specialization - John Hopkins University
I have compiled 9 Best Data Science Courses from the World-Class educators according to Student rating data points.
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.
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. Probability and Statistics: To p or not to p? - University of London
3. Bayesian Statistics: From Concept to Data Analysis - University of California
4. Statistics with Python Specialization - University of Michigan
- I've as well compiled a list of Probability and Statistics for Data Science course to help you get ahead in your learning. *
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. Data Science Math Skills - Duke University
2. Introduction to Mathematical Thinking - Stanford University
3. Mathematics for Machine Learning and Data Science Specialization - Imperial College London
I just published another piece about the courses to learn Mathematics for Data Science article. Give this one a read to learn basics or get a refresher.
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.
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!
I’ve also got this Data-Centric newsletter that you might be into. I send a tiny email once or twice every quarter with some useful resource I’ve found.