A Layman's Guide to Data Science Journey

It is very easy to become confused when you start your data science journey. However the problem is not dearth of study materials, but having too many of them! It is almost impossible to find the best in the heap and becoming frustrated is natural.

 

As I had started my data science journey 6 months back, I am providing list of only three best resources in each area which really helped me a lot for steady progress. I hope it will help you also to declutter your confusions and come up with an actionable plan.

 

A.      Books:

 

1.       Python Machine Learning by Sebastian Raschke

2.       Think Stats – Exploratory Data Analysis in Python  – Allen B Downey

3.       Introduction to Statistical Learning – by Gareth James (for R)

 

B.      Courses:

 

1.       CS109 – Data Science by Harvard (For more advanced, CS229 – ML by Stanford)

2.       Machine Learning by Stanford University – Coursera

3.       Machine Learning A-Z, Hand on Python & R in Data Science

 

C.      Forums:

 

       1.       AnalyticsVidhya.com

2.       kaggle.com (this is also an exvellent source of pratice data sets).

3.       Linkedn Community in data science – follow experts in the field for direction and latest trends. -

 

Now once you have gained some confidence, take up one machine learning model at a time, understand the logic behind and don’t depend only on the functions available in Python or R, practice the same with dataset to understand the behavior better. Trying to get familiar with too many models simultaneously will confuse you more. In this phase, you may also explore the book Data Mining by Charu C Aggarwal if you  inclined particularly towards data mining.