Gain insights into data science with easy-to-follow, hands-on explanations. Explore essential concepts quickly and efficiently, even without prior statistics knowledge, for a career boost.

GrokkingDataScientist.tar.gz

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Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

No need to waste hours and hours on browsing from one article to the next and piecing together the info you need to grasp important topics. No need to get overwhelmed by the information overload. Find easy to follow, hands-on, and fun explanations of all the essential topics in one place so you can quickly and efficiently learn what you need to thrive as a data scientist.

"Is this course right for me?" Continue to read to decide for yourself!

- "I want to understand this data science concept. Let me Google it". Then after hours of surfing, reading random articles, and invoking the heavens, you are more confused than before.
- "Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".
- "I have a basic knowledge of Python, willingness to learn, and commitment to become a great data scientist."

Is that you? If yes, you are at the right place.

Grokking Data Science

## A Dataset and a Machine Learning Problem, What Should You Do?

Say you have been recently hired as a Data Scientist to work on a project and you have been given some real estate data. How can you approach the problem in a **systematic and structured way** rather than ending up with a spaghetti code? What are the steps to follow? 

In this section, we are going to **deconstruct** the main step needed to work on a ML project via a real end-to-end example with code. We are going to work with a challenge based on a **Kaggle Competition**.



# A Dataset and a Machine Learning Problem, What Should You Do?

Say you have been recently hired as a Data Scientist to work on a project and you have been given some real estate data. How can you approach the problem in a **systematic and structured way** rather than ending up with a spaghetti code? What are the steps to follow? 

In this section, we are going to **deconstruct** the main step needed to work on a ML project via a real end-to-end example with code. We are going to work with a challenge based on a **Kaggle Competition**.



Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Introduction

A Dataset and a Machine Learning Problem, What Should You Do?