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.

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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."

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Grokking Data Science

### Why NumPy
Data comes in all shapes and sizes. We can have image data, audio data, text data, numerical data, etc. We have all these heterogeneous sources of data but computers understand only 0's and 1's --- At its core, data can be thought of as arrays of numbers. In fact, the prerequisite for performing any data analysis is to convert the data into numerical form. This means it is important to be able to store and manipulate arrays efficiently, and this is where Python's NumPy package comes into picture.

Now, you might be questioning, *"When can I use Python's built-in lists and to do all sorts of computations and manipulations through list comprehensions, for-loops, etc., why should I bother with NumPy arrays?"* You are right in thinking so because, in some aspects, NumPy arrays are like Python’s lists. Their advantage is that they provide **more efficient storage and data operations** as the arrays grow larger in size. This is the reason NumPy arrays are at the core of nearly all data science tools in Python. This, in turn, implies that it is essential to ***know NumPy well!***

# Why NumPy
Data comes in all shapes and sizes. We can have image data, audio data, text data, numerical data, etc. We have all these heterogeneous sources of data but computers understand only 0's and 1's --- At its core, data can be thought of as arrays of numbers. In fact, the prerequisite for performing any data analysis is to convert the data into numerical form. This means it is important to be able to store and manipulate arrays efficiently, and this is where Python's NumPy package comes into picture.

Now, you might be questioning, *"When can I use Python's built-in lists and to do all sorts of computations and manipulations through list comprehensions, for-loops, etc., why should I bother with NumPy arrays?"* You are right in thinking so because, in some aspects, NumPy arrays are like Python’s lists. Their advantage is that they provide **more efficient storage and data operations** as the arrays grow larger in size. This is the reason NumPy arrays are at the core of nearly all data science tools in Python. This, in turn, implies that it is essential to ***know NumPy well!***

Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Learning NumPy - An Introduction

Why NumPy