Statistics

Statistics is a branch of mathematics working with data collection, data organization, data analysis, interpretation and presentation.
Statistics is very important to understand the theories behind Machine Learning and Deep Learning.
Statistics can be majorly divided into:

Descriptive Statistics

Inferential Statistics

Research Design is an important topic before you get into descriptive and inferential statistics.

Click on the links above to learn more about each.
For a quick brush up on Python, click here
For a quick brush up on Scipy and Numpy, click here and here.
For a quick brush up on pandas, click here

Machine Learning

Analytics is a collection of techniques and tools used for creating value from data.
Techniques include concepts such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms.
AI, ML, and DL are defined as follows:

1. Artificial Intelligence: Algorithms and systems that exhibit human-like intelligence.
2. Machine Learning: Subset of AI that can learn to perform a task with extracted data and/or models.
3. Deep Learning: Subset of machine learning that imitate the functioning of human brain to solve problems.

Some of the Applications of AI(ML and DL) are:

1. Computer Vision
2. Natural Language Processing
3. Structured Data Analysis

Click on each to read more.
For a quick brush up on ML concepts, click here and here

Data Structures & Algorithms

Data Structures and Algorithms are the fundamentals of Computer Programming.
Check out this repo for a practical implementation of commonly used Data structures in Python