# Curriculum

## Prerequisites

### Programming

- Python
- C++ alternatives:
- name: R
- name: Matlab

### Computer Science

These theories make people think faster. They don’t pose direct limits on what data scientists can do but they will definitely give data scientists a boost.

- Data Structures
- Complexity

### Math

Some basic understanding of these is absolutely required. Higher levels of these topics will also be listed in details.

- Statistics
- Linear Algebra
- Calculus
- Differential Equations

### EDA Tools

These tools are used almost everywhere in data science.

- SQL
- numpy
- scipy
- pandas
- dask
- matplotlib
- seaborn
- plotly

## Statistics

### Descriptive statistics

It is crucial for the interpretations in statistics.

### Inferential statistics

To get closer to the ultimate question about causality

- Hypothesis Testing
- Bayesian inference
- Frequentist inference

## Visualization

### Types of Data

### Types of Charts

### Grammar of Graphics

## EDA

### Dimensionality Reduction

### Association Rules

### Anomaly Detection

## Statistical Learning

### Regression

- Linear Regression
- Higher-order Regression

### Classification

- Logistic Regression
- SVM
- Tree

## Graphs and Networks

## Natural Language Processing

Published:
by Lei Ma;

## Table of Contents

**Current Ref:**

- awesome/curriculum/index.md