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 scientist 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