Korbit’s Data Science Learning Modules

Korbit offers everything you need from A-Z about data science in one place.
See our comprehensive list of learning modules below.

Comprehensive Course Catalogue

Explore Korbit’s learning modules and projects

Korbit Platform

1. Tutorial

Welcome to Korbit
Python

2. Hello Python

- Hello Python
- Variables and Types
- Operators and Expressions
- Input / Output
- If Else
- For Loops
- While Loops
Mathematics

3. Probability Basics

- Introduction to Probability
- Discrete vs. Continuous Random Variables
- Expected Value vs. Sample Mean
- Variance and Standard Deviation
- Binomial Distribution
- Normal Distribution
- Joint and Marginal Probability
- Distributions
- Conditional Probability
- Bayes' Theorem
Mathematics

4. Linear Algebra Basics

- Why Learn Linear Algebra?
- Vectors, Matrices and Scalars
- Multiplication and Transpose
- Dot Product
- Norm and Euclidean Distance
- Matrix Multiplication
- Identity Matrix
Statistics

5. Introduction to Statistics

- Introduction to Statistics
- Levels of Measurement and Sampling Techniques
- Graphs
- Mean, Median and Mode
- Percentiles and Quartiles
Data science

6. Exploratory Data Analysis

- Descriptive Statistics
- Introduction to Graphs
- What is a Dataset?
- Exploratory Data Analysis
Data science

7. RFM Analysis for Customer Segmentation

- Descriptive Statistics
- Introduction to Graphs
- What is a Dataset?
- Exploratory Data Analysis
Machine learning

8. What is Machine Learning?

- What is Machine Learning?
- Supervised Learning
- Classification
- Regression
- Unsupervised Learning
- Clustering
- Reinforcement Learning
Machine learning

9. Linear Regression

- Linear Regression
- Interpolation vs. Extrapolation
- Evaluation Metrics (Regression)
- Linear Regression with Categorical Features
- Conditions for Linear Regression
- Handling Outliers in Linear Regression
Machine learning

10. Logistic Regression

- What is Machine Learning?
- Classification
- Binary Classification
- Logistic Regression Basic
- Sigmoid Function
- Evaluation Metrics (Classification)
Machine learning

11. Data Preprocessing

- Overview of Data Preprocessing
- Data Cleaning
- Handling Outliers
- Splitting Data
- Feature Engineering
- One-Hot Encoding
- Feature Importance
- Feature Scaling
- Dimensionality Reduction
- Feature Selection
- Principal Component Analysis
Machine learning

12. Classification

- Classification
- Binary Classification
- Logistic Regression Basic
- Sigmoid Function
- Evaluation Metrics (Classification)
- Binary Classification for Imbalanced Classes
- Naive Bayes’ Classifiers
- K-Nearest Neighbours
Machine learning

13. Foundational Machine Learning Theory

- Splitting Data
- Cost and Loss Functions
- Cross Validation
- Parameters vs. Hyperparameters
- Hyperparameter Tuning
- Overview of Regularization
- L1 vs. L2 Regularization
Machine learning

14. CART Decision Trees and Random Forests

- Introduction to Decision Trees
- CART Decision Tree Splits
- Decision Tree Selection Criteria
- Introduction to Random Forests
Machine learning

15. Unsupervised Learning

- Unsupervised Learning
- Clustering
- K-Means Clustering
- Dimensionality Reduction
- Principal Component Analysis
Machine learning

16. Supervised Learning

- Motivation
- Supervised Learning
- Linear Approximators
- Generalized Linear Approximators
- Overfitting and Underfitting
- Bias and Variance: Cross-Validation
- Bias-Variance Decomposition
- Overview of Logistic Regression
- Regularization for Logistic Regression
Machine learning

17. Predicting Credit Card Fraud

- What is a Dataset?
- Exploratory Data Analysis
- Splitting Data
- Feature Scaling
- Principal Component Analysis
- Logistic Regression Basic
- Evaluation Metrics (Classification)
- Predicting Credit Card Fraud with Logistic Regression
Machine learning

18. Predictive Maintenance

- Introduction to Decision Trees
- CART Decision Tree Splits
- Decision Tree Selection Criteria
- Introduction to Random Forests
- Overview of Data Preprocessing
- Feature Engineering
- Feature Scaling
- Feature Selection
- Splitting Data
- Cross Validation
- Hyperparameter Tuning
- Predictive Maintenance
Machine learning

- Exploratory Data Analysis
- Overview of Data Preprocessing
- One-Hot Encoding
- Feature Importance
- Feature Scaling
- Feature Selection
- Logistic Regression Basic
- Evaluation Metrics (Classification)
Machine learning

20. Handling Outliers

- Introduction to Graphs
- Data Cleaning
- Handling Outliers
- Handling Outliers in Linear Regression
Machine learning

21. Feature Manipulation

- Feature Engineering
- One-Hot Encoding
- Feature Scaling
- Dimensionality Reduction
- Feature Importance
- Feature Selection
- Principal Component Analysis
Machine learning

22. Splitting Data

- What is a Dataset?
- Splitting Data
- Cross Validation
Machine learning

23. Regularization

- Splitting Data
- Cost and Loss Functions
- Overview of Regularization
- L1 vs. L2 Regularization
Machine learning

24. Dimensionality Reduction

- Unsupervised Learning
- Dimensionality Reduction
- Feature Scaling
- Feature Selection
- Principal Component Analysis
Machine learning

25. Clustering

- Unsupervised Learning
- Clustering
- K-Means Clustering
Machine learning

26. Naive Bayes’ Classifiers

- Classification
- Evaluation Metrics (Classification)
- Joint and Marginal Probability Distributions
- Conditional Probability
- Bayes' Theorem
- Naive Bayes’ Classifiers
Machine learning

27. Hyperparameter Tuning

- Splitting Data
- Cost and Loss Functions
- Cross Validation
- Parameters vs. Hyperparameters
- Hyperparameter Tuning
Deep learning

28. Introduction to Neural Networks

- The Rise of Deep Learning
- An Artificial Neuron
- Example: 'OR' Neuron Using Sigmoid Activation
- Example: Neuron with Rectified Linear Function
- One-Layer Neural Network
- Example: 'XOR' Neural Network
- Deep Training Neural Networks
Deep learning

29. Training Neural Networks

- The Backpropagation Algorithm
- Optimization Difficulties
- Optimization Algorithms
- Visualizing How a Neural Network Is Trained
- Example: Data Preprocessing
- Overview of Model Selection
Deep learning