KU-Data-Science-Course

This repository contains all my solutions for the Data Science course at KU.

Some of the topics this course has covered:

• Foundations of statistical learning, probability theory.
• Classification methods, such as Linear models, K-Nearest Neighbor.
• Regression methods, such as Linear, Logistic regression.
• Bayesian Statistics
• Clustering.
• Dimensionality reduction and visualisation techniques such as principal component analysis (PCA).

Assignment 1: Basic Python, Hypothesis tests

• Assignment 1

Assignment 2: K-NN, Normalization

• Assignment 2

Assignment 3: PCA, K-means

• Assignment 3

Assignment 4: Data exploration with PCA, Clustering

• Assignment 4

Final Assignment: Linear Regression, Logistic Regression, Decision trees, Random forest, Gradient descent, Image recognition etc.

• Assignment 5