Introduction to Python and its data analysis libraries (NumPy, Pandas)
Data Wrangling: cleaning and manipulating data
Data visualisation with Matplotlib and Seaborn
Exploratory Data Analysis (EDA)
Statistical Analysis: Probability theory and Inferential statistics
Regression Analysis: Linear regression, multiple regression
Machine Learning Fundamentals
Classification: K-nearest neighbours, Logistic Regression, Decision Trees, Random Forests
Clustering: K-means Clustering, Hierarchical Clustering
Model evaluation and selection
Capstone project: students can apply the skills and techniques learned throughout the course to complete a final project.
Data Analysis with Python
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