1.1 - Getting Started with Python
1.2 - Python Programming Basics
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2.1 NumPy for Numerical Data
2.2 Why Do We Need Numpy?
2.3 Multidimensional Arrays
2.4 Basic Statistics with NumPy
2.5 Reshaping and Transforming Data
2.6 Indexing and Sorting Data
3.1 Introduction to Pandas
3.1.1 Practical Activity - Introduction
3.1.2 Practical Activity - DataFrames
3.1.3 Practical Activity - Series
3.1.4 Practical Activity - Filtering
3.1.5 Practical Activity - Player Picks
3.2 Exploring the Data Frame with Pandas
3.2.1 Practical Activity - Exploring DataFrames
3.2.2 Practical Activity - Currency Data
3.2.3 Practical Activity - Modifying DataFrames
3.2.4 Practical Activity - Spotify Data Explorer
3.2.5 Practical Activity - Pandas Dataframe Sorting
3.2.6 Practical Activity - Filtering and Selection
3.2.7 Practical Activity - Explore, Filter, Sort and Analyse
3.2.8 Practical Activity - Merge and Joining Data
3.2.9 Practical Activity - Merged Markets
3.3 Cleaning Data with Pandas and Python
3.3.1 Practical Activity - Cleaning Missing Values
3.3.2 Practical Activity - Cleaning Duplicate Data
3.3.3 Practical Activity - Practice Data Cleaning
3.3.4 Practical Activity - Invalid Values
3.3.5 Practical Activity - From Raw to Refined
4.1 Visualising Data with Matplotlib
4.1.1 Practical Activity - Introduction
4.1.2 Practical Activity - Advanced Plot Positioning
4.1.3 Practical Activity - Enhancing Visualizations
4.1.4 Practical Activity - Understanding Matplotlib's Architecture
4.1.5 Practical Activity - Understanding Relationship Plots
4.1.6 Practical Activity - Exploring Relationships
4.1.7 Practice Matplotlib with Business Sales Snalysis
4.1.8 Practical Activity - U.S. Budget
4.1.9 Practical Activity - Distribution Plots
4.1.10 Practical Activity - Distribution Plots : Black Friday
4.1.11 Practical Activity - Mapping COVID-19 Crisis
4.2 Advanced Data Visualisation
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5.1 Time Series Analysis
5.2 Introduction to Machine Learning
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6.1 Project Planning and Data Collection
6.2 Data Preprocessing and EDA
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