Unit Structure

            
Data Analysis and ML with Python
|
├── 1. Key Python Libraries for ML & Data Analysis
│   ├── NumPy, SciPy, Matplotlib, Pandas, Scikit-Learn
│
├── 2. NumPy Basics
│   ├── Multidimensional Arrays (ndarrays)
│   ├── Creating ndarrays
│   ├── Data Types for ndarrays
│   └── Basic Indexing and Slicing
│
├── 3. Getting Started with Pandas
│   ├── Series, DataFrames, and Index Objects
│   ├── Re-indexing
│   ├── Indexing, Selection, and Filtering
│   ├── Sorting and Ranking
│   └── Loading Data (CSV and other structured formats)
│
├── 4. Data Preprocessing and Handling
│   ├── Normalizing Data
│   ├── Dealing with Missing Data
│   ├── Data Manipulation
│   │   ├── Alignment, Aggregation, Summarization
│   ├── Group-based Operations (Split-Apply-Combine)
│
├── 5. Statistical and Time Series Analysis
│   ├── Statistical Analysis with Pandas, Date and Time Series Analysis
│
└── 6. Data Visualization
    └── Visualizing Data using Matplotlib and Pandas
            
        

• Before we dive deep into machine learning algorithms, it’s essential to get comfortable with handling, analyzing, and visualizing data — and Python makes this incredibly powerful (and fun!) with its rich ecosystem of libraries.
• This unit introduces you to the core Python tools used in data analysis and machine learning, like NumPy, Pandas, Matplotlib, and Scikit-Learn. We start with NumPy, which gives us high-performance tools to work with arrays — the backbone of data in ML. You'll learn how to create arrays, slice and index them, and understand how data types work behind the scenes.
• Then we move on to Pandas, your go-to library for dealing with real-world datasets. You’ll learn how to use Series and DataFrames, reindex data, filter and sort it, and load datasets from files like CSVs. It also covers cleaning data, handling missing values, and performing advanced operations like grouping, aggregating, and normalizing data.
• You’ll also explore basic statistical analysis and dive into time series data, which is especially useful for things like stock trends or sensor data. Finally, you’ll learn how to visualize your data using Matplotlib and Pandas' built-in plotting — because making sense of data visually is a key skill in any ML or data science role.