Before diving into data science tasks, it’s important to understand some fundamental Python concepts: Variables and Data Types: Python supports various data types like integers, floats, strings, and more. Understanding these types helps when working with datasets that have different kinds of information. Control Structures: Python uses control structures like loops (for, while) and conditional statements (if, else) to control the flow of programs, essential for data processing. Functions: Functions are reusable blocks of code that perform a specific task. In data science, functions help to organize code and make it modular. Lists, Dictionaries, and Tuples: These data structures help in storing and manipulating collections of data. For example, lists can store rows of data, while dictionaries store key-value pairs, which are essential for structured data handling. Libraries: Python’s real power in data science comes from its libraries. The ability to import and use these libraries enables data scientists to perform complex tasks efficiently.

NumPy In data science, machine learning, and scientific computing, efficiency and speed are essential. Python’s built-in data structures like lists are flexible but not optimized for handling large amounts of numerical data. This is where NumPy (Numerical Python) comes in. NumPy is the foundation for numerical computing in Python. It provides: Multidimensional arrays (ndarrays): Fast, memory-efficient containers for numerical data. Vectorized operations: Perform mathematical computations on entire arrays without using explicit loops, making your code shorter and faster. Linear algebra and matrix operations: Built-in functions for matrix multiplication, decomposition, and more. Integration with other libraries: Libraries like Pandas, SciPy, scikit-learn, and TensorFlow are built on top of NumPy arrays. In this unit, you will learn how to: Create and manipulate NumPy arrays Perform mathematical and statistical operations Use slicing, indexing, and broadcasting for efficient data handling Apply NumPy to solve real-world numerical problems Pandas While NumPy provides powerful numerical operations, working with structured data (like tables or spreadsheets) requires more specialized tools. That’s where Pandas comes in. Pandas is a high-performance, easy-to-use data analysis library built on top of NumPy. It provides two main data structures: Series: A one-dimensional labeled array. DataFrame: A two-dimensional labeled data structure, similar to an Excel spreadsheet or SQL table. With Pandas, you can: Import and export data from multiple file formats (CSV, Excel, SQL, JSON, etc.). Clean, filter, and transform messy datasets with ease. Handle missing data gracefully. Perform descriptive statistics and group-by operations. Merge, join, and reshape datasets for deeper analysis. In this unit, you will learn how to: Create and manipulate Series and DataFrames Load, clean, and prepare data for analysis Perform powerful data exploration and summarization Combine datasets to uncover insights

Introduction to Matplotlib & Seaborn Data analysis is incomplete without visualization — turning raw numbers into meaningful charts that reveal patterns, trends, and insights. Two essential Python libraries for this purpose are Matplotlib and Seaborn. Matplotlib Matplotlib is the foundational plotting library in Python. It provides fine-grained control over every element of a plot, from axes and labels to colors and line styles. With Matplotlib, you can create: Line plots, bar charts, scatter plots, histograms, and more Customizable, publication-quality graphs Visualizations integrated with NumPy and Pandas data Seaborn While Matplotlib is powerful, it can be verbose for creating statistical graphics. Seaborn, built on top of Matplotlib, simplifies this process by offering high-level functions with attractive default styles. With Seaborn, you can: Easily create heatmaps, violin plots, pair plots, and other statistical graphics Visualize distributions and relationships between variables Work seamlessly with Pandas DataFrames In this unit, you will learn how to: Create and customize plots using Matplotlib Use Seaborn for quick and elegant statistical visualizations Combine visualization with data analysis to tell compelling stories with data