Python for Data Science Course

• Build a strong foundation in Python programming.
• Learn to manipulate and analyze data using Python libraries.
• Create effective and insightful data visualizations.
• Gain an understanding of basic machine learning concepts.
• Apply Python skills to real-world data science problems.

Module 1: Python Basics

• Introduction to Python: Understanding Python’s role in data science and setting up your environment (Anaconda, Jupyter Notebook).
• Basic Syntax, Variables, and Data Types: The building blocks of Python programming.
• Control Structures: Mastering if-else statements, loops, and list comprehensions.
• Functions and Modules: Defining functions, importing libraries (NumPy, Pandas, Matplotlib), and writing organized Python scripts.
• Basic Data Structures: Working with lists, tuples, dictionaries, and sets, and learning how to manipulate and access these data structures.

Module 2: Data Manipulation with Pandas

• Introduction to Pandas: Working with DataFrames and Series, and loading data from various file formats.
• Data Cleaning and Preprocessing: Handling missing data, filtering, sorting, and grouping data efficiently.
• Data Transformation: Merging, joining, and concatenating DataFrames, using pivot tables, and performing data aggregation.

Module 3: Data Visualization with Matplotlib

• Introduction to Data Visualization: The importance of visualization in data science and an overview of Matplotlib and Seaborn.
• Creating Basic Plots: Line plots, bar charts, histograms, and how to customize them.
• Advanced Visualization Techniques: Creating scatter plots, box plots, heatmaps, and working with multiple plots. Learning how to save and export visualizations.