Course Title: Beginner Data Analysis with Python

📅 Duration: 4 Weeks (Twice a Week)
🖥 Format: Online Live Sessions + Hands-on Assignments

Week 1: Introduction to Data Analysis & Python Basics

📌 Session 1:

• Introduction to Data Analysis & Its Applications

• Overview of Python for Data Analysis

• Setting Up the Environment (Jupyter Notebook, Anaconda, Google Colab)

• Working with Python Data Structures (lists, tuples, dictionaries)

• Mini Project: Simple Data Summary with Lists & Dictionaries

📌 Session 2:

• Introduction to NumPy for Data Handling

• Creating & Manipulating Arrays (numpy.array, reshape, slicing)

• Basic Statistical Operations (mean, median, std)

• Mini Project: Analyzing Temperature Data with NumPy

Week 2: Data Manipulation with Pandas

📌 Session 3:

• Introduction to Pandas for Data Analysis

• Loading Data from CSV, Excel, and JSON Files

• DataFrames & Series: Selection, Filtering, & Sorting

• Handling Missing Data (dropna(), fillna())

• Mini Project: Cleaning and Analyzing Sales Data

📌 Session 4:

• Data Transformation & Aggregation (groupby(), pivot_table())

• Creating New Columns & Applying Functions (apply(), map())

• Handling Dates & Time-Series Data

• Mini Project: Analyzing Daily Sales Trends

Week 3: Data Visualization & Basic Statistics

📌 Session 5:

• Introduction to Data Visualization with Matplotlib & Seaborn

• Creating Line, Bar, and Scatter Plots

• Customizing Plots (Labels, Titles, Colors, Legends)

• Mini Project: Visualizing Stock Market Trends

📌 Session 6:

• Introduction to Basic Statistics for Data Analysis

• Understanding Distributions, Variance, & Correlations

• Introduction to Hypothesis Testing

• Mini Project: Analyzing Customer Behavior with Statistical Insights

Week 4: Real-World Data Projects & Final Project

📌 Session 7:

• Introduction to Data Storytelling & Report Generation

• Combining Multiple Datasets (Merging & Joining in Pandas)

• Automating Data Processing Tasks

• Mini Project: Analyzing COVID-19 Trends

📌 Session 8:

• Final Project Implementation & Debugging

• Best Practices in Data Analysis (Data Cleaning, EDA, Documentation)

• Final Project Showcase & Code Review

• Next Steps: Moving to Intermediate Data Analysis & Machine Learning