Data Analysis with Python Course Syllabus

Overview: This course provides a comprehensive introduction to data analysis using Python, designed for learners with basic Python knowledge. You'll gain hands-on experience working with real-world datasets, learning key techniques from data collection to model development and evaluation. The course blends theory with practical exercises using Python libraries like Pandas, NumPy, and scikit-learn. With a flexible structure, it takes approximately 16 hours to complete, ideal for working professionals aiming to build foundational data analysis skills. Upon completion, you'll earn an IBM digital badge and a certificate to showcase your proficiency.

Module 1: Importing Data Sets

Estimated time: 3 hours

• Understand different data formats (CSV, JSON, Excel)
• Import data using Pandas
• Load data from web and local sources
• Handle data encoding and parsing issues

Module 2: Cleaning and Preparing the Data

Estimated time: 3 hours

• Identify and handle missing values
• Normalize and standardize data
• Correct data types and format inconsistencies
• Detect and manage outliers

Module 3: Summarizing the Data Frame

Estimated time: 3 hours

• Generate descriptive statistics using Pandas
• Visualize data distributions with Matplotlib and Seaborn
• Explore correlations and relationships between variables
• Perform exploratory data analysis (EDA)

Module 4: Model Development

Estimated time: 3 hours

• Introduction to regression modeling
• Build linear and multiple regression models using scikit-learn
• Analyze variable relationships
• Interpret model coefficients and outputs

Module 5: Model Evaluation

Estimated time: 2 hours

• Evaluate regression models using metrics (MSE, R-squared)
• Use train-test splits for performance assessment
• Apply cross-validation techniques

Module 6: Final Project

Estimated time: 2 hours

• Import and clean a real-world dataset
• Perform exploratory data analysis and visualization
• Build and evaluate a regression model

Prerequisites

• Basic understanding of Python programming
• Familiarity with Jupyter Notebooks
• Introductory knowledge of data concepts

What You'll Be Able to Do After

• Import and clean diverse data formats using Python
• Prepare and manipulate data for analysis with Pandas and NumPy
• Summarize data using statistical and visualization tools
• Develop and evaluate regression models with scikit-learn
• Create end-to-end data analysis pipelines