Learn Data Analysis Course Syllabus

Overview: This project-driven course guides you through the complete data analysis workflow, from raw data to actionable insights. You'll gain hands-on experience with Python, Pandas, SQL, statistics, and visualization tools—all in a browser-based environment with no setup required. With approximately 18.5 hours of interactive learning, you’ll build practical skills through real-world examples and finish with a capstone project that demonstrates your abilities. Ideal for aspiring data analysts seeking job-ready skills in high-demand industries.

Module 1: Introduction to Data Analysis

Estimated time: 1.5 hours

• Overview of analysis lifecycle
• Understanding data formats
• Project planning fundamentals
• Exploring sample datasets

Module 2: Python & Pandas Essentials

Estimated time: 2 hours

• Working with Series and DataFrame objects
• Data indexing and selection
• Filtering and subsetting data
• Merging and combining datasets

Module 3: Data Cleaning & Wrangling

Estimated time: 3 hours

• Handling missing values
• Detecting and managing outliers
• Type conversion and data formatting
• Feature engineering basics

Module 4: Exploratory Data Visualization

Estimated time: 2.5 hours

• Creating histograms and box plots
• Building scatter plots and pair plots
• Generating heatmaps for correlation
• Interpreting visual insights

Module 5: Statistical Analysis

Estimated time: 2.5 hours

• Computing descriptive statistics
• Analyzing correlation between variables
• Performing hypothesis testing
• Calculating confidence intervals

Module 6: SQL for Data Analysis

Estimated time: 2 hours

• Writing SELECT statements
• Using joins and aggregations
• Constructing subqueries
• Applying window functions

Module 7: Time Series Analysis

Estimated time: 2 hours

• Handling date/time data
• Calculating rolling statistics
• Seasonal decomposition techniques
• Simple forecasting methods

Module 8: Dashboarding & Reporting

Estimated time: 2 hours

• Designing effective dashboards
• Building interactive widgets
• Using Plotly or Streamlit basics
• Publishing reports

Module 9: Capstone Project

Estimated time: 2.5 hours

• Planning an end-to-end analysis project
• Executing data ingestion and cleaning
• Performing analysis and visualization
• Presenting findings in a polished report

Prerequisites

• Basic familiarity with Python programming
• Access to a modern web browser
• No prior experience with data analysis required

What You'll Be Able to Do After

• Execute a full data analysis workflow from start to finish
• Manipulate and clean real-world datasets using Pandas
• Visualize data effectively with Matplotlib and Seaborn
• Apply statistical methods to derive meaningful insights
• Query and analyze relational databases using SQL