Learn Data Science Course Syllabus

Overview: This beginner-friendly, 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, and data visualization tools—all in a browser-based environment with no setup required. With approximately 18.5 hours of interactive content, you’ll build practical skills through real-world examples and finish with a capstone project that showcases your ability to analyze and present data effectively.

Module 1: Introduction to Data Analysis

Estimated time: 1.5 hours

• Overview of the data analysis lifecycle
• Understanding common data formats and sources
• Project planning and defining analysis goals
• Exploring sample datasets

Module 2: Python & Pandas Essentials

Estimated time: 2 hours

• Working with Pandas Series and DataFrame objects
• Indexing and selecting data
• Filtering and querying data frames
• Merging and combining datasets

Module 3: Data Cleaning & Wrangling

Estimated time: 3 hours

• Handling missing values and data imputation
• Detecting and managing outliers
• Type conversion and data standardization
• Feature engineering and creating derived variables

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 analysis
• Interpreting visualizations to uncover patterns

Module 5: Statistical Analysis

Estimated time: 2.5 hours

• Computing descriptive statistics
• Analyzing correlation between variables
• Performing hypothesis testing (t-tests, chi-square tests)
• Constructing and interpreting confidence intervals

Module 6: SQL for Data Analysis

Estimated time: 2 hours

• Writing SELECT statements for data retrieval
• Using JOINs to combine tables
• Applying aggregations and GROUP BY clauses
• Utilizing subqueries and window functions

Module 7: Time Series Analysis

Estimated time: 2 hours

• Handling date and time data in Python
• Calculating rolling statistics
• Decomposing seasonal patterns
• Creating simple forecasts and trend visualizations

Module 8: Dashboarding & Reporting

Estimated time: 2 hours

• Designing effective data dashboards
• Building interactive widgets with Plotly
• Introduction to Streamlit for reporting
• Publishing insights in a shareable format

Module 9: Capstone Project

Estimated time: 2.5 hours

• Planning an end-to-end data analysis project
• Executing data ingestion, cleaning, and analysis
• Creating visualizations and a final report

Prerequisites

• Familiarity with basic Python syntax
• Basic understanding of variables and data types
• Access to a modern web browser

What You'll Be Able to Do After

• Perform end-to-end data analysis using Python and Pandas
• Clean and transform messy real-world datasets
• Create insightful visualizations using Matplotlib and Seaborn
• Query and analyze relational data using SQL
• Build interactive dashboards and present findings professionally