DeepLearning.AI Data Analytics Professional Certificate Course Syllabus

Overview: This professional certificate offers a comprehensive, beginner-friendly introduction to data analytics, combining foundational theory with hands-on practice in Python, SQL, and generative AI. Learners will progress through six modules covering the full data lifecycle—from data collection and wrangling to analysis, visualization, and AI-enhanced storytelling. With a project-driven structure, the course prepares learners for real-world analytics tasks. Estimated total time: 120–150 hours, designed for self-paced learning with lifetime access.

Module 1: Foundations of Data Analytics

Estimated time: 15 hours

• Introduction to data analytics and its role in business strategy
• Understanding the data analytics process and data lifecycle
• Key data roles and responsibilities in organizations
• Types of data and methods of data collection
• Introduction to Python and SQL in analytics workflows

Module 2: Statistics and Data Wrangling

Estimated time: 20 hours

• Descriptive statistics and probability fundamentals
• Hypothesis testing and statistical inference
• Data wrangling concepts and best practices
• Cleaning and preparing datasets for analysis
• Techniques for handling missing or inconsistent data

Module 3: Data Analysis and Visualization with Python

Estimated time: 25 hours

• Using pandas and NumPy for data manipulation
• Performing exploratory data analysis to uncover trends
• Creating visualizations with matplotlib and seaborn
• Automating analysis workflows using Python scripts
• Interpreting visual outputs for decision-making

Module 4: SQL for Data Analytics

Estimated time: 20 hours

• Writing SQL queries to filter and sort data
• Joining and aggregating data from multiple tables
• Using subqueries and nested queries for complex analysis
• Integrating SQL with Python for end-to-end workflows
• Querying real-world datasets to extract insights

Module 5: Generative AI in Analytics

Estimated time: 15 hours

• Integrating generative AI tools into the analytics pipeline
• Using AI to summarize findings and generate reports
• Enhancing data storytelling with AI-driven insights
• Automating repetitive data tasks using AI assistants
• Understanding ethical considerations and limitations of AI in analytics

Module 6: Data Analytics Capstone Project

Estimated time: 30 hours

• Analyze a real-world dataset from start to finish using Python and SQL
• Create data visualizations to support key business insights
• Present findings using AI-generated narratives and reports

Prerequisites

• Familiarity with basic computer operations
• No prior programming experience required, but comfort with technology is helpful
• Basic understanding of business concepts is beneficial

What You'll Be Able to Do After

• Perform end-to-end data analysis using Python and SQL
• Apply statistical thinking to real-world data problems
• Create compelling data visualizations and narratives
• Use generative AI tools to enhance analytical workflows
• Build a portfolio-ready capstone project demonstrating job-ready skills