IBM Data Science Professional Certificate Course Syllabus

This IBM Data Science Professional Certificate on Coursera is designed for beginners and provides a comprehensive, hands-on introduction to data science. The program covers essential skills including Python, SQL, data analysis, visualization, and machine learning. With a total time commitment of approximately 11-14 months at 5-7 hours per week, learners will progress through foundational concepts to real-world application via a capstone project. Each module blends theory with practical exercises using industry-standard tools.

Module 1: Foundations of Data Science

Estimated time: 60 hours

• Understand the role and responsibilities of a data scientist
• Explore key data science methodologies and workflows
• Learn about data structures and types
• Get started with Jupyter Notebooks and Python programming

Module 2: Data Analysis and Visualization

Estimated time: 80 hours

• Use Pandas for data manipulation and cleaning
• Apply NumPy for numerical operations
• Create visualizations using Matplotlib and Seaborn
• Perform exploratory data analysis (EDA) to uncover patterns

Module 3: Machine Learning with Python

Estimated time: 120 hours

• Understand the basics of machine learning and AI
• Build regression models using Scikit-learn
• Implement classification algorithms
• Apply clustering techniques for unsupervised learning

Module 4: Databases and SQL for Data Science

Estimated time: 80 hours

• Learn SQL syntax for querying databases
• Extract, filter, and manipulate data using SELECT, WHERE, GROUP BY
• Work with relational databases and cloud-based storage

Module 5: Data Science Project Methodology

Estimated time: 40 hours

• Define data science project objectives
• Follow a structured approach from data collection to deployment
• Document and communicate findings effectively

Module 6: Final Project

Estimated time: 150 hours

• Solve a real-world data science problem
• Apply data cleaning, analysis, visualization, and modeling techniques
• Present insights using data storytelling and submit a portfolio-ready project

Prerequisites

• No prior programming experience required
• Basic computer literacy
• High school level mathematics knowledge

What You'll Be Able to Do After

• Use Python and Jupyter Notebooks for data analysis
• Perform data cleaning and exploratory analysis with Pandas and NumPy
• Create insightful visualizations using Matplotlib and Seaborn
• Build and evaluate machine learning models with Scikit-learn
• Write SQL queries to extract and analyze data from databases