Applied Data Science Specialization – By IBM Course Syllabus

Overview: This specialization offers a beginner-friendly, hands-on introduction to applied data science, covering the full workflow from data cleaning to machine learning and visualization. Through five core modules and a capstone project, learners gain practical experience using Python and industry-standard tools like Jupyter notebooks, Pandas, Matplotlib, and Scikit-learn. Each module combines theory with coding labs and real-world case studies. The total time commitment is approximately 140–160 hours, designed to be completed over several months at a flexible pace.

Module 1: Python Basics for Data Science

Estimated time: 20 hours

• Introduction to Python syntax and programming environment
• Working with data types, variables, and operators
• Using loops, conditionals, and functions in Python
• Practicing coding in Jupyter notebooks

Module 2: Data Analysis with Python

Estimated time: 30 hours

• Importing and cleaning datasets using Pandas and NumPy
• Transforming data and handling missing values
• Performing descriptive statistics and aggregations
• Exploring data distributions and relationships

Module 3: Data Visualization with Python

Estimated time: 25 hours

• Creating visualizations with Matplotlib and Seaborn
• Building line plots, histograms, and scatter plots
• Customizing charts for clarity and impact
• Developing data dashboards and storytelling techniques

Module 4: Machine Learning with Python

Estimated time: 40 hours

• Understanding supervised vs. unsupervised learning
• Training regression and classification models
• Implementing clustering algorithms
• Evaluating model performance using Scikit-learn

Module 5: Applied Data Science Capstone Project

Estimated time: 30 hours

• Defining a real-world data problem
• Applying data wrangling, analysis, and modeling techniques
• Creating visualizations to communicate insights

Module 6: Final Project

Estimated time: 20 hours

• Deliverable 1: Cleaned and analyzed dataset
• Deliverable 2: Trained machine learning model with evaluation
• Deliverable 3: Final report and visualization dashboard

Prerequisites

• Familiarity with basic computer operations
• No prior coding experience required
• Willingness to learn programming concepts

What You'll Be Able to Do After

• Write Python code for data analysis tasks
• Import, clean, and analyze real-world datasets
• Create compelling data visualizations using Python
• Build and evaluate machine learning models
• Complete a portfolio-ready capstone project with IBM recognition