IBM Applied Data Science Specialization Course Syllabus

This Applied Data Science Specialization from IBM is a beginner-friendly, project-driven program designed to equip learners with practical skills across the data science pipeline. Spanning approximately 69 hours, the course progresses from foundational Python programming to real-world data analysis, visualization, machine learning, and a capstone project. Through hands-on labs on IBM Cloud and guided projects, you'll gain experience with Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly, and Dash—building job-ready competencies for roles in data analysis and data science.

Module 1: Python for Data Science, AI & Development

Estimated time: 25 hours

• Python programming basics: variables, data types, and control flow
• Working with functions, loops, and data structures
• Introduction to Jupyter Notebooks and REST APIs
• Web scraping fundamentals using Python
• Foundations of Pandas and NumPy for data manipulation

Module 2: Python Project for Data Science

Estimated time: 8 hours

• Data extraction from APIs and web sources
• Data cleaning and preprocessing with Pandas
• Creating interactive visualizations using Plotly
• Building a dashboard with Dash

Module 3: Data Analysis with Python

Estimated time: 16 hours

• Data wrangling and exploratory data analysis
• Handling missing data and outliers
• Feature engineering and data transformation
• Building and evaluating regression models using Scikit-Learn

Module 4: Data Visualization with Python

Estimated time: 20 hours

• Creating static visualizations with Matplotlib and Seaborn
• Geospatial data visualization using Folium
• Designing interactive charts with Plotly
• Developing interactive dashboards using Plotly Dash

Module 5: Machine Learning and Model Evaluation

Estimated time: 10 hours

• Introduction to supervised learning: logistic regression and KNN
• Decision trees and support vector machines (SVM)
• Model selection and evaluation techniques
• Applying ML to classification problems

Module 6: Applied Data Science Capstone

Estimated time: 10 hours

• Perform end-to-end data analysis on real-world data (e.g., SpaceX launch data)
• Apply multiple machine learning models (SVM, logistic regression, decision trees)
• Create interactive dashboards to visualize predictions and insights

Prerequisites

• No prior programming or data science experience required
• Basic computer literacy and internet navigation skills
• Access to a modern web browser and IBM Cloud account (provided)

What You'll Be Able to Do After

• Write Python code for data manipulation and automation
• Clean, analyze, and visualize real-world datasets
• Build interactive dashboards using Plotly and Dash
• Apply machine learning models to classification and regression tasks
• Complete a professional-grade capstone project for your portfolio