Applied Data Science with Python Specialization – By University of Michigan Course Syllabus

Overview: This specialization is designed for beginners and provides a comprehensive introduction to data science using Python. Over approximately 40-60 hours of content, learners will progress through six modules covering the full data science workflow—from data manipulation and cleaning to visualization, statistical analysis, and machine learning. Each module combines theory with hands-on practice using real-world datasets, culminating in a capstone project that integrates all skills learned. The course is self-paced with lifetime access, ideal for aspiring data professionals seeking practical experience and portfolio development.

Module 1: Introduction to Data Science with Python

Estimated time: 20 hours

• Overview of data science workflow and Python programming
• Introduction to Python basics: data types, variables, and operators
• Control structures: loops and conditionals in Python
• Writing and using functions in Python

Module 2: Data Wrangling & Cleaning

Estimated time: 30 hours

• Introduction to Pandas and NumPy for data manipulation
• Handling missing data and data type conversion
• Data filtering, merging, and reshaping with Pandas
• Preprocessing structured and unstructured data for analysis

Module 3: Data Visualization & Exploratory Data Analysis (EDA)

Estimated time: 40 hours

• Creating static visualizations with Matplotlib
• Building advanced plots using Seaborn
• Performing exploratory data analysis to identify patterns
• Interpreting visual outputs to inform data-driven decisions

Module 4: Statistics & Probability for Data Science

Estimated time: 50 hours

• Descriptive statistics: measures of central tendency and spread
• Inferential statistics and hypothesis testing
• Understanding probability distributions and their applications
• Correlation and regression analysis

Module 5: Machine Learning with Python

Estimated time: 60 hours

• Introduction to machine learning concepts and Scikit-learn
• Implementing classification models (e.g., logistic regression, KNN)
• Building regression models for prediction tasks
• Applying clustering techniques (e.g., K-means) for unsupervised learning

Module 6: Final Project

Estimated time: 40 hours

• Clean and preprocess a real-world dataset using Pandas and NumPy
• Conduct exploratory data analysis and create visualizations
• Build and evaluate a machine learning model using Scikit-learn

Prerequisites

• No prior programming experience required
• Basic understanding of high school level mathematics
• Access to a computer with internet for Jupyter notebooks and course materials

What You'll Be Able to Do After

• Manipulate and clean real-world datasets using Python
• Create insightful data visualizations with Matplotlib and Seaborn
• Apply statistical methods to analyze and interpret data
• Build and evaluate machine learning models for classification and regression
• Complete a portfolio-ready data science project from end to end