Data Science Specialization Course Syllabus

Overview: This comprehensive Data Science Specialization, offered by Johns Hopkins University on Coursera, provides a structured pathway from foundational concepts to advanced applications in data science. The course spans approximately 10 modules, each taking 4–6 weeks to complete with a recommended 6–8 hours of work per week. Learners will gain hands-on experience using R, master data analysis techniques, and complete a capstone project applying real-world data science methods. The program emphasizes reproducibility, ethical practices, and practical skills essential for a career in data science.

Module 1: The Data Scientist's Toolbox

Estimated time: 16 hours

• Introduction to data science and the role of a data scientist
• Overview of key tools: R, RStudio, Git, and GitHub
• Using version control with Git and GitHub
• Introduction to Markdown and R Markdown for documentation

Module 2: R Programming

Estimated time: 16 hours

• Fundamentals of R syntax and data types
• Control structures, functions, and loops in R
• Debugging and profiling R code
• Writing reusable and efficient R functions

Module 3: Getting and Cleaning Data

Estimated time: 16 hours

• Techniques for acquiring data from various sources
• Data tidying and transformation using R
• Handling missing data and outliers
• Working with dates, strings, and regular expressions

Module 4: Exploratory Data Analysis

Estimated time: 16 hours

• Data visualization using base R and ggplot2
• Summarizing data distributions and relationships
• Applying exploratory techniques to uncover patterns
• Best practices in visual representation of data

Module 5: Reproducible Research

Estimated time: 16 hours

• Principles of reproducible research
• Creating dynamic reports with R Markdown and knitr
• Integrating code, text, and results in a single document
• Sharing reproducible analyses via GitHub

Module 6: Statistical Inference

Estimated time: 16 hours

• Foundations of statistical inference and hypothesis testing
• Confidence intervals and p-values
• Resampling methods: bootstrapping and permutation tests
• Application of inference in real data contexts

Module 7: Regression Models

Estimated time: 16 hours

• Linear regression and model fitting in R
• Interpreting regression coefficients and diagnostics
• Model selection and validation techniques
• Assumptions and limitations of regression models

Module 8: Practical Machine Learning

Estimated time: 16 hours

• Introduction to machine learning concepts and workflows
• Supervised learning: classification and regression trees
• Model training, cross-validation, and overfitting
• Evaluating model performance using metrics

Module 9: Developing Data Products

Estimated time: 16 hours

• Building interactive data applications with Shiny
• Creating R packages and APIs
• Deploying data products for public use
• Integrating data visualizations into web applications

Module 10: Data Science Capstone

Estimated time: 24 hours

• Define a real-world data problem and research question
• Collect, clean, analyze, and model data using R
• Create and present an interactive data product

Prerequisites

• Familiarity with basic algebra and statistics
• Basic computer literacy and internet navigation
• No prior programming experience required, but helpful

What You'll Be Able to Do After

• Use R for data manipulation, analysis, and visualization
• Apply statistical inference and regression modeling to real data
• Build and evaluate machine learning models
• Create reproducible research reports using R Markdown
• Develop and deploy interactive data products with Shiny