What you will learn
- Data Manipulation and Analysis with R: Utilize R programming for data cleaning, analysis, and visualization.
- Statistical Inference and Regression Models: Apply statistical methods to draw meaningful insights and build predictive models.
- Machine Learning Techniques: Implement algorithms for predictive analytics and pattern recognition.
- Reproducible Research Practices: Ensure transparency and reliability in data analysis through reproducible workflows.
- Data Product Development: Develop interactive data products and visualizations to communicate findings effectively.
- Version Control with GitHub: Manage and document projects using Git and GitHub.
Program Overview
The Data Scientist’s Toolbox
⏱️2-3 weeks
- Learn about the data science field, including tools like R, RStudio, Git, and GitHub.
- Understand how to structure a data science project and practice version control.
- Set up your environment for data analysis.
R Programming
⏱️3-4 weeks
- Dive deep into R programming basics and data structures.
- Learn looping, functions, and debugging.
- Practice writing efficient R code for data analysis.
Getting and Cleaning Data
⏱️2-3 weeks
- Learn how to collect, clean, and preprocess data for analysis.
- Work with APIs, web scraping, and data reshaping tools.
- Handle missing values and standardize data formats.
Exploratory Data Analysis
⏱️2-3 weeks
- Visualize and summarize data with base R and ggplot2.
- Understand distributions, trends, and relationships in datasets.
- Identify outliers and patterns through graphical analysis.
Reproducible Research
⏱️1-2 weeks
- Learn how to create fully reproducible analytical reports using R Markdown.
- Understand documentation practices and reproducibility in research.
- Integrate code, visualizations, and narratives into a single document.
Statistical Inference
⏱️2-3 weeks
- Learn about probability theory, sampling, and hypothesis testing.
- Understand confidence intervals and p-values.
- Use simulations to validate statistical models.
Practical Machine Learning
⏱️2-3 weeks
- Understand machine learning algorithms and model evaluation.
- Practice classification, clustering, and prediction tasks.
- Learn how to split data into training and testing sets and tune model parameters.
Developing Data Products
⏱️2-3 weeks
- Build interactive web apps using Shiny.
- Create dynamic reports and visual dashboards.
- Learn product design concepts and user interaction fundamentals.
Data Science Capstone
⏱️4-6 weeks
- Apply all your learned skills to a real-world data science project.
- Solve a business or research problem using publicly available data.
- Present your findings in a reproducible, professional format.
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Job Outlook
- Data science roles are projected to grow by 36% through 2031, far above the average (U.S. Bureau of Labor Statistics).
- Entry-level data scientists earn between $85,000 – $110,000, with senior roles reaching $150,000+.
- Demand spans industries like tech, healthcare, finance, e-commerce, and government.
- Employers seek proficiency in R, statistics, machine learning, and data visualization.
- The specialization provides a strong foundation for roles such as Data Analyst, Data Scientist, and Research Analyst.
Specification: Data Science Specialization – By Johns Hopkins University
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