Data Science Specialization – By Johns Hopkins University Course

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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