R Programming

What you'll gain

Foundations of R Programming:

• Introduction to R: Familiarize yourself with the R environment, syntax, and basic data types.
• Data Structures: Learn about vectors, matrices, lists, and data frames, and understand how to manipulate them effectively.
• Control Structures: Master using loops, conditional statements, and functions to automate tasks and streamline your code.

2. Data Manipulation and Cleaning:

• Data Import and Export: Explore various methods for importing data into R from different file formats such as CSV, Excel, and databases.
• Data Cleaning: Learn techniques to identify and handle missing values, outliers, and inconsistencies in your datasets.
• Data Transformation: Utilize functions from packages like dplyr and tidyr to reshape, filter, and aggregate data for analysis.

3. Data Visualization:

• Introduction to ggplot2: Learn the grammar of graphics and create customized and visually appealing plots for exploratory data analysis.
• Advanced Plotting Techniques: Explore interactive visualizations, faceting, and customization options to communicate complex data insights effectively.
• Data Visualization Best Practices: Discover principles for designing compelling visualizations and conveying your findings clearly to stakeholders.

4. Statistical Analysis with R:

• Descriptive Statistics: Calculate summary statistics, frequency distributions, and central tendency and dispersion measures.
• Inferential Statistics: Explore hypothesis testing, confidence intervals, and regression analysis to draw meaningful conclusions from data.
• Time Series Analysis: Learn techniques for analyzing and forecasting time series data using packages like forecast and tsibble.

5. Machine Learning with R:

• Introduction to Machine Learning: Understand machine learning algorithms’ fundamentals and their R applications.
• Supervised Learning: Explore regression and classification techniques such as linear regression, logistic regression, decision trees, and random forests.
• Unsupervised Learning: Learn clustering algorithms like K-means and hierarchical clustering for segmentation and pattern recognition.

6. Real-World Applications and Case Studies:

• Practical Projects: Apply your R programming skills to real-world datasets and solve hands-on exercises and projects.
• Case Studies: Analyze case studies from various domains such as finance, healthcare, marketing, and social sciences to gain insights into industry-specific applications of R.