R and Python Basics Curriculum (30 Hours)

Module 1: Introduction to Programming with R and Python (3 hours)

  • Topics Covered:
    • Overview of R and Python
    • Setting Up R and RStudio
    • Setting Up Python and IDEs (Anaconda, Jupyter Notebook)
    • Basic Syntax and Data Types in R and Python

Module 2: Data Structures in R and Python (4 hours)

  • Topics Covered:
    • Vectors, Lists, Matrices, and Data Frames in R
    • Lists, Tuples, Dictionaries, and DataFrames in Python
    • Manipulating Data Structures
    • Indexing and Slicing

Module 3: Control Structures (3 hours)

  • Topics Covered:
    • Conditional Statements (if, else, elif in Python; if, else in R)
    • Loops (for, while)
    • Loop Control (break, next, continue)
    • Applying Control Structures in Data Manipulation

Module 4: Functions and Modules (4 hours)

  • Topics Covered:
    • Writing Functions in R and Python
    • Function Arguments and Return Values
    • Importing and Using Libraries/Packages (tidyverse in R; pandas, numpy in Python)
    • Writing and Using Modules

Module 5: Data Input and Output (3 hours)

  • Topics Covered:
    • Reading and Writing Data Files (CSV, Excel, JSON)
    • Web Scraping Basics
    • Accessing Databases
    • Data Input/Output in R and Python

Module 6: Data Cleaning and Manipulation (5 hours)

  • Topics Covered:
    • Data Cleaning Techniques
    • Handling Missing Values
    • Data Transformation (reshaping, merging, and concatenating data)
    • Data Manipulation with dplyr in R and pandas in Python

Module 7: Data Visualization (4 hours)

  • Topics Covered:
    • Introduction to Data Visualization
    • Creating Basic Plots (scatter, line, bar, histogram)
    • Advanced Visualization Techniques
    • Data Visualization with ggplot2 in R and matplotlib/seaborn in Python

Module 8: Basic Statistical Analysis (4 hours)

  • Topics Covered:
    • Descriptive Statistics
    • Probability Distributions
    • Hypothesis Testing
    • Statistical Analysis in R and Python

Capstone Project 1: Exploratory Data Analysis with R (2 hours)

  • Objective:
    • Perform exploratory data analysis on a given dataset.
    • Clean and preprocess the data.
    • Visualize key insights and summary statistics.
  • Skills Applied:
    • Data Cleaning and Manipulation
    • Data Visualization
    • Descriptive Statistics

Capstone Project 2: Data Analysis and Visualization with Python (2 hours)

  • Objective:
    • Analyze a dataset to extract meaningful insights.
    • Perform data cleaning and transformation.
    • Create visualizations to present the findings.

Skills Applied:

  • Data Cleaning and Manipulation
  • Data Visualization
  • Basic Statistical Analysis