Introduction

This course provides a foundational understanding of programming in R and Python, focusing on data manipulation techniques essential for data science, analytics, and machine learning. Participants will learn core programming concepts, data structures, and essential libraries such as dplyr in R and pandas in Python. Through hands-on exercises, learners will develop skills to clean, transform, and analyze data efficiently.

Prerequisites

1. Basic Understanding of Mathematics and Statistics – Familiarity with basic mathematical concepts is helpful.
2. No Prior Programming Experience Required – Suitable for beginners looking to start with R and Python.
3. Interest in Data Analysis – Ideal for individuals aiming to explore data science and analytics.

Table of Contents

1. Introduction to R and Python Programming

• 1.1 Overview of R and Python in Data Science
• 1.2 Setting Up R and Python Environments (RStudio, Jupyter Notebook)
• 1.3 Writing and Running Basic Code in R and Python
• 1.4 Variables, Data Types, and Operators

2. Data Structures and Control Flow

• 2.1 Lists, Vectors, Matrices, and Data Frames in R(Ref: R Programming for Data Manipulation and Visualisation)
• 2.2 Lists, Tuples, Dictionaries, and DataFrames in Python
• 2.3 Conditional Statements and Looping Constructs
• 2.4 Functions and Code Modularization

3. Data Manipulation with R and Python

• 3.1 Introduction to dplyr (R) and pandas (Python)
• 3.2 Importing and Exporting Data (CSV, Excel, Databases)
• 3.3 Data Cleaning: Handling Missing Values and Duplicates
• 3.4 Data Filtering, Sorting, and Aggregation
• 3.5 Reshaping and Merging Datasets

4. Exploratory Data Analysis (EDA)

• 4.1 Summarizing and Visualizing Data
• 4.2 Generating Descriptive Statistics
• 4.3 Data Distributions and Correlation Analysis
• 4.4 Introduction to Data Visualization with ggplot2 (R) and matplotlib/seaborn (Python)

5. Automating Data Processing Tasks

• 5.1 Writing Reusable Functions for Data Cleaning
• 5.2 Automating Reports and Data Pipelines
• 5.3 Introduction to Web Scraping with rvest (R) and BeautifulSoup (Python)
• 5.4 Working with APIs for Data Retrieval

Conclusion

This course equips participants with essential programming skills in R and Python, enabling them to manipulate, clean, and analyze data efficiently. By the end of the training, learners will be prepared to apply these skills in data science, analytics, and automation workflows. This course serves as a foundation for more advanced topics in data analysis and machine learning.

Reference