Programming in Python and R

Course details

This course introduces students to two of the most widely-used programming languages in the data science domain: Python and R. By mastering these languages, students will be well-equipped to handle, analyze, and visualize vast datasets, and further employ advanced machine learning algorithms.

Start Date

TBD

Duration

14-15 weeks

Prerequisites

Basic understanding of programming concepts and familiarity with mathematical operations.

Learning Objectives

Grasp the core programming constructs of Python and R.
Implement data handling, cleaning, and visualization techniques.
Dive deep into data science libraries like pandas, numpy (Python) and dplyr, ggplot2 (R).
Create robust data-driven applications and analytical tools.
Understand the strengths and use-cases for each language in a data science context.

Key Features

Weekly hands-on coding sessions.
Projects based on real-world data challenges.
Continuous evaluation via coding challenges and quizzes.
Exploration of both Python and R ecosystems.
Expert tips on optimizing and deploying code.

Learning path

Unit 1: Introduction to Python and R (2)
Unit 2: Core Programming Concepts (6)
Unit 3: Data Handling and Manipulation (6)
Unit 4: Data Visualization (5)
Unit 5: Advanced Programming Concepts (5)
Unit 6: Data Science Libraries and Tools (4)

Topics covered

Overview of Python and R: History and Significance
Setting up the Development Environment

Variables, Data Types, and Operators (Python)
Control Structures: If-else, Loops (Python)
Functions and Libraries (Python)
Variables, Data Types, and Operators (R)
Control Structures: If-else, Loops (R)
Functions and Packages (R)

Lists, Tuples, and Dictionaries (Python)
Dataframes and Series using pandas (Python)
Data Cleaning and Transformation (Python)
Vectors, Lists, and Dataframes (R)
Data Manipulation using dplyr (R)
Data Cleaning and Transformation (R)

Introduction to matplotlib and seaborn (Python)
Plotting Graphs and Charts (Python)
Introduction to ggplot2 (R)
Visualization Techniques (R)
Advanced Visualization: Interactive plots and Dashboards

Object-Oriented Programming in Python
Error Handling and Debugging (Python)
R's Environment and Functional Programming
Error Handling in R
Integrating Python and R for Projects

Advanced Python Libraries: numpy, scipy
Statistical Analysis in Python
Advanced R Libraries: tidyr, lubridate
Time-Series Analysis in R

Grading:

Class Participation: 10%
Bi-weekly Coding Assignments: 30%
Group Programming Project: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Reading Material: A blend of traditional textbooks on Python and R, complemented by online resources, tutorials, and research papers. Exclusive access to a digital repository on the LMS.

Assignments: Varied tasks that challenge the students to apply their programming knowledge to solve real-world data problems.

Faculty: An industry veteran with substantial experience in Python, R, and data analytics

Teaching Instructors: TBD