| Pre-Requisites | DA110, DA111 |
| Co-Requisites | None |
| Instructional Hours | 40 |
| Instructional Mode | Lecture |
| Delivery Mode | In-Person / Blended / Online |
This course, Introduction to Data Analysis with Python (DA210), will introduce students to Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. Python is a popular programming language for data analysis due to its simplicity and the powerful libraries available for data manipulation and analysis. In this course, students will learn how to use these libraries to extract insights from complex datasets through a programmatic approach.
By the end of this course, students will be able to:
The course content will be presented through a series of lectures and hands-on exercises. Students will be evaluated through assignments and a final project.
Throughout the semester, students will be given assignments that will require them to perform data analysis tasks using Python libraries such as NumPy and Pandas. These assignments will allow students to practice their data analysis skills and will count towards the overall course grade.
At the end of the semester, students will complete a final project where they will have to perform a comprehensive data analysis using Python. This project will serve as a culmination of the skills learned throughout the course and will count towards a significant portion of the overall course grade.
The following is a general outline of the topics covered in the course:
| Week | Topic |
|---|---|
| 1 | Introduction to Data Analysis with Python |
| 2 | Introduction to NumPy for Data Manipulation |
| 3 | Data Wrangling with Pandas |
| 4 | Data Visualization with Matplotlib |
| 5 | Exploratory Data Analysis |
| 6 | Data Cleaning and Preprocessing |
| 7 | Statistical Analysis with Python |
| 8 | Processing Natural Language Text Data |
| 9 | Advanced Data Visualization Techniques |
| 10 | Big Data Analysis with Python |
| 11 | Case Studies in Data Analysis |
| 12 | Final Project Presentations and Course Reflection |