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Course Syllabus
Course Syllabus: Programming for Data Science
Based on the official syllabus of the College of Technology, National Economics University (version dated 17 June 2025). The original syllabus is attached at the end of this lesson.
General information
| Item | Details |
|---|---|
| Course | Programming for Data Science |
| Responsible unit | College of Technology – National Economics University |
| Date offered | February 2026 |
| Credits and workload | 1 credit – 30 hours of studying: 20 hours of class contact and 10 hours of independent study |
| Course duration | 4 weeks |
| Pre-requisites | Principles of Programming |
| Website | https://nct.neu.edu.vn/ |
Course Description
This foundational course introduces learners to Python, the most widely used programming language in data science and software development. By the end of this course, learners will be able to:
- Understand and apply core programming constructs: variables, data types, control flow, functions, and object-oriented programming.
- Work proficiently with essential Python libraries such as NumPy and Pandas in Jupyter Notebooks.
- Access and retrieve data from the web using APIs and Python scraping tools such as BeautifulSoup.
Learning Outcomes
Knowledge. Upon successful completion of the course, students will gain a foundational understanding of programming in Python, including core constructs such as variables, data types, control flow, functions, and object-oriented programming. They will understand how Python is used in modern data science and software development, and how it enables efficient data manipulation and analysis. Students will also develop knowledge of essential data science libraries, specifically NumPy and Pandas, and how to utilize Jupyter Notebooks.
Skills. Students will be able to write structured Python programs using both procedural and object-oriented approaches. They will work proficiently with numerical and tabular data using NumPy and Pandas, and perform data processing tasks interactively in Jupyter Notebooks. Furthermore, students will acquire practical skills in accessing web-based data sources through RESTful APIs and conducting basic web scraping using BeautifulSoup.
Course Content and How to Study on the Platform
| Module | Syllabus content | On the platform |
|---|---|---|
| 1. Fundamentals of Python Programming | Introduction to variables, data types, operators, control flow (if statements, loops), functions, and basic object-oriented programming. | 11 short video units of 3–6 minutes, each with in-video questions and a quiz; 4 review lessons; a capstone practice |
| 2. Data Manipulation with NumPy and Pandas | Working with arrays and DataFrames, including data cleaning, transformation, aggregation, and exploratory data analysis using Jupyter Notebooks. | 4 readings with runnable code, each with a quiz; a lab on data cleaning and exploration |
| 3. Accessing and Collecting Web Data | Retrieving data via RESTful APIs, extracting JSON data, and performing basic web scraping using BeautifulSoup. | 2 readings with code, each with a quiz; a data collection lab; an end-of-course knowledge check |
Teaching Method
The course will utilize a blended learning approach that combines the following methods to ensure both theoretical understanding and practical application:
- Face-to-face teaching: Instructor-led lectures and live coding sessions to explain core programming concepts and demonstrate Python tools in action.
- Group discussion: Collaborative activities and peer interaction to explore data science problems and share coding solutions.
- Case-based assignments: Practical exercises and mini-projects based on real-world data science scenarios to reinforce programming and data analysis skills.
- Individual and group presentations: Opportunities for learners to present their solutions, explain code logic, and receive feedback from instructors and peers.
- Self-study: Guided readings, online tutorials, and practice notebooks to deepen understanding and promote independent learning.
Assessment
- Group Project and Presentation (40%)
- Final Exam (60%)
The quizzes, assignments and end-of-course knowledge check on the platform help you practice and assess yourself. They do not replace the two assessment components above.
Required Literature
- McKinney, W. (2022). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter (3rd ed.). O'Reilly Media.
- Mitchell, R. (2018). Web Scraping with Python: Collecting Data from the Modern Web (2nd ed.). O'Reilly Media.