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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.
| 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/ |
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:
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.
| 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 |
The course will utilize a blended learning approach that combines the following methods to ensure both theoretical understanding and practical application:
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.