PolyU | Introduction to Data Analytics
This is a foundational undergraduate course offered in Spring 2026 at PolyU that introduces students to the core concepts, methods, and tools of data analytics. The course builds a solid analytical foundation by integrating essential mathematics (linear algebra and calculus) with practical skills in R programming, data manipulation, and data visualization, and progresses to key analytical techniques such as Monte Carlo simulation, linear regression, and time-series analysis. Through a balanced mix of theory and hands-on practice, students learn how to analyze and interpret data systematically, with learning assessed through quizzes, assignments, a midterm test, and a final examination.
Course Overview
📚 Content Summary
This course, DSAI 1201: Introduction to Data Analytics, offered in Spring 2026 at PolyU, provides a foundational understanding of data analytics concepts. It is designed to equip students with the necessary skills to manipulate, analyze, and visualize data using various analytics tools. The curriculum also emphasizes the application of related mathematical operations essential for data science. Assessment is conducted through a combination of quizzes, assignments, a midterm test, and a final exam.
Designed to foster a deep understanding of data analytics concepts, this course guides students through essential mathematics, R programming, and advanced analysis techniques like simulation and regression.
🎯 Learning Objectives
- Understand Analytics Concepts: Grasp the fundamental theories and concepts behind data analytics.
- Data Manipulation & Visualization: Learn how to effectively manipulate, analyze, and visualize data using industry-standard analytics tools.
- Apply Mathematical Operations: Understand and apply the necessary mathematics, including Linear Algebra and Calculus, to data problems.
🔹 Lesson 1: Data Analytics: An Introduction
Overview: The inaugural session providing a broad introduction to the field of Data Analytics. Date: Jan 12
Learning Outcomes:
- Gain a foundational overview of Data Analytics concepts.
🔹 Lesson 2: Introduction to Linear Algebra (Part 1)
Overview: The first part of the mathematical foundation module, focusing on Linear Algebra. Date: Jan 19
Learning Outcomes:
- Begin the study of Linear Algebra concepts essential for data science.
🔹 Lesson 3: Introduction to Linear Algebra (Part 2)
Overview: Continuation of Linear Algebra, deepening the mathematical theoretical framework. Date: Jan 26
Learning Outcomes:
- Complete the second part of the Linear Algebra introduction.
🔹 Lesson 4: Introduction to Calculus (Part 1)
Overview: Introduction to Calculus concepts. This week also includes the first assessment. Date: Feb 2
Learning Outcomes:
- Start the study of Calculus (Part 1).
- Assessment: Complete Quiz (5% of grade).
🔹 Lesson 5: Introduction to Calculus (Part 2)
Overview: Conclusion of the Calculus module before the scheduled break. Date: Feb 9
Learning Outcomes:
- Finalize the introduction to Calculus (Part 2).
- Note: Followed by Lunar New Year Break (Feb 16).
🔹 Lesson 6: In-class Midterm Test
Overview: A dedicated session for evaluating student understanding of the material covered in the first half of the semester. Date: Feb 23
Learning Outcomes:
- Assessment: Complete the In-class Midterm Test (25% of grade).
🔹 Lesson 7: Programming with R (Part 1)
Overview: Transition from theory to practice with an introduction to programming using the R language. Date: Mar 2
Learning Outcomes:
- Begin programming with R (Part 1) for data analysis.
🔹 Lesson 8: Programming with R (Part 2)
Overview: Advanced concepts and continued practice in R programming. Date: Mar 9
Learning Outcomes:
- Advance skills in Programming with R (Part 2).
🔹 Lesson 9: Data Visualization
Overview: Focus on techniques to visually represent data insights. Date: Mar 16
Learning Outcomes:
- Learn and apply Data Visualization techniques.
🔹 Lesson 10: Monte-Carlo Simulation
Overview: Introduction to simulation techniques. The major course assignment is released this week. Date: Mar 23
Learning Outcomes:
- Understand the principles of Monte-Carlo Simulation.
- Assessment: Assignment released (25% of grade).
🔹 Lesson 11: Linear Regression
Overview: Study of predictive modeling using Linear Regression. Date: Mar 30
Learning Outcomes:
- Implement and understand Linear Regression models.
- Note: Followed by The day following Ching Ming Festival (Apr 6).
🔹 Lesson 12: Time-series Analysis
Overview: The final instructional topic covering time-dependent data analysis, coinciding with the assignment deadline. Date: Apr 13
Learning Outcomes:
- Master concepts of Time-series Analysis.
- Assessment: Assignment Due.