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Main
UQU Programs

Data Analysis and Visualization

AI3301
Two hours English

Data Analysis and Visualization

Data Analysis and Visualization
1 Introduction to Data Analysis
2 Data Preparation and Cleaning
3 Exploratory Data Analysis (EDA) (Summarization techniques, Identifying trends and patterns)
4 Data Modeling and Statistical Analysis (Inferential statistics, Regression analysis)
5 Data Visualization Principles
6 Visualization Tools (Tableau, R)
7 Advanced Visualizations (Dynamic and real-time, Network, and time-series visualization)
1.1 Mapped to: K1, K2

Understand and demonstrate the core principles of data structures and types, and how data cleaning, transformation, and analysis in real-world applications.

Teaching Strategy Lectures, discussions
Assessment Methods Quizzes, Practical assignments, Exams
1.2 Mapped to: K1, K3

Understand statistical analysis techniques for data analysis and interpretation to extract meaningful insights.

Teaching Strategy Lectures, discussions
Assessment Methods Quizzes, Practical assignments, Exams
1.3 Mapped to: K1

Understand the importance of data visualization and the role of effective representation of complex data.

Teaching Strategy Lectures, discussions
Assessment Methods Quizzes, Practical assignments, Exams
2.1 Mapped to: S1, S2

Develop programming and software development skills to manipulate and process various data types using programming languages (e.g., Python, R)

Teaching Strategy Hands-on Programming, Exercises Project-Based Learning
Assessment Methods Practical assignments, project
2.2 Mapped to: S1

Design, implement, and evaluate statistical algorithms and methods to extract insights from data.

Teaching Strategy Hands-on Programming Exercises Project-Based Learning
Assessment Methods Practical assignments, project
2.3 Mapped to: S4

Communicate data insights clearly and effectively using data visualization tools (e.g., Tableau, R).

Teaching Strategy Hands-on Programming Exercises Project-Based Learning
Assessment Methods Practical assignments, project
3.1 Mapped to: V2

Collaborate with others to design visualizations that effectively communicate insights and drive decision-making.

Teaching Strategy Project-Based Learning
Assessment Methods Project
Assessment
Weight
Quizzes
10%
Practical assignments: tasks designed to help students apply theoretical knowledge to real-world data problems and provide hands-on experience. Using programming Libraries (e.g. Python, R) and visualization software (e.g. Tableau)
20%
Group Project: The project acts as a capstone assessment where students integrate theoretical concepts with practical implementation to address real-world challenges using data analysis and visualization techniques. Students collaborate in teams, enhancing their teamwork, critical thinking, and communication skills throughout the process. To ensure steady progress, the project is divided into distinct phases. Students are expected to uncover meaningful trends, create effective visualizations, and provide actionable insights based on their findings.
25%
Midterm exam
15%
Final Exam
30%
Total
100%