Masters (Masters)
Data Science
2 years
Computing
This course focuses on collecting, analyzing, and interpreting data to discover useful insights and support better decision‑making. It combines skills from mathematics, statistics, computer programming, and critical thinking to understand patterns and solve real‑world problems. Students learn how data is gathered, how to work with modern digital tools, and how data can be used to improve businesses, technology, health, and everyday life.
1. Introduction to Data Science
- What data science is and why it matters
- Real‑world applications (business, health, technology, education)
- Roles of data scientists
2. Data Collection & Exploration
- Types of data (structured and unstructured)
- Data sources (surveys, sensors, web data, databases)
- Basic data cleaning and organization
3. Statistics & Data Analysis
- Descriptive statistics (mean, median, mode, charts)
- Basic probability
- Identifying patterns and trends in data
4. Introduction to Programming for Data Science
- Basic coding skills (Python or R)
- Variables, loops, functions
- Working with data libraries (e.g., pandas, NumPy)
5. Data Visualization
- Creating charts and graphs
- Using tools such as Excel, Power BI, or Python visualization libraries
- Presenting data clearly for decision‑making
6. Databases & Data Management
- Introduction to databases
- Basic SQL queries
- How data is stored and retrieved
7. Machine Learning Basics
- What machine learning is
- Types of machine learning (supervised and unsupervised)
- Simple prediction and classification examples
8. Data Ethics & Privacy
- Responsible data use
- Understanding data protection laws
- Bias, fairness, and transparency in data
9. Real‑World Projects
- Collecting and analyzing data
- Creating visual reports
- Presenting findings in a simple, clear format
10. Emerging Trends in Data Science
- Artificial intelligence
- Big data technologies
- Automation and data‑driven decision‑making