Course Outline
Part 1: Python Foundations for Analytics (3.5 Hours)
· Module 1: The Analytics Landscape (45 min)
o Why Python? Comparing Python to Excel and SQL in academic research.
o Setting up for success: Introduction to Jupyter Notebooks and Google Colab.
Google Colab will be easier since no installation needed but need stronger internet connection.
If possible, participants can install Jupyter Notebooks for smoother experiences.
· Module 2: The Building Blocks of Data (60 min)
o Variables, Data Types (Strings, Integers, Floats), and basic Logic.
o Understanding Lists and Dictionaries—how Python stores information.
· Module 3: Python for Data Analysis Demo & Lab (75 min)
o Introduction to Pandas: The industry standard for data manipulation.
o Hands-on: Loading a CSV file, filtering data, and calculating basic statistics.
Part 2: Introductory Business Analytics (2.0 Hours)
· Module 4: The Analytics Mindset: Understanding the "Ask-Analyze-Act" framework. How to define business questions that data can answer.
· Module 5: Descriptive vs. Predictive: High-level overview of interpreting trends and spotting anomalies in a financial context.
· Module 6: Communicating Insights: Principles of data storytelling—turning technical output into executive recommendations.
Requirements
- An understanding of data analytics
- Experience with data processing
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
