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 is convenient as it requires no installation but demands a strong internet connection. Participants are encouraged to install Jupyter Notebooks locally for a smoother experience, if possible.
· 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
- Familiarity with data analytics concepts.
- Experience in 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.
