Course Outline
Part 1: Python Foundations for Analytics (3.5 Hours)
• Module 1: The Analytics Landscape (45 min)
◦ Why Python? A comparative analysis of Python, Excel, and SQL in academic research settings for government.
◦ Setting up for success: An introduction to Jupyter Notebooks and Google Colab. While Google Colab requires a stronger internet connection, it eliminates the need for installation. Participants are encouraged to install Jupyter Notebooks if possible for a more seamless experience for government operations.
• Module 2: The Building Blocks of Data (60 min)
◦ Variables, Data Types (Strings, Integers, Floats), and basic logic concepts for data manipulation in Python.
◦ Understanding Lists and Dictionaries—how Python stores and manages information efficiently for government applications.
• Module 3: Python for Data Analysis Demo & Lab (75 min)
◦ Introduction to Pandas: The industry-standard library for data manipulation, tailored for use in government analytics.
◦ Hands-on activity: Loading a CSV file, filtering data, and calculating basic statistics to enhance data-driven decision-making for government.
Part 2: Introductory Business Analytics (2.0 Hours)
• Module 4: The Analytics Mindset: Understanding the "Ask-Analyze-Act" framework. Techniques for defining clear business questions that can be answered through data analysis for government operations.
• Module 5: Descriptive vs. Predictive: A high-level overview of interpreting trends and identifying anomalies in a financial context, with applications for government fiscal management.
• Module 6: Communicating Insights: Principles of data storytelling—transforming technical findings into actionable recommendations for executive decision-making for government leaders.
Requirements
- A comprehensive understanding of data analytics for government operations.
- Practical experience in data processing within governmental contexts.
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.
