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 Duration 7 hours

Course Outline

Part 1: Python Basics for Analytics (3.5 Hours)

·       Module 1: The Analytics Environment (45 min)

o   The case for Python: A comparison of Python, Excel, and SQL in academic research.

o   Preparation for success: Overview of Jupyter Notebooks and Google Colab.
Google Colab offers convenience since no local installation is required, though it demands a stable internet connection.
Where feasible, participants may install Jupyter Notebooks for a smoother workflow.

·       Module 2: Core Data Concepts (60 min)

o   Variables, data types (Strings, Integers, Floats), and fundamental logic.

o   Exploring Lists and Dictionaries—methods Python uses to organize information.

·       Module 3: Python for Data Analysis Demonstration & Lab (75 min)

o   Introduction to Pandas: The industry-leading tool for data manipulation.

o   Practical session: Reading a CSV file, applying filters, and computing basic statistics.

Part 2: Introduction to Business Analytics (2.0 Hours)

·       Module 4: The Analytical Approach: Grasping the "Ask-Analyze-Act" model. Learning to frame business questions that data can resolve.

·       Module 5: Descriptive vs. Predictive: A high-level look at interpreting trends and identifying anomalies in financial settings.

·       Module 6: Conveying Insights: Best practices in data storytelling—transforming technical results into executive-ready recommendations.

Requirements

  • A foundational understanding of data analytics
  • Prior experience with data processing

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