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
Testimonials (3)
Practice in using python is really helpful to understand the basics.
Wildana Ramadhani - OJK
Course - Foundations of Data & Business Analytics
Good presentation and expert trainers
Khairunnisa Andira - OJK
Course - Foundations of Data & Business Analytics
The technical details