Exercise 1: The rise of analytics
In this session, we split into two groups.
| Exercise part | Time (min) |
|---|---|
| Task 1: Competing on Analytics | 30 |
| Task 2: Jupyter notebook | 30 |
| Discussion | 20 |
| Wrap-up | 5 |
| Overall | 85 |
Task 1: Read Competing on Analytics
Read the Competing on Analytics paper by Davenport (2006). Prepare to discuss the following questions:
- What does it mean for a company to โcompete on analyticsโ?
How is this different from simply using data or reports in decision-making?
- What organizational capabilities are required to compete on analytics?
Consider aspects such as leadership, culture, people, and technology.
- Which companies or industries compete on analytics?
Give examples and explain how analytics creates their competitive advantage.
Task 2: Create an analytical notebook in Jupyter
Navigate to your Codespace in the analytics-and-big-data-notebooks repository and create your first analytical notebook in Jupyter.
1. Select an example case (business problem and dataset)
Choose a dataset from Kaggle, for example:
Define a clear business problem that can be addressed using the selected dataset.
2. Draft the analytical notebook
Create a new notebook and structure it according to the CRISP-DM framework.
Start with a structured skeleton:
- Include both markdown and code cells
- Import a dataset (e.g., from Kaggle)
- Describe planned steps and analytical approaches
- If unsure about specific steps, clearly mark them as
TODO, suggest likely approaches, or outline alternative options
3. Refine the notebook
Review your notebook and annotate each part by indicating whether it is:
- Descriptive (what happened?)
- Predictive (what will happen?)
- Prescriptive (what should be done?)
4. Begin with the implementation
Proceed with initial implementation steps:
- Import the dataset (e.g., CSV file)
- Create a descriptive overview of the data
- Select and implement a basic predictive model
Consult the documentation of relevant Python libraries such as pandas and scikit-learn as needed.
Discussion
In class, we will discuss
- Solutions for Task 1 (Competing on Analytics)
- Solutions for Task 2 (Jupyter Notebooks)
You can share your notebook ๐ด LIVE: in this meeting.
Wrap-up
๐๐ You have completed the first notebook - good work! ๐๐
In this notebook, we have learned to
- Explain what it means to compete on analytics and why it creates competitive advantage
- Set up and use Jupyter notebooks in a Python-based analytics environment
- Structure an analytical workflow following CRISP-DM principles
- Distinguish between descriptive, predictive, and prescriptive analytics
- Begin implementing a basic data analysis workflow in Python
To continue using your work in the next session, stop your Codespace here. Stopping the Codespace preserves the current state of your work and does not consume computational resources.
Before you wrap up, please complete the Session 1 survey here: ?meta:surveys.session_01.url. Thank you ๐