Guidelines for Identifying and Selecting Your Data Analytics Project

Applied Data-Driven Solutions Course

1. Purpose of the Project

The goal of your project is to solve a real-world problem using data analytics — from defining the problem to delivering insights or predictive models that could help stakeholders make better decisions.
Your project should demonstrate:

- Clear problem understanding
- Appropriate data selection and preparation
- Correct application of analytical or machine learning methods
- Meaningful insights and interpretation

2. Steps to Identify and Select a Project

Step 1: Identify a Problem of Interest

Choose a topic that:

- Interests you personally (you’ll work on it for weeks)
- Has available, relevant data
- Can be completed within the course timeline

Ask yourself:

- Who are the stakeholders?
- What decision/problem will this analysis help address?
- Is the problem descriptive, diagnostic, predictive, or prescriptive?

Step 2: Search for Potential Datasets

You can find project datasets from:

- Kaggle Competitions & Datasets (https://www.kaggle.com/competitions)
- UCI Machine Learning Repository (https://archive.ics.uci.edu)
- OpenML (https://www.openml.org)
- Hugging Face Datasets (https://huggingface.co/datasets)
- Government open data portals (https://data.gov, https://data.europa.eu)
- Domain-specific repositories (see examples in Section 5)

Step 3: Check Feasibility

Before committing, ensure:

- Data is accessible and well-structured (or can be cleaned within time limits)
- Labels exist for supervised learning problems
- There is enough variety for feature engineering
- The scope fits your skills and the course timeframe

Step 4: Define Clear Goals and Success Metrics

Examples:

- Predict whether a customer will churn → Metric: F1-score, Precision, Recall
- Forecast daily energy usage → Metric: Mean Absolute Error (MAE)
- Classify product reviews → Metric: Accuracy, Macro-F1

Step 5: Get Inspiration from Kaggle Competitions

Joining a Kaggle competition is a great way to:

- Work with a real dataset
- Benchmark your solution against others
- Learn from public notebooks

Examples:

- House Prices: Advanced Regression Techniques – Predict real estate prices
- Titanic: Machine Learning from Disaster – Binary classification (survival prediction)
- Retail Product Demand Forecasting – Time series forecasting
- RSNA Pneumonia Detection Challenge – Medical image classification
- Predict Future Sales – Sales forecasting from time series data

Step 6: Narrow Down to One Project

Select a project based on:

- Interest – Will keep you motivated
- Impact – Useful to a stakeholder or community
- Feasibility – Can be done with your current skills in the available time
- Data Quality – Enough records, few missing values, relevant variables
- Learning Value – Gives you the chance to apply course techniques

3. Example Project Ideas by Domain

Domain | Example Project | Possible Dataset Source
Healthcare | Predict hospital readmission within 30 days | MIMIC-IV (PhysioNet)
Finance | Credit card fraud detection | Kaggle: Credit Card Fraud Dataset
Retail | Sales forecasting for products | Kaggle: Predict Future Sales
Marketing | Customer churn prediction | Kaggle: Telco Customer Churn
IT Operations | Predict long-resolution IT tickets | ServiceNow (synthetic), Kaggle IT ticket data
Education | Predict student dropout risk | Open University Learning Analytics Dataset
Transportation | Predict taxi demand in NYC | NYC Open Data, Kaggle Taxi Data
Sports Analytics | Predict NBA game outcomes | Kaggle NBA Stats datasets
Environmental | Air quality forecasting | UCI Air Quality Dataset, OpenAQ

4. Dataset Repository References

- Kaggle Datasets – https://www.kaggle.com/datasets
- UCI Machine Learning Repository – https://archive.ics.uci.edu
- OpenML – https://www.openml.org
- Hugging Face Datasets – https://huggingface.co/datasets
- Data.gov – https://www.data.gov
- NYC Open Data – https://opendata.cityofnewyork.us
- Our World in Data – https://ourworldindata.org
- Google Dataset Search – https://datasetsearch.research.google.com

5. Deliverables for the Selection Phase

By the end of Week 2 of the course, you should submit:

1. Project Title
2. Problem Statement (max 150 words)
3. Dataset Description (source, number of records, variables)
4. Planned Analytical Approach (descriptive, predictive, etc.)
5. Success Metric
6. Why You Chose This Project (interest, feasibility, impact)

Project identification and selection template: Project_selection_template.docx