Data Science Methodology

Business and Data Understanding:

1. What is the problem that you are trying to solve?
2. How can you use data to answer the question?
3. What would you do if you had all the data?

Collection of data:

1. What data do you need to answer the question?
2. Where is the data coming from and how will you get it?
3. Which data is relevant?
4. Is the data that you collected representative of the problem to be solved?
5. Are there any privacy issues?

Exploring the data:

1. How does the data look like?
2. Are there any patterns?
3. What additional work is required to manipulate and work with the data?

Analyzing the data and Deriving the answer:

1. How to best build a model?
2. Do the results make sense?
3. How well does the model fit?
4. How can the model be validated?
5. Does the model used really answer the question or does it need to be adjusted?
6. Can the model be put into practice?

Storytelling: Visualization and Communication

1. In what way can the data be visualised to get the answers required?
2. How can you tell the story?
3. Can constructive feedback into answering the question be obtained?

Taking Actions

1. Making decisions

Data Sets

Recommender System

Graph

Reading Resources