The document outlines a comprehensive approach to solving data science problems, focusing on a methodology that starts with business understanding and follows through to model evaluation and feedback. It illustrates the application of this process using examples such as predicting flight delays and classifying cuisines based on ingredients. Key elements include defining project objectives, data preparation, model selection, and iterative evaluation for deployment.

1. How to Approach
Data Science Problems
from Start to End
Polong Lin
Data Scientist
IBM Analytics, Emerging Technologies
@polonglin
@bigdatau
台灣資料科學年會

2. • Free online courses
• Data Science & Data Engineering
• A communityinitiative led by IBM
• Certificates and Badges
• > 450,000 users
What is Big Data University (BDU)?

3. 3

4. 4

5. 5
“5-5-5 Rule”
Course
Lesson 1
Lesson 2
Lesson 3
Lesson 4
Final Exam
Certificate/Badge
Lesson 5
5 videos
5 videos
5 videos
5 videos
5 videos

6. Lab Exercises
6
Learn hands-on. Exercises in the cloud.
DataScientistWorkbench.com

7. 1. Business
Understanding
Data Science Methodology
7. Modelling
6. Data
Preparation
3. Data
Requirements
9. Deployment10. Feedback
Prediction
Interpretation
Justification
Testing
4. Data
Collection
8. Evaluation
5. Data
Understanding
2. Analytic
Approach

8. “Polong will fly from San Francisco to New York
for a meeting at 3:00pm on Friday, July 22.”
Can Polong anticipate whether his flight will be delayed?
Flight delays
8
San Francisco New York

9. • Every project begins with business understanding.
• What is the project objective?
• What are we trying to do – what is our goal?
1. Formulate a clear question
2. Define problem and solution requirements
9
1. Business
Understanding
Flight delays: Create some solution that can help
users predict if a flight on a given day will be
delayed or not delayed
1. Business understanding

10. Using departing & arrival airport, date, carrier, etc.,
we could predict flight [DELAY] or [NO-DELAY] using
logistic regression.
• Identify suitable statistical/machine learning technique(s)
10
2. Analytic
Approach
• Linear regression
• Logistic regression
• Clustering
• Decision Trees
• Principal component
analysis
• Text analysis
• SVM/SVR
• Neural networks
• Dimension
Reduction
2. Analytic approach

11. 11
3. Data
Requirements
4. Data
Collection
5. Data
Understanding
What data is required?
What format?
Collect the data
What does the data look like?
What are initial insights?
Can we visualize the data?
Are missing anything?
• Flight data
• Open data available
• All domestic US flights per year
• CSV format
• Which airports are busiest?
• Which flights are most delayed?
• Which airports are best/worst?

12. Flight Data
12
We will only look at data from 2007 (seven million flights)
http://stat-computing.org/dataexpo/2009/the-data.html
Departure Delay (min)

13. 13
Which airports are busiest?

14. 14
Which flights are most likely be delayed?

15. Data Preparation typically includes:
• Data cleaning
• Merging data
• Transforming data
• Feature engineering
• Text analysis
15
6. Data preparation
6. Data
Preparation
Flights are classified as “delayed” if >15 min late.
• Delayed? [True or False]
Does time of day for departure predict delays?
• Hour

16. 16
Which day of the week and time of departure is worst?

17. 1. Business
Understanding
Data Science Methodology
7. Modelling
6. Data
Preparation
3. Data
Requirements
9. Deployment10. Feedback
Prediction
Interpretation
Justification
Testing
4. Data
Collection
8. Evaluation
5. Data
Understanding
2. Analytic
Approach

18. Modeling is a:
• Highly iterative process
• Multiple models may be used and tested
18
Modelling
Modeling
Using inputs:
• Year
• Month
• Day of Month
• Hour of departure
• Distance
• Destination airport
Predict:
Delay (True/False)
Logistic Regression

19. How well does our model accurately predict
delays?
• Does the model performance meet our business goals?
• Do we need to refine our model?
19
Evaluation
Model evaluation

20. • Once finalized, the model is deployed into a production environment.
• May be in a limited / test environment until model is proven
• Involves additional groups, skills, and technologies
• Solution owner
• Marketing
• Application developers and designers
• IT administration
• Feedback to assess model performance
• Gathering and analysis of feedback for assessment
of the model’s performance and impact
• Iterative process for model refinement and redeployment
• Accelerate through automated processes
20
Deployment
Feedback
Prediction
Interpretation
Justification
Testing
Deployment and feedback

21. 21
Creating a prototype

22. 1. Business
Understanding
Data Science Methodology
7. Modelling
6. Data
Preparation
3. Data
Requirements
9. Deployment10. Feedback
Prediction
Interpretation
Justification
Testing
4. Data
Collection
8. Evaluation
5. Data
Understanding
2. Analytic
Approach

23. Case-study & Demo: Food
Can we use ingredients to predict what cuisine a recipe belongs to?
23

24. What cuisine is this?
2 PM
4 minute
BLT
Beast
24

25. What cuisine is this?
Ingredients:
Rice
Seaweed
Wasabi
Soy sauce
25
http://allrecipes.com/recipe/189477/california-roll-sushi/

26. 26
How are we able to tell what kind
of cuisine some food dish is,
even if we’ve never seen it before?
Schellack at English Wikipedia
https://www.flickr.com/photos/10559879@N00/4004745542

27. A. Based on the ingredients alone, can we predict
what cuisine a food dish belongs to?
B. Which cuisines are similar to each other based
on their ingredients?
27
Business
1. Research
Understanding
Japanese American
British Indian
Chinese
French Italian
Vietnamese Canadian
Food and ingredients

28. 28
Rice?
ALL CUISINES
NON-ASIAN
FOOD
ASIAN FOOD
NO YES
Wasabi?
NO YES
NOT JAPANESE JAPANESE
A. Based on the ingredients alone,
can we predict what cuisine a food dish belongs to?
2. Analytic
Approach
Decision trees

29. B. Which cuisines are similar to each other based on their ingredients?
Analytic
Approach
K-means
Clustering Group similar cuisines together
into k number of clusters.

30. www.allrecipes.com
www.epicurious.com
www.menupan.com
30
Web Scrape
Data
Collection
Data scraped by Yong-Yeol Ahn
http://yongyeol.com/

31. 31
Data
Understanding