The document describes a case study using soccer player data to perform data science analysis. It discusses acquiring the dataset, preparing the data by cleaning and selecting features, analyzing the data using statistical exploration, visualization, and clustering techniques in Python and scikit-learn. The analysis formed meaningful player groups and identified attributes that contribute to performance. Insights from the analysis can help coaches design training programs to improve player and team performance.

1. Python
for
Data
Science
Python
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Data Science:
Getting Value out of Data
Python
for
Data
Science
Dr. Ilkay Altintas and Dr. Leo Porter
Twitter: #UCSDpython4DS

2. Python
for
Data
Science
Python
for
Data
Science
By the end of this video, you will be able:
• Describe what modern data science is
• Explain why data science is the key to
getting value out of data and where the
growing interest for it comes from
• List a recommended set of skills for a data
scientist

3. Python
for
Data
Science
Python
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Big
Data
Insight
Action
Data Science

4. Python
for
Data
Science
Python
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Insight Data Product
Data
Question
Analysis
Insight

5. Python
for
Data
Science
Python
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Data
Science
Insight Data Product
Data
Question
Analysis
Insight

6. Python
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Python
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Science
Book Recommendations
Customer
Demographic
Previous
Purchases
Book reviews
What kind of
books does this
customer like?
Book
recommendations

7. Python
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Science
Python
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Data
Science
Model of customer’s
book preferences
Find Potential Audience for a Book
New book
information
Who is likely to like
this book?

8. Python
for
Data
Science
Python
for
Data
Science
Market a New Book
Action to market the
book to the right
audience
Who is likely to like
this book?

9. Python
for
Data
Science
Python
for
Data
Science
Market a New Book
Action to market the
book to the right
audience
Who is likely to like
this book?
Insight Action

10. Python
for
Data
Science
Historical data Near real-time data
Prediction
Actionable Information

11. Python
for
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Python
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Prediction
Action

12. Python
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Python
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Why the Increased Interest in
Data Science?

13. Python
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Python
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Science
BIG DATA COMPUTING AT SCALE
Many dynamic data-driven applications
Smart Manufacturing
Computer-Aided Drug Discovery
Personalized Precision Medicine
Smart Cities
Smart Grid and Energy Management
Disaster Resilience and Response
New era of
data science!

14. Python
for
Data
Science
Python
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Data
Science
How Much Data Is Big Data?

15. Python
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Python
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Science
Image Source: http://www.marketwatch.com/story/one-chart-
shows-everything-that-happens-on-the-internet-in-just-one-
minute-2016-04-26

16. Python
for
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Science
Python
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Data
Science
204 Million emails
Every minute…
200,000 photos
1.8 Million likes
2.78 Million video views
72 hours of video uploads

17. Python
for
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Science
Python
for
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Science
Scientific Data Management and Analysis
• HPWREN: hpwren.ucsd.edu
• 30 TB of data annually
• MODIS: modis.gsfc.nasa.gov
• 219 TB of data annually
• Precision Medicine
• 4 EB (1018 bytes) of data in 2016
(www.fastcompany.com)
• LIGO, Deep Space Network, Protein Data
Bank, …

18. Python
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Science
Python
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Science
100 MBs ~= couple of
volumes of Encyclopedias
A DVD ~= 5 GBs
1 TB ~= 300 hours of
good quality video
LHC ~= 15 PBs a year

19. Python
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Python
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Science

20. Python
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Exponential data growth!

21. Python
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Python
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Science
Data Deluge
“We are drowning in
information and
starving for knowledge”
– John Naisbitt
Source: Megatrends, 1982
Image Source: http://www.digitalzenway.com/2011/12/data-diet-a-resolution-you-can-stick-to/

22. Python
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How do we find the connections?

23. Python
for
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Science
Python
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Data
Science
Modern Data Science Skills
•Programming in Python
•Statistics
•Machine Learning
•Scalable Big Data Analysis

24. Python
for
Data
Science
Python
for
Data
Science
The sum is bigger than the
parts!
Data Science
Big Data Actionable Insight
Modern Data
Science Skills
Python Programming
Machine Learning
Scalable Big Data Analysis
Statistical Analysis

25. Python
for
Data
Science
Python
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Science
The Role of Python
Programming in Data Science
Python
for
Data
Science
Dr. Ilkay Altintas and Dr. Leo Porter
Twitter: #UCSDpython4DS

26. Python
for
Data
Science
Python
for
Data
Science
By the end of this video, you will be able:
•List some of the traits of modern data
scientists
•Explain why Python is a good
programming language for data
science
•Recite four major Python modules
that are useful for data analysis

27. Python
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Python
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28. Python
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Python
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29. Python
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Python
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30. Python
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Are data scientists
unicorns?

31. Python
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Data science is
team sport!

32. Python
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Data
Science
Have passion for data
Data scientists…
Relate problems to analytics
Care about engineering solutions
Exhibit curiosity
Communicate with teammates

33. Python
for
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Science
http://www.kdnuggets.com/2017/01/most-popular-language-machine-learning-data-science.html

34. Python
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Python
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Why Python for Data Science?
• Easy-to-read and learn
• Vibrant community
• Growing and evolving set of libraries
• Data management
• Analytical processing
• Visaualization
• Applicable to each step in the data science process
• Notebooks

35. Python
for
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Python
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What to look forward to!
•Jupyter notebooks
•NumPy
•Pandas
•Matplotlib
•Scikit-Learn
•BeautifulSoup

36. Python
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Science
Python
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Case Study:
Soccer Data Analysis
Python
for
Data
Science
Dr. Ilkay Altintas and Dr. Leo Porter
Twitter: #UCSDpython4DS

37. Python
for
Data
Science
Python
for
Data
Science
By the end of this video, you will be able:
• Talk about the “Big Picture” of data science through a soccer case
study
• Generate statistics about a soccer data set
• Summarize how data cleaning and correlations were applied to an
existing dataset
• Recite the data visualization techniques employed in this study
• Explain how clustering similar groups and plotting these clusters
helped the case study
• Recall what was used to drawing conclusion based on data analysis

38. Python
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Week 1 Case Study: Soccer Data Analysis
• Form meaningful player groups
• Discover other players that are similar to your favorite
athlete
• Form strong teams by using analytics
Dataset location: https://www.kaggle.com/hugomathien/soccer

39. Python
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Understanding the Benefits
• Better understanding and insights on
• player strengths
• enhancing performance
• critical attributes for a player’s performance
• Coach can design programs that improve these
areas in teams
Ask yourself:
“What insights do I expect to get!”
Data
Insight
Action
Data Science
INSIGHTS
ACTIONS

40. Python
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Python
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Basic Steps in a Data Science Project
• Import raw dataset into your analytics platform
• Explore & Visualize
• Perform Data Cleaning
• Feature Selection
• Model Selection
• Analyze the results
• Present your findings
• Use them
ACQUIRE
PREPARE
ANALYZE
REPORT
ACT

41. Python
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Python
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Data Collection from Diverse Sources
• Databases
• Relational
• Non-relational (NoSQL)
• Text files
• CSV files
• Text files
• Live feeds
• Sensors
• Online Platforms
• Twitter
• Live feeds of weather observations

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Data Ingestion to Analytics Platform

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Data Preparation: Explore using Statistics

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Data Cleaning
• Why do we need to clean data?
• Missing entries
• Garbage values
• NULLs
• How do we clean data?
• Remove the entries
• Impute these entries with a counterpart
• Ex. Average values of the column
• Ex. Assign 0, -1, etc

45. Python
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Python
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Data Visualization
A picture is worth a thousand words
Convey more in less space and time
Use Graphs when possible
http://seaborn.pydata.org/examples/

46. Python
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Python
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Analysis
and
Modeling
• Supervised Learning
• Unsupervised Learning
• Semi supervised Learning
Image Source: https://jixta.wordpress.com/2015/07/17/machine-learning-algorithms-mindmap/

47. Python
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Python
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scikit-learn for Machine Learning in Python
http://scikit-learn.org

48. Python
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Soccer Data Analysis: Feature Selection
• What are intrinsic attributes on which ‘you’ would group players ?
• You can also build complex features
Agility
Reaction Time
Shot Power
Sprint Speed
Hair Style
Movies the player likes
f ( shot power, reaction time )

49. Python
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Python
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Clustering in Python: sklearn.cluster
http://scikit-learn.org/stable/modules/clustering.html

50. Python
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Python
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K-Means clustering in Python
from sklearn.cluster import Kmeans
…
Y = KMeans(n_clusters=3,random_state=random_state).fit_predict(X)
…

51. Python
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How to choose the right algorithm?
Image Source: http://4.bp.blogspot.com/-o0vLxYf6YZ4/UQVO9K2jxDI/AAAAAAAACt8/Z5w0bSgqkxw/s1600/machine_learning.png

52. Python
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Interpreting Clustering Results
• How many players per cluster ?
• Too many in few clusters ?
• Too few ?
• Look at distribution of features in each cluster
• Investigate the values for each cluster
• If few clusters à Plot for comparative analysis

53. Python
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Python
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Presenting Data Science Outcomes
Cluster #
Player Attributes
Attribute
Value
(Scaled)

54. Python
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Summary
• Better understanding and insights on
• player strengths
• enhancing performance
• critical attributes for a player’s performance
• Coach can design programs that improve these
areas in teams
INSIGHTS
ACTIONS
ACQUIRE
PREPARE
ANALYZE
REPORT
ACT