The document discusses the role of data science and analytics in various domains, emphasizing the integration of big data, sensors, and machine learning. It outlines challenges in processing and analyzing large datasets, such as those required for predictive policing and disaster recovery. Key skills for data scientists and the importance of effective communication within data science teams are also highlighted.

1. Chula Data Science
Center of Excellence in Multi-Disciplinary
Big Data Analytics

2. Big Data and Data Science
Learning Path
Digital Transformation #แบ่งปัน
Head of Department
Dept. of Computer Engineering
Faculty of Engineering
Chulalongkorn University
natawut.n@chula.ac.th
@natawutn
http://natawutn.wordpress.com
http://www.slideshare.net/natawutnupairoj
Asst. Prof. Natawut Nupairoj, Ph.D.

4. Data Science = Sensors + Big Data + Data Analytics
The New Equation

5. Data Analytics Simplified
Descriptive
• “A.Natawut drinks about 1 cup of coffee a
day”
Diagnostic
• “Number of cups that A.Natawut drinks
depend on number of meetings he has each
day”
Predictive
• “Tomorrow, A.Natawut has 2 meetings, it is
very likely that A.Natawut will drink 2 cups
tomorrow”
Prescriptive
• “Inform secretary to prepare 1 cup in the
morning and one in the afternoon for
A.Natawut”

7. Sensors = App / IoT / Social Network
Big Data = Processing Capabilities
Data Analytics = Domain-Oriented Machine Learning

8. Introducing FDA-Approved
Ingestible Sensors in Pills
http://www.forbes.com/sites/singularity/2012/08/09/no-more-skipping-your-medicine-fda-approves-first-digital-pill/

10. Case study: Predictive Policing
Being used by 60 cities in the US e.g. Atlanta, LA, etc.
Source: http://www.forbes.com/sites/ellenhuet/2015/02/11/predpol-predictive-policing

11. NHK Documentary: Disaster Big Data - Key to recovery

12. Key Question
“How many people are still resided in each area?”

13. Challenges
• How to process big data?
• 122M subscribers + 2.5 years of data = 200TB-300TB
• How to analyze data?
• What is the definition of being “residence”?
• How to sampling mobile subscribers correctly?
• How can we understand the results?
• How to visualize data?
• How to tell story?

14. “Data Science is a Team Sport” – DJ Patil
Domain
Knowledge
Math &
Statistics
Computer
Science
Data Scientist
Statistical ResearchData Processing
Machine Learning

15. Data Scientist Skills in the Context of
NHK Documentary
Domain
Knowledge
Math &
Statistics
Computer
Science
Statistical ResearchData Processing
Machine Learning
• How to store 300TB of data?
• How to process 300TB
effectively?
• How about Data Cleansing?
• How to visualize data?
• How to sample data correctly?
• How to turn geolocation into
structured data?
• How to predict population
accurately?
• How to define “residence”?
• How to classify local people
from workers?
• How to utilize these results?

18. Modern Data Science Team
Source: http://www.slideshare.net/continuumio/why-open-data-science-matters-
gartner-bi-analytics-summit-16
Understanding / Preparation / Modeling / Evaluation
Deployment

19. http://nirvacana.com/thoughts/becoming-a-data-scientist/

20. Most In-Demand Skills for Data
Scientist in 2016
Source: https://www.crowdflower.com/what-skills-should-data-scientists-have-in-2016/

21. Final Thoughts
• A Good Data Scientist Communicates Effectively To
Business Users
• A Good Data Scientist Knows Your Business
• A Good Data Scientist Understands Statistical
Phenomena
• A Good Data Scientist Makes Efficient Predictions
• A Good Data Scientist Provides Production-Ready
Solutions
• A Good Data Scientist Can Work On A Mass Scale
https://blog.dataiku.com/2013/11/10/the-six-core-skills-of-a-data-scientist

22. Chula Data Science
Center of Excellence in Multi-Disciplinary
Big Data Analytics