Sharing at SMU's Business Intelligence and Analytics Club on Data Analytics and how to pick it up.

1. SMU BIA Info-Session:
Data Analytics
SMU Business Intelligence and Analytics
Info-Session 23th Aug 2017

2. SMU BIA Info-Session:
Data Analytics
SMU Business Intelligence and Analytics
Info-Session 23th Aug 2017

3. What is data analytics?

4. Analysis and visualization to provide
insight that help solve problems

5. Machine Learning to solve problems in an
automated and scalable fashion

6. Why data analytics?

7. Make informed bottom-up decisions—no more
guessing

8. Competitive advantage—for yourself and
organizations

9. Data is exponentially produced—demand will
only increase

10. How to learn
data analytics tools?

11. SQL
- w3schools (step-by-step on basic SQL)
- sqlzoo (practice questions on SQL)
Underlined bullets are hyperlinks

12. Python / R
- Interactive programming in Python (Rice)
- Introduction to Computer Science in Python (MIT)
- R programming (Johns Hopkins)
- Introduction to Probability and Data in R (Duke)
Underlined bullets are hyperlinks

13. Spark
- Introduction to Apache Spark (Berkeley)
- Big data analysis with Apache Spark (Berkeley)
- Databricks education materials
Underlined bullets are hyperlinks

14. How to learn
data analytics skills?

15. Probability, statistics, and experiment design
- Inferential Statistics (Duke)
- Statistical Inference (Johns Hopkins)
Underlined bullets are hyperlinks

16. Machine Learning
- Machine Learning (Stanford)
- Statistical Learning (Stanford)
Underlined bullets are hyperlinks

17. Communication
- Written
- Spoken

18. How to practice
data analytics?

19. Start your own projects
- Learn what’s not taught at school
- Add to your portfolio

20. Volunteer with NGOs
- DataKind, an NGO that helps NGOs tackle
problems through data science

21. Write and Speak
- Write a blog on your learning
- Share at meetups

22. What can you
do today?

23. Steps to take
- Get very good at basic SQL
- Get very good at either R or Python
- Understand basic machine learning techniques
- Understand distributed systems and processing
- Improve communication by writing and sharing
- Get experience by doing projects or volunteering

24. How did I get into
data analytics?

25. Studied Psychology and Business at SMU
- Probability, statistics, experimental design
- Written and verbal communication
- SPSS & R

26. Economic and political analysis at MTI
- Written and verbal communication

27. Joined IBM to pursue passion
in working with data

28. First step into data analytics as a data analyst

29. Developed dashboards and analytics for
end-to-end supply chain optimization
- SQL
- Dashboarding (Cognos)

30. Worked on anti-money laundering and entity
resolution system for global bank
- SQL
- Python
- Machine Learning

31. Collected and analyzed tweets to provide
insights for electronics conglomerate
- Python
- Machine Learning (NLP)

32. Was transferred to workforce analytics team,
working on data from IBM’s 450k employees

33. Forecast models for global job demand to
optimize recruitment and workforce allocation
- SQL
- Python
- Machine Learning
- Written and Verbal Communication

34. Job recommender to increase internal transfer,
skill renewal, satisfaction, and reduce attrition
- SQL
- Python
- Machine Learning
- Written and verbal communication

35. Current at Lazada’s Data Science team

36. More information here
- Blog post: How to get started in Data Science
- Slideshare: My Data Science journey and what I do
at Lazada
- Meetup: DataScience SG
Underlined bullets are hyperlinks

37. Thank you!
eugene.yan@lazada.com
eugeneyanziyou@gmail.com