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
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
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
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