PyData Global 2021 Impact Program: How to Get Started with Your Data Science Career

How to Get Started with Your Data Science Career (a highly opinionated perspective)
Event: PyData Global 2021 Impact Program
Date: 28 October 2021
Location: Online

1. How to Get Started with Your Data Science Career (a highly opinionated perspective) By: Chin Hwee Ong (@ongchinhwee) 28 October 2021 PyData Global Impact Program
2. Hello, my name is Chin Hwee! • Currently: Data Engineer, Digital Value Services (DVS) @ DT One • Education: ◦ B.Eng.(Hons.) in Aerospace Engineering with Minor in Business ◦ M.Sc. in Mechanical Engineering (Specialization in Computation and Modelling), National University of Singapore ◦ MBA (Honors) from Quantic School of Business and Technology • International speaker at technology conferences (FOSDEM, EuroPython, PyData Global, Open Up Global Summit etc.) • “Building a Better World with Technology” @ongchinhwee
3. A Snapshot of My Career History (or How I Somehow Ended Up in Data) @ongchinhwee
4. What My Career History Looks Like Data Engineer, Digital Value Services (DVS) DT One, Singapore January 2021 - Present Repair Engineering Intern Liebherr Aerospace, Singapore January 2012 - May 2012 Research Assistant Rolls-Royce@NTU Corporate Lab, Singapore September 2014 - August 2016 Data Engineer, Data Analytics Strategic Technology Centre ST Engineering, Singapore October 2018 - January 2021 @ongchinhwee
5. What My Career Journey Actually Is Moving to a Tech Company My involvement in the tech community (indirectly) got me the (strategic) move January 2021 - Year 3 Internship Didn’t even get an interview for my top choices and had to settle for this January 2012 - May 2012 First Job out of Graduation Got a referral to work on aviation research while figuring out my future plans for further studies September 2014 - August 2016 First Job in Data An indirect referral from a fellow TechLaunch alumni helped - and I somehow got involved in Data Engineering at an Innovation Centre October 2018 - January 2021 School-Work Transition Struggle between financial constraints and further studies June 2013 - September 2014 Major Burnout + Re-Pivot January - December 2016 Pursuing an M.Sc. Re-learnt coding and started on my (first) Masters; did TechLaunch + a CFD research project that involves using OpenFOAM / C++ January 2017 - May 2018 Out of Comfort Zone Started attending tech networking events; started self-learning Python May 2018 - September 2018 Pursuing an MBA January 2019 - November 2019 Conference Speaking August 2019 - Present @ongchinhwee
6. How I “stumbled” into Data • A Masters coursework on Neural Networks got me hooked into machine learning and deep learning • Stretched my computational skills with a self-sourced Masters project • Taught myself Python + updated my GitHub account (with code samples) • Regularly attended tech events to network with tech professionals and learn more about the data industry • Secured a few referrals → interview opportunities → my first job in Data! @ongchinhwee
7. “[Growth] is something that isn’t linear. It needs to be something that’s a little bit more lateral.” Deepak Shukla, CEO Pearl Lemon @ongchinhwee
8. How to Get Started with Your Data Science Career (more commonly known as “How do I become a Data Scientist”) @ongchinhwee
9. Start simple. @ongchinhwee Source: Twitter thread by Eugene Yan, Applied Scientist at Amazon
10. Start simple. • Build up your fundamentals first ◦ Technical skills 1. Linear Algebra, Statistics, Calculus 2. Programming (Python, R etc.) 3. Machine Learning @ongchinhwee
11. Start simple. • Build up your fundamentals first ◦ Technical skills 1. Linear Algebra, Statistics, Calculus 2. Programming (Python, R, SQL etc.) 3. Machine Learning ◦ Non-technical skills ▪ Communication Skills ▪ Storytelling @ongchinhwee
12. Getting into the Data Industry • Stiff competition for “entry-level” roles ◦ Increased supply of entry-level data science talent ▪ Data science / business analytics programs ▪ Remote work @ongchinhwee
13. Getting into the Data Industry • Stiff competition for “entry-level” roles ◦ Increased supply of entry-level data science talent ▪ Data science / business analytics programs ▪ Remote work ◦ How to stand out? ▪ Networking • Attend meetups and conferences • Connect with people in the data community (LinkedIn/Twitter) @ongchinhwee
14. Getting into the Data Industry • “You need experience to get experience” ◦ How to show experience? ▪ Relevant work projects (internships, work rotation etc.) ▪ A good portfolio that documents your unique learning journey @ongchinhwee
15. Getting into the Data Industry • “You need experience to get experience” ◦ How to show experience? ▪ Relevant work projects (internships, work rotation etc.) ▪ A good portfolio that documents your unique learning journey • What topics are you passionate about? • What are your key objectives and takeaways? • Possible ideas: blogs, GitHub projects, recorded talks @ongchinhwee
16. Getting into the Data Industry @ongchinhwee • “Data Scientist” is not the only role in the Data industry! ◦ Example of Data roles: ▪ Data Analyst ▪ Data Engineer ▪ Machine Learning Engineer ▪ Quantitative Developer Source: Twitter (@EvidenceNMedia)
17. Getting into the Data Industry • Figuring out your Unique Selling Proposition ◦ Career switch ≠ starting from scratch ◦ Personal SWOT analysis ◦ Domain expertise - use it to your advantage! @ongchinhwee Strengths Weaknesses Opportunities Threats Internal External Helpful Harmful
18. Getting into the Data Industry • Getting past the interviews ◦ Technical interviews ▪ Case studies, coding interviews, take-home assignments etc. ▪ How to prepare? • Data science projects • Leetcode (Easy/Medium) • Treat every interview as great practice for the next one! @ongchinhwee
19. Getting into the Data Industry • Getting past the interviews ◦ Behavioural interviews ▪ Tell your story using STAR method • Situation • Task • Action • Response @ongchinhwee
20. Possible Routes to a Data Science Career @ongchinhwee Masters/PhD in a quantitative field Data Science bootcamps / MOOCs Internal application / transfer to a Data team • Build up strong fundamentals in applied math and coding over 1-3 years • Able to learn and understand data concepts more quickly • Build up skills/portfolio within a short time through capstone projects • Very strong prior technical background needed to succeed • Learn and apply data skills to solve business problems within the company • Easier to transition with proven track record of delivering value
21. How to Succeed in Your Data Science Career • Focus on creating positive impact on the business ◦ What problem are you trying to solve? ▪ Designing a business case based on “pain points” ▪ Using tools to design a practical solution ▪ Pitching your solution with a compelling story • Tolerance for “failure” as an inevitable cost @ongchinhwee
22. More Resources • Programming in Python ◦ Udacity Intro to Python Programming ◦ MITx 6.00.1x Introduction to Computer Science and Programming Using Python ◦ “Python Cookbook” by David Beazley and Brian K. Jones ◦ “Fluent Python” by Luciano Ramalho @ongchinhwee
23. More Resources • Statistics and Machine Learning ◦ “Python Data Science Handbook” by Jake VanderPlas ◦ “Data Science from Scratch” by Joel Grus ◦ “The Elements of Statistical Learning” by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie @ongchinhwee
24. More Resources • Miscellaneous ◦ “Designing Data-Intensive Applications” by Martin Kleppmann ◦ SQLBolt.com (for learning SQL) ◦ Data Science podcasts ▪ Towards Data Science ▪ Symbolic Connection • I’m featured on Episodes 13 - 14 @ongchinhwee
25. More questions? Reach out to me! : ongchinhwee : @ongchinhwee : hweecat : https://ongchinhwee.me