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
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
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
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 [email protected] Corporate Lab, Singapore September 2014 - August 2016 Data Engineer, Data Analytics Strategic Technology Centre ST Engineering, Singapore October 2018 - January 2021 @ongchinhwee
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
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
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
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
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
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
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)
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
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
Getting into the Data Industry ● Getting past the interviews ○ Behavioural interviews ■ Tell your story using STAR method ● Situation ● Task ● Action ● Response @ongchinhwee
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
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
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
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
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
ongchinhwee
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