How To Become a successful Data Scientist
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It’s been 15 years since the term data scientist has become one of the most sought-after professions. Nevertheless, if you ask a lot of data scientists what their profession is you will get very different answers, which will mostly depend on the kinds of companies they work for. Talk given at CodeTalks 2023 in Hamburg. So how does one learn Data Science when the definition of the field is open to interpretation? Or, when putting together all the job descriptions, in order to become a Data Scientist one would need to know all the theory, and new approaches and be able to use hundreds of tools. In this talk, we will explore the fundamentals needed to be a data scientist, from the perspective of theory, tooling, and approaches. We will talk about some of the common misconceptions people starting to learn data science have. And about some of the reframing that I have seen successful learners have done on their path to data science. And what role do large language models play in the future of learning data science?
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How To Become a successful Data Scientist
- 1. TEREZA IOFCIU How to successfully become a Data Scientist Data Analytics & Science
- 2. HELPWANTED -SURVEYTIME WHICH SKILLS DID YOU NOTICE IN DATA PEOPLE YOU ENJOYED WORKING WITH?
- 3. AN “OFFICIAL" PROFESSION SINCE 2008 WHATISADATASCIENTIST? DJ Patil and Je ff Hammerbacher of LinkedIn and Facebook made "Data Scientist" an o ff icial buzzword. They were looking for a job title that didn’t sound too Wall Street (Data Analyst) nor too academic (researcher). HTTP://ETC.CH/Q7YT
- 4. AN “OFFICIAL" PROFESSION SINCE 2008 WHATISADATASCIENTIST? DJ Patil and Je ff Hammerbacher of LinkedIn and Facebook made "Data Scientist" an o ff icial buzzword. They were looking for a job title that didn’t sound too Wall Street (Data Analyst) nor too academic (researcher). “THETITLESOUNDSSOPHISTICATEDAND JUSTVAGUEENOUGHTOTRANSCEND INDUSTRIESANDBETAKENSERIOUSLY, EVENBYPEOPLEWHOHAVENOIDEA WHATITIS.”
- 6. FIRST USED IN 1974 IN “THE CONCISE SURVEY OF COMPUTER METHODS” WHATISDATASCIENCE? Peter Naur de f ined Data Science as "THEUSEFULNESSOFDATAANDDATA PROCESSESDERIVESFROMTHEIR APPLICATIONINBUILDINGANDHANDLING MODELSOFREALITY.”
- 7. SUCCESSFULLY BECOMINGA DATASCIENTIST DO NOT TRY TO BE A UNICORN… NO MATTER WHAT THE T-SHIRTS SAY
- 9. /IN/TEREZA-IOFCIU SOME PEOPLE THINK… YOUNEEDTOBEATHEORETICALEXPERT Lots of interviews still ask theoretical questions … Conceptual understanding & generalist mindset -> more useful on the job
- 10. /IN/TEREZA-IOFCIU SOME PEOPLE THINK… YOUNEEDTOBEAPROGRAMMINGEXPERT - The speed of new tools in DS space is only increasing - Focus on the basic - If you learned those from scratch -> helpful with knowledge transfer to another stack
- 11. /IN/TEREZA-IOFCIU SOME PEOPLE THINK… YOUNEEDTOBEAUNICORN,EXCELLENTATPROGRAMMING, STATISTICSANDLINEARALGEBRAANDDATABASES/SQL, VISUALISATIONANDKNOWALOTABOUTINDUSTRY - not really.. be good at 1-2 skills to get started and know you need to care about all. DS is a team e ff ort. - you can seem mediocre, you do need though good communication skills and examples of how you are e ff ective in a team or facing a new problem. Practice working in team projects.
- 12. /IN/TEREZA-IOFCIU SOME PEOPLE THINK… YOUNEEDTOTRY100MODELSTOPRODUCEA USEFULSOLUTION This kind of work can really be replaced by autoML or LLM based tools in the future Practice interpreting your data and your charts, build and use domain and statistics knowledge by asking yourself questions
- 13. /IN/TEREZA-IOFCIU SOME PEOPLE THINK… YOUNEEDTOSHOWALLTHETHINGSYOUKNOW - Hardly ever.. you need to show how you understand and tackle a problem with one approach, create a useful baseline, work iteratively towards a better solution - In an interview, you have limited time, ask what would be most important: speed, impact, tech debt and suggest a solution addressing that
- 15. Understand distributions Understand insights from other specialists, develop your analytic business knowledge You often start with getting data, cleaning data, doing EDA, and feature engineering. You get better at these when you understand your data and the business You might need to design an AB test to validate your solution /IN/TEREZA-IOFCIU THEORY-DATA STATISTICS & PROBABILITY
- 16. Really understand the basic models: linear and logistic regression, random forest, gradient boosting, ridge regression .. rather than rushing to LLMs and NN Decision-making in industry is mostly performed based on tabular data.. needing models that are not just accurate, but also e ff icient and interpretable /IN/TEREZA-IOFCIU THEORY-ML BASICS ARE ESSENTIAL
- 17. Strong 🐍 python skills .. beyond the notebook, including code testing and code reviews Essential e ff ective pandas, numpy, scipy and matplotlib or other viz libraries /IN/TEREZA-IOFCIU TOOLING BASICS ARE ESSENTIAL
- 18. Data is cross-functional. No matter what you will need to be able to • Clearly gather requirements and scope your problem • Use storytelling and viz to demonstrate impact • Get projects/budgets approved /IN/TEREZA-IOFCIU TOOLING COMMUNICATION IS 🔑
- 19. PICK AN APPROACH THAT WORKS FOR YOU AND YOUR EXPERIENCE / OR A COMBINATION GETTINGSTARTED Short programs Long programs / degrees Self - learning
- 21. ARE RESOURCEFUL AND HAVE STRONG MOTIVATION
- 23. KNOW WHEN TO STOP AND CAN STOP
- 24. HAVE GOOD TECHNICAL WRITING SKILLS
- 28. THANK YOU Special thanks to Noa Tamir for helping out with this talk 🫶 @VIS.SOCIAL@TEREZAIF /IN/TEREZA-IOFCIU 📧HELLO@TEREZAIOFCIU.COM ICONS FROM THENOUNPROJECT & ICONS8