What Managers Need to Know about Data Science
5 likes812 views
The document outlines the essential knowledge managers need regarding data science, including its definition, industry trends, and the roles of data scientists and managers. It highlights the growing demand for skilled data professionals, driven by advancements in technology and data availability, while also discussing challenges in managing data scientists. The presentation includes guidance on getting started with data science and the importance of continuous learning and management in this evolving field.
![Why
Just
Do
It?
Why
do
I
need
to
learn
about
data
science
and
manage
data
projects?
“I
have
[insert
#
of
years]
years
of
experience
in
[insert
my
industry].
I’m
comfortable
and
successful
being
a
[insert
your
Atle
here].”](https://image.slidesharecdn.com/sotec-what-mgr-need-to-know-about-ds-151026231459-lva1-app6891/85/What-Managers-Need-to-Know-about-Data-Science-25-320.jpg)
What Managers Need to Know about Data Science
- 1. What Managers Need to Know about Data Science Annie Flippo
- 2. Outline • What is data science • Industry trends • What is data • The Optimal Data Scientist • The Optimal Manager • Topics in Data Science • Topics in Cloud Computing
- 3. Who am I? Annie Flippo Data Scientist Software Engineer Product Manager Database Developer
- 4. What is Data Science?
- 5. Usage of Data Science Finance: fraud detecAon, score buying habits, calculate risks Insurance: inspect driving habits, assess risks, determine premiums
- 6. Usage of Data Science Biometrics: wearable devices to monitor and improve health Digital MarkeAng: recommender systems, audience segmentaAon, retargeAng, churn predicAon
- 7. Usage of Data Science Retail: Walmart launches compeAAon to solve business problems and to recruit talent Online: NeHlix launched $1 million prize to improve recommendaAon system
- 8. Usage of Data Science Healthcare: Heritage Network launched a compeAAon to predict the probability of hospitalizaAon of paAents. ScienAfic: NaAonal Data Science Bowl to predict ocean health: one plankton at a Ame
- 9. Why Should YOU Care? According to McKinsey1 (2011), Big Data: The next fron5er for innova5on, compe55on, and produc5vity. “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analyAcal skills as well as 1.5 million managers and analysts with the know-‐how to use the analysis of big data to make effecAve decisions”
- 10. Why Should YOU Care? According to Forbes2 (Oct 2015), The Hunt For Unicorn Data Scien5sts LiCs Salaries For All Data Analy5cs Professionals • Experienced data scienAsts are paid more than $200k per year • Median salary for data scienAst increased from $115,250 to $125,000 in one year • Managers managing large teams can expect a median salary of $235,000
- 11. Because it’s a growing and exciAng field with high compensaAon!
- 12. Explosion of Data Science Why now? • Storage cost has decreased dramaAcally • CompuAng power has increased exponenAally • People are carrying smartphones, mini supercomputers in their pockets • Perfect intersecAon of data availability and compuAng power for analyAcs
- 13. Massive amount of data Streaming into your company …
- 14. What is data? It can be raw web traffic logs …
- 15. What is data? Semi-‐structured data from APIs …
- 16. What is data? Or, structured data from databases… … what to do with all this data?
- 17. The Data ScienAst Can wrangle data from many sources or formats
- 18. The Data ScienAst do deep data exploraAons … and perform thorough analyses
- 19. DS Skills Inferred by Job Openings • Ph.D. in math, staAsAcs, engineering or physical science (Is it really required?) • Has 5+ years in programming experience in Java, Scala, Python, R, SQL, MapReduce, etc. • Has 5+ years experience in most of the Apache Open Source Technologies (e.g. Hadoop, Spark, Hive, Pig, Kaka, etc)* • Tell a story like a novelist (coherently and beauAfully) * By the Ame you read this footnote, the Apache stack has already grown.
- 20. The OpAmal Data ScienAst Is a person with deep staAsAcal and machine learning knowledge, extensive somware engineering skills and well-‐versed in business strategy!
- 21. The OpAmal Data ScienAst – Take 2 Personality Traits3 • Compulsive • Propulsive laziness • Drive to create and learn • Irritable determinaAon • InsensiAvity to pain (hmm…) • Integrity • Humility
- 22. The OpAmal DS Manager • Former data scienAst (good to have but not necessary; that’s just asking for another unicorn!) • Actually interested in managing people • Thirst to learn • Apt in managing different projects • PaAent and diplomaAc to manage a diverse group of data scienAsts and business owners • Understand when to go with an 80/20 approach
- 23. Data ScienAsts: The Challenge of Managing Stubbornly Autonomous Experts4 “I no5ced … that data scien5sts, but also sta5s5cians and top coders, oCen have difficul5es accep5ng orders from managers who don’t have technical skills themselves.” -‐ Istvan Hajnal
- 24. Journey to become a DS Manager Nate Silver on Finding a Mentor, Teaching Yourself StaAsAcs, and Not Sesling in Your Career5 • Find a Mentor (Yes, even if you’re already a senior manager) • Teach Yourself (online resources, MOOCs) • Understand the life-‐cycle of a data-‐driven project • Just do it!
- 25. Why Just Do It? Why do I need to learn about data science and manage data projects? “I have [insert # of years] years of experience in [insert my industry]. I’m comfortable and successful being a [insert your Atle here].”
- 27. Data Sources Data projects are lurking everywhere …
- 28. Machine Learning
- 29. Machine Learning
- 30. Machine Learning
- 31. Machine Learning Google X laboratory5
- 33. Data Science Concepts PredicAve AnalyAcs ClassificaAon RecommendaAon Systems
- 35. Topics in Cloud CompuAng New services added:
- 36. Your Job: Provide Guidance Tell us a data story … about your business Do you understand the outcome? What is your recommendaAon to the business?
- 37. Gezng Started: Locally Meetups • LA R users group • LA Machine Learning • LA Data Warehouse, BI & AnalyAcs • LA Big Data Users Group Conferences: • datascience.la • bigdatadayla.org
- 38. Gezng Started: Podcasts dataskepAc.com thetalkingmachines.com
- 40. Good Places to Start Data Science for Business by Foster Provost & Tom Fawces
- 41. Good Places to Start Doing Data Science by Rachel Schus & Cathy O’Neil (mathbabe.org) Free at www.columbiadatascience.com
- 42. Good Places to Start The Art of Data Science by Roger Peng & Elizabeth Matsui hsps://leanpub.com/artofdatascience
- 43. Get Kids Started scratch.mit.edu www.ixl.com
- 44. Thank You! Annie Flippo @ACflippo Slides are available at goo.gl/1X2NMH
- 45. References 1. hsp://www.mckinsey.com/insights/business_technology/ big_data_the_next_fronAer_for_innovaAon 2. hsp://www.forbes.com/sites/gilpress/2015/10/09/the-‐hunt-‐for-‐unicorn-‐ data-‐scienAsts-‐lims-‐salaries-‐for-‐all-‐data-‐analyAcs-‐professionals/ 3. hsp://cdn.oreillystaAc.com/en/assets/1/event/119/Data%20Science %20Bootcamp%20PresentaAon.pdf 4. hsp://www.ibmbigdatahub.com/blog/data-‐scienAsts-‐challenge-‐managing-‐ stubbornly-‐autonomous-‐experts 5. hsps://hbr.org/2013/09/nate-‐silver-‐on-‐finding-‐a-‐mentor-‐teaching-‐yourself-‐ staAsAcs-‐and-‐not-‐sesling-‐in-‐your-‐career/ 6. hsp://www.nyAmes.com/2012/06/26/technology/in-‐a-‐big-‐network-‐of-‐ computers-‐evidence-‐of-‐machine-‐learning.html 7. hsp://research.google.com/archive/unsupervised_icml2012.html