How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansion, Galvanize
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This document provides information about becoming a data scientist. It discusses the perfect storm of factors driving growth in data science jobs, including abundant data, cheap storage, and competitive advantages from data. It outlines the skills needed like mathematics, statistics, computer science, machine learning, and software engineering. It recommends learning programming languages like Python and R. It also suggests demonstrating expertise through projects on sites like GitHub and DataTau. Finally, it describes an immersive data science program that provides training and connections to employers.
![• Ask good questions. What do we not know? What would we
like to know?
• Define and test an hypothesis. Run experiments with data.
• Scrape, munge, transform, and clean data. Instrument.
• Explore Data, Discover Unknowns
• Model Data, Understand Data Relationships
• Create Data Products. Tell Relevant Business Stories.
10 Things [most] Data Scientists Do](https://image.slidesharecdn.com/h7bpdw1tfca1pq91bozg-signature-891f1936330eb29c5d8bafecb5cdd679eca2680b8388551b42456f5220e7be9d-poli-150415191715-conversion-gate01/85/How-to-Become-a-Data-Scientist-By-Ryan-Orban-VP-of-Operations-and-Expansion-Galvanize-17-320.jpg)
How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansion, Galvanize
- 1. HOW TO BECOME A DATA SCIENTIST Wednesday, April 15th 6:15-8:30p 1062 Delaware St.
- 2. Co-Founder & CEO Zipfian Academy Ryan Orban @ryanorban VP of Operations & Expansion Galvanize
- 3. Why are we talking about data science?
- 6. Technology Year Capacity (GB) Cost per GB (USD) 1992 0.08 $3,827.20 1997 2.1 $157.00 2002 80 $3.74 2007 750 $0.35 2012 3,000 $0.05 Source: http://www.jcmit.com/diskprice.htm
- 7. Technology Source: http://www.jcmit.com/diskprice.htm 0 1000 2000 3000 4000 1992 1997 2002 2007 2012 Capacity (GB) Cost per GB (USD)
- 11. Data Science Job Growth Source: LinkedIn Analytics
- 13. Enter the Data Scientist
- 14. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 17. • Ask good questions. What do we not know? What would we like to know? • Define and test an hypothesis. Run experiments with data. • Scrape, munge, transform, and clean data. Instrument. • Explore Data, Discover Unknowns • Model Data, Understand Data Relationships • Create Data Products. Tell Relevant Business Stories. 10 Things [most] Data Scientists Do
- 18. What do people look for in a data scientist?
- 20. T-Shaped Skillset Machine Learning, Statistics, Domain Knowledge Softw are EngineeringBusiness Acum en Distributed Com puting Com m unication
- 22. How to I become a data scientist?
- 24. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 25. Mathematics Statistics Mathematics & Statistics Statistical Distributions (Non) Parametric Tests Significance & Hypothesis Testing Bayesian Methods Linear Algebra Multivariable Calculus Graph Theory Probability
- 27. Learn the tools of the trade. 2
- 29. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 30. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 31. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 32. Learn to Code
- 33. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 34. Distributed Computing Supervised (SVM, Random Forest) NLP / Information Retrieval Algorithms & Data Structures Data Visualization Data Munging Machine Learning & Software Engineering Machine Learning Software Engineering Validation, Model Comparison Unsupervised (Clustering, Topic Modeling)
- 36. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 37. DataTau GitHub
- 38. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 39. DATA SCIENCE HIERARCHY OF NEEDS Mathematics, Statistics, Probability FOUNDATIONAL Software Engineering, Machine Learning, ETL, Data Transformation, SQL FUNCTIONAL Distributed Computing: Hadoop, Cascading, Spark, GraphLab SCALABLE Make an impact, communicate results, identify business objectives EMOTIONAL
- 40. Learning data science on your own can be long and winding road.
- 42. Open-Source Data Science Masters
- 45. SlideRule
- 46. Building the modern, educational campus for digital innovators and entrepreneurs
- 49. BOOT CAMPS LACK A LARGER ECOSYSTEM ISSUE #3
- 50. AN URBAN CAMPUS & EDUCATION PLATFORM
- 51. Galvanize provides immersive educational programs in web development and data science. IMMERSIVE EDUCATIONAL PROGRAMS Full stack development curriculum provided over an immersive, six month full-time program • Strong Test-Driven Development (TDD)/agile software development environment • No prior coding experience required FULL STACK Data science program (previously Zipfian academy) taught over 12-week immersive schedule • One year accredited program resulting in Masters degree (Engineering/Big Data) DATA SCIENCE PROGRAM
- 52. Timeline STRUCTURED CURRICULUM HIRING DAY CAPSTONE PROJECT GRADUATION 1 8 11 12 INTERVIEW PREP Program Timeline
- 53. The Program • Project-based curriculum with real datasets, solving actual problems • Guest lectures from leaders in the field • Personal mentorship to help students grow • Capstone project with company partners • Vast network of hiring partners and events
- 56. Backgrounds Educational Background BS MS PhD 0 4 8 12 16
- 57. Backgrounds Disciplines Software Engineering Analysts Finance/Economics Engineering Physics Physical Sciences Mathematics Statistics Astronomy Linguistics Professional Poker 0 2 4 6 8
- 58. 94% Placement Rate94% Placement $115k avg. salary
- 59. Hiring Partners
- 60. • Working knowledge of programming • Background in a quantitative discipline, strong fundamentals • Comfortable with mathematics and statistics • Child-like curiosity What We Look For
- 61. Data Science Immersive Denver, July 6th
- 62. • Present a guest lecture or share a data story • Donate datasets and propose projects • Sponsor a scholarship • Attend one of our Hiring Days Get Involved
- 63. • Instructors (Full-time & Part-Time) • Outcomes Manager • Student Services • Community Coordinator • Membership Manager We’re Hiring!
- 64. Data Science Immersive GalvanizeU Full-stack Web Development Weekend Workshops
- 65. A Practical Intro to Data Science http://bit.ly/learndatascience
- 66. THANK YOU RYAN ORBAN| VP OF OPERATIONS & EXPANSION @RYANORBAN www.galvanize.com