O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

Startup - Big Data - Data Science

705 visualizações

Publicada em

Gambaran umum mengenai startup, fenomena big data, dan peran data science di dunia startup. Disampaikan dalam kuliah tamu "Karir dalam Matematika" Prodi Matematika ITB. 24022016

Publicada em: Dados e análise
  • Seja a primeira pessoa a gostar disto

Startup - Big Data - Data Science

  1. 1. Startup @teguhn
  2. 2. Bukalapak Mission: Empowering SMEs in Indonesia
  3. 3. What is startup? “a business or undertaking that has recently begun operation” The American Heritage Dictionary “a company designed to grow fast” Paul Graham “a temporary organization used to search for a repeatable and scalable business model” Stave Blank
  4. 4. Startup Culture ● Creative problem solving ● Open communication ● A flat hierarchy
  5. 5. Lean Startup
  6. 6. Lean Approach http://www.slideshare.net/Leananalytics
  7. 7. Lean Approach http://www.slideshare.net/Leananalytics
  8. 8. Big Data @teguhn
  9. 9. Big Data is cultural phenomenon It describes how much data is part of our lives, precipitated by accelerated advances in technology http://www.internetlivestats.com/
  10. 10. 4 V’s
  11. 11. Value Chains of Big Data ● Infrastructure (Data Origins to Data Integration) ● Analytics (Data Science world) ● Applications (Implementation)
  12. 12. Big Data Landscape
  13. 13. Data Science @teguhn
  14. 14. Why Data Science?
  15. 15. LinkedIn Hottest Skills “(statistical analysis and data mining) It is the only skill category that is consistently ranked in the top 4 across all of the countries we analyzed.” http://blog.linkedin.com/2016/01/12/the-25-skills-that-can-get-you-hired-in-2016/
  16. 16. Skills of Data Scientists http://www.business2community.com/big-data/big-data-and-analytics-value-chain-cross-section-0589031
  17. 17. Skills of Data Scientists
  18. 18. Types of questions ● Descriptive: What happened? ● Exploratory: See patterns in variables ● Inferential: Estimating variables population ● Causal: Does x affect y? ● Predictive: Predict values for another object Image : http://www.gs.washington.edu/academics/courses/akey/56008/lecture/lecture1.pdf
  19. 19. Test your hypothesis with A/B Testing
  20. 20. Why A/B Testing? ● Randomization experiment ● Confirming which idea is better ● Only some users will be tested ● Development effectivity ● Know the significance of the change ● Utilizing traffic Image : http://www.gs.washington.edu/academics/courses/akey/56008/lecture/lecture1.pdf
  21. 21. teguh@bukalapak.com ● Data Scientist ● Lead of Growth Team
  22. 22. References https://rjmetrics.com/resources/reports/the-state-of-data-science/ https://datasciencespecialization.github.io http://www.gs.washington.edu/academics/courses/akey/56008/lecture.htm http://datascopeanalytics.com/blog/six-qualities-of-a-great-data-scientist/ http://conferences.oreilly.com/strata/big-data-conference-uk-2015/public/schedule/detail/39814 https://datajobs.com/what-is-data-science http://mattturck.com/2016/02/01/big-data-landscape/ http://www.business2community.com/big-data/big-data-and-analytics-value-chain-cross-section-0589031 Lillian Pierson, “Data Science for Dummies”