Data Science 101 - An overview medley

This is a medley of great talks on Data Sciences (see references)
copied together & compressed to fit into a 20 min. overview talk on the topic

Transcript

1. Data Science 101 - An overview medley - Dev.Talk December 2015 Marcel Körtgen
2. Disclaimer - What this is •A medley of great talks on DS (see references) •copied together & compressed → To fit into a 20 min. overview talk on the topic
3. Agenda •What is Data Science? •Why Data Science? •What is a Data Scientist? •How to become a Data Scientist?
4. What is Data Sciene?
5. D.Conways Venn Diagram
6. Data Science: The Origins 1970s: Peter Naur introduces “data science” as a synonym to “computer science” 1997: Jeff Wu claims “statisticians” are “data scientists”. 2001: William Cleveland introduces data science as an independent discipline, extending statistics. 2008: DJ Patil (LinkedIn) and Jeff Hammerbacher (Facebook) describe their job role as that of “Data Scientist”
7. Data Science: The Origins Term became trending since 2008 38 years
8. What about Big Data? •Volume SQL → HDFS •Velocity complex events processing, apache storm, apache spark streaming •Variety structured | semi-structured | unstructured social graphs, system logs, tweets/blogs, CCTV many variables, sampling variability (e.g., spatiotemporal)
9. What about Big Data? •Volume •Velocity •Variety Nobody wants data. Everybody wants data-driven reliable actionable insights
10. What is Data Science? Big Data ≠ Data Science
11. Really, what is data science? According to NIST “Data science is the empirical synthesis of actionable knowledge from raw data through the complete data lifecycle process.” According to NIST Big Data Framework
12. Why Data Sciene?
13. Man on the Moon - Small Data! Computer Program Date: 1969 64 Kb, 2Kb RAM, Fortran Must work lst time Apollo XI Speed: 3,500 km/hour Weight: 13,500 kg Lots of complex data Man on the Moon Distance: 356,000 km Never been there before Must return to Earth
14. Think About lt - We live in Crazy Times! Apollo Xl, 1969 64 Kb SkyDive Stratos, 2012 Tens of Gigabytes
15. Data Analyst Shortage Source: http://www.delphianalytics.netiwp-content/uploads/2013/04/GrowthOfDataVsDataAnalysts.png
16. What is a Data Scientist?
17. What is a Data Scientist?
18. What is a Data Scientist?
21. How to become a Data Scientist?
22. How to become a Data Scientist? Golden times for autodidacts • So much Open Source & Open Data (GitHub) • Never been easier to get in touch (Twitter, Social) • Low-cost Compute resources (Cloud, SAAS, PAAS) → However, you will finally hit a wall.
23. How to become a Data Scientist? At the latest then get in touch with • bootcamps, retreats, courses such as... • NYC DS bootcamp, DS Retreat, Udacity, Education.emc For now some references to get started...
24. References • 10 myths about Data Scientists (J. Kobelius) • Big Data [sorry] & Data Science (Data Science London) • Intro to Data Science (P. Nathan) • Data Science in 2016: Moving Up (P. Nathan) • How to Become a Data Scientist (R. Orban) • Jose Quesada über Skills der Data Scientists (Heise) • slideshare.net/urlwolf (Jose Quesada at SlideShare)
25. References Statistical Modeling: The Two Cultures by Leo Breiman, Statistical Science, 2001
26. References Data Quality by Jack Olson, Morgan Kaufmann, 2003
27. References Building Data Science Teams by DJ Patil, O’Reilly, 2011
28. References Data Jujitsu by DJ Patil, O’Reilly, 2012
29. References RStudio download and run on your laptop https://www.rstudio.com
30. Some great tools
31. [Sort of] Data Scientist Toolkit • Java, R, Python... (bonus: Clojure, Haskell, Scala) • Hadoop, HDFS & Map Reduce... (bonus: Spark, Storm) • HBase, Pig & Hive... (bonus: Shark, Impala, Cascalog) • ETL, Webscrapers, Flume, Sqoop... (bonus: Hume) • SQL RDBMS,DW,OLAP… • Knime, Weka, RapidMiner...(bonus: SciPy, NumPy, scikit-learn, pandas) • D3.js, Gephi, ggplot2, Tableu, Flare, Shiny… • SPSS, Matlab, SAS... (the enterprise man) • NoSQL, Mongo DB, Couchbase, Cassandra… • And Yes! ... MS-Excel: the most used, most underrated DS tool
32. Some great algorithms
33. Thank You Time for Questions!