Getting into Data Science @ HisarCS 2021

Slides for my introduction to Data Science given at the Hisar Coding Summit 2021: http://event.hisarcs.com/en.html

Talk description:
This is a brief introduction to Data Science with an overview on some interesting problems that it can tackle. The talk is mainly aimed at students who are interested in knowing more about Data Science, and it will try to answer the question "what do data scientists do all day?", in order to offer some insights on whether you should consider a career in Data Science and how to start building your Data Science skill set.

Transcript

1. Getting into Data Science @MarcoBonzanini Hisar Coding Summit 2021
2. Nice to meet you • Data Science consultant: Natural Language Processing, Machine Learning, Data Engineering • Corporate training: Python + Data Science • PyData London chairperson 2
3. My Goals for Today • Answer some questions: What is Data Science? What do Data Scientists do? (and more) • Inspire some of you to learn more about Data Science 3
4. WHAT IS DATA SCIENCE
7. Data Value 🦄 7
8. Data Value 🦄 ??? 8
9. Value Insights Decision making Data products { 9
13. Data Value 🦄 ??? 13
14. Coding Math Modelling Visualisation Reporting { 🦄 14
15. Source: Doing Data Science (Cathy O’Neil & Rachel Schutt, 2013) Raw
 Data Processing
 Data Clean
 Data Exploratory
 Analysis Models &
 Algorithms Communicate
 Visualise
 Report Data
 Product Decision
 Making 15
16. Source: https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007 16
17. Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram 17
18. Computer Science? 18
19. Software Engineering? 19
20. Business Intelligence? 20
21. Do you need 3 PhD? 21
22. 🤝 Computer
 Science Statistics Domain Expertise 22
23. ARE WE ALL DATA SCIENTISTS?
24. Source: https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007 24
25. 25 Software Engineers Data Engineers
26. 26 ML Engineers
27. 27 Data Analysts
 Business Analysts
28. 28 Data Scientists
29. 29 Research Scientists
30. 30 Data Scientists
 at small corp
31. DATA SCIENCE APPLICATIONS
32. Weather 32
33. 33 https://www.youtube.com/watch?v=_3sVA-_zIrc
34. Healthcare 34
35. 35 https://www.youtube.com/watch?v=B5n8Uavhl00
36. Biology 36
37. 37 https://www.youtube.com/watch?v=_9x4cmQWZ6g
38. Journalism 38
39. 39 https://www.youtube.com/watch?v=yPJhj855tvQ
40. Product Recommendations 40
41. 41 https://www.youtube.com/watch?v=qpbELUmbDIk
42. Other Cool Stuff 42
43. 43 https://www.youtube.com/watch?v=3k96HLqvhc0
44. 44 https://www.youtube.com/watch?v=S9PcPbtTcPc
45. 45 https://www.youtube.com/watch?v=UzGZtgu3PBM
46. DATA SCIENCE SKILLS
47. Getting into Data Science 47
48. Getting into Data Science 48 You 🦄 Data Scientist
49. Getting into Data Science 49 You 🦄 Data Scientist What they tell you
50. Getting into Data Science 50 You 🦄 Data Scientist How it feels like
51. Getting into Data Science 51 “It depends”
52. Getting into Data Science 52 Where are you? Where do you want to go?
53. 🤝 Computer
 Science Statistics Domain Expertise 53
54. Computer Science 54
55. Computer Science 55 • Basic coding in 1 language (e.g. Python) • Data Manipulation • Optional: out-of-the-box Machine Learning • Database technologies (e.g. SQL, NoSQL, etc) • “Behind the scenes” of Machine Learning • More programming languages (R, Scala, …), data processing tools (Spark, Elasticsearch, …), and other shiny toys Start Next
56. Math / Stats 56
57. Math / Stats 57 • Basic descriptive statistics • Data visualisation techniques • Linear algebra (vector/matrix computation) • Calculus • Mathematical optimisation • More advanced probability / stats Start Next
58. Domain Expertise 58
59. Domain Expertise 59 • Speak the language • Basic data analysis • Deeper domain understanding • Communicate with business stakeholders (non-technical roles) Start Next
60. Soft Skills 60
61. Soft Skills • They should be called “Core Skills” really • Communication • Story telling • Problem solving • Learning to learn 61
62. What’s Next 62
63. What’s Next 1. Find a topic you like 2. Find a dataset about the topic * 3. 63 🦄 * links at the end
64. SUMMARY
65. Summary • Data Science lets you work in any domain • What kind of data scientist do you want to be? • You don’t need to be an expert in everything 65
66. Resources • Datasets: kaggle.com • Datasets: archive.ics.uci.edu • Datasets: “awesome data” on GitHub.com • Book: Doing Data Science (O’Neil and Schute) • Videos: youtube.com/user/PyDataTV 66
67. Thank You • Twitter: @MarcoBonzanini • Blog: marcobonzanini.com • Newsletter: marcobonzanini.com/newsletter • Questions? 67