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
   

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2. Nice to meet you
   • Data Science consultant:
   Natural Language Processing,
   Machine Learning,
   Data Engineering
   • Corporate training:
   Python + Data Science
   • PyData London chairperson
   2
   

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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
   

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4. WHAT IS
   DATA SCIENCE
   

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7. Data
   Value
   🦄
   7
   

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8. Data
   Value
   🦄
   ???
   8
   

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9. Value
   Insights
   Decision making
   Data products
   {
   9
   

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13. Data
    Value
    🦄 ???
    13
    

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14. Coding
    Math Modelling
    Visualisation
    Reporting
    {
    🦄
    14
    

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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
    

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16. Source: https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007
    16
    

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17. Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
    17
    

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18. Computer
    Science?
    18
    

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19. Software
    Engineering?
    19
    

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20. Business
    Intelligence?
    20
    

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21. Do you need
    3 PhD?
    21
    

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22. 🤝
    Computer

    Science
    Statistics
    Domain
    Expertise
    22
    

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23. ARE WE ALL
    DATA SCIENTISTS?
    

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24. Source: https://medium.com/hackernoon/the-ai-hierarchy-of-needs-18f111fcc007
    24
    

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25. 25
    Software Engineers
    Data Engineers
    

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26. 26
    ML Engineers
    

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27. 27
    Data Analysts

    Business Analysts
    

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28. 28
    Data Scientists
    

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29. 29
    Research Scientists
    

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30. 30
    Data Scientists

    at small corp
    

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31. DATA SCIENCE
    APPLICATIONS
    

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32. Weather
    32
    

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33. 33
    https://www.youtube.com/watch?v=_3sVA-_zIrc
    

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34. Healthcare
    34
    

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35. 35
    https://www.youtube.com/watch?v=B5n8Uavhl00
    

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36. Biology
    36
    

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37. 37
    https://www.youtube.com/watch?v=_9x4cmQWZ6g
    

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38. Journalism
    38
    

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39. 39
    https://www.youtube.com/watch?v=yPJhj855tvQ
    

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40. Product
    Recommendations
    40
    

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41. 41
    https://www.youtube.com/watch?v=qpbELUmbDIk
    

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42. Other
    Cool Stuff
    42
    

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43. 43
    https://www.youtube.com/watch?v=3k96HLqvhc0
    

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44. 44
    https://www.youtube.com/watch?v=S9PcPbtTcPc
    

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45. 45
    https://www.youtube.com/watch?v=UzGZtgu3PBM
    

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46. DATA SCIENCE
    SKILLS
    

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47. Getting into Data Science
    47
    

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48. Getting into Data Science
    48
    You
    🦄
    Data
    Scientist
    

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49. Getting into Data Science
    49
    You
    🦄
    Data
    Scientist
    What they tell you
    

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50. Getting into Data Science
    50
    You
    🦄
    Data
    Scientist
    How it feels like
    

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51. Getting into Data Science
    51
    “It depends”
    

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52. Getting into Data Science
    52
    Where are you?
    Where do you want to go?
    

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53. 🤝
    Computer

    Science
    Statistics
    Domain
    Expertise
    53
    

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54. Computer Science
    54
    

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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
    

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56. Math / Stats
    56
    

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57. Math / Stats
    57
    • Basic descriptive statistics
    • Data visualisation techniques
    • Linear algebra (vector/matrix computation)
    • Calculus
    • Mathematical optimisation
    • More advanced probability / stats
    Start
    Next
    

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58. Domain Expertise
    58
    

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59. Domain Expertise
    59
    • Speak the language
    • Basic data analysis
    • Deeper domain understanding
    • Communicate with business stakeholders
    (non-technical roles)
    Start
    Next
    

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60. Soft Skills
    60
    

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61. Soft Skills
    • They should be called “Core Skills” really
    • Communication
    • Story telling
    • Problem solving
    • Learning to learn
    61
    

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62. What’s Next
    62
    

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63. What’s Next
    1. Find a topic you like
    2. Find a dataset about the topic *
    3.
    63
    🦄
    * links at the end
    

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64. SUMMARY
    

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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
    

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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
    

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67. Thank You
    • Twitter: @MarcoBonzanini
    • Blog: marcobonzanini.com
    • Newsletter: marcobonzanini.com/newsletter
    • Questions?
    67
    

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