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Data Science: Past, Now, and Future

Data Science: Past, Now, and Future

What is data science, why this study is emerging, what is happened in the past, now, and how about the future?

A data science study short introduction from technology, business, management, ethics, and history domains

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Fiqry Revadiansyah

June 28, 2020
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  1. Data Science Fate: Past, Now, & Future Fiqry Revadiansyah

  2. Trainer Bio Data Scientist Bukalapak (2018 – Present) Technical Content

    Reviewer Packt Publishing (2019 – Present) Email : fiqryrevadiansyah@gmail.com | Linkedin : https://www.linkedin.com/in/fiqryrevadiansyah Fiqry Revadiansyah Work Experience Teaching Experience Guest Lecturer MB1201 Business Statistics at SBM ITB (April 2020) Part-time Teacher Data Science Purwadhika (2019), and Workshop speaker: Introduction to ML for DS (June 2020) & Statistics for Business Analytics (Nov 2019)
  3. A condition of Past and Current of Data Science

  4. Advancement(s) In our daily routines

  5. Development Happens Through Innovation War

  6. War Among Companies

  7. War Among Companies *Brand Finance: "Global 500 report 2020” source:

    visual capitalist
  8. Innovation at War IoT Implementation on Amazon Warehouse Amazon Warehouse

    management Streaming data as the fuel of robotics automation on warehouse management system
  9. Innovation at War Text suggestions on Google Mail (Gmail) Google

    AI Services Data as the backbone of many AI services in Google
  10. Innovation at War Immersive Experience on Google Maps Google AI

    Services Data as the backbone of many AI services in Google
  11. Why and How it is happening?

  12. Interpreting The Speed of Change How Industrial Revolution was started

    Mechanization Era Steam Engine (1780 - 1870) Mass Production Era Assembly Line (1870 – 1970) Automation Era Robotics & Computer (1970 – Now)
  13. Interpreting The Speed of Change How Industrial Revolution was started

    Automation Era Robotics & Computer (1970 – Now) Smart Device Era AI and IoT (2010 – Now) Only 40 Years How?
  14. We are in the middle of AGE OF DISRUPTION -

    How Question -
  15. Interpreting The Speed of Change How Industrial Revolution was started

    Automation Era Robotics & Computer (1970 – Now) Smart Device Era AI and IoT (2010 – Now) “VUCA World” Firstly Introduced (1987)
  16. Interpreting The Speed of Change VUCA World, a Cause of

    Speed Previously OODA Observe - Orient - Decide - Act “The global socio-economic condition is so volatile, uncertain, too complex and ambiguous” Open Innovation Frugal Innovation Community Innovation Disruptive Innovation
  17. Interpreting The Speed of Change Disruptive Innovation, A Gear of

    Speed “Disruptive Innovation” -> How they enter the market Incumbent Company Entrants Company
  18. “Our world is an interconnected system straining under the burden

    of its own complexity” - World Economic Forum on Annual Report 2015 - 2018
  19. Interpreting The Speed of Change Disruptive Innovation, a Gear of

    Speed Automation Era Robotics & Computer (1970 – Now) Smart Device Era AI and IoT (2010 – Now) Disruptive Innovation VUCA World Then Why?
  20. Source of Change DATA ANALYTICS EVOLUTION - Why Question -

  21. Data Analytics Evolution Human Nature on Decision Making The Babylonian

    Map of the World, the Oldest Usable Map (700 - 500 BCE) The Idea of Data-Driven decision is not exactly new, it is a human nature to make decision based on data. Our ancestors shown that they made a map of the world, using a clay tablet. Even in the early 18th centuries, people decided things using a back form of data, like paper to write and store information
  22. Data Analytics Evolution Evolved Tools and Big Data Emergence Thanks

    to electricity invention, the first wave of technological advancement born in 1954 (First computer by IBM, IBM 650). Computer helps human to make a quicker decision making with abundant information. Right now, we have CPU, GPU, TPU, etc. As the computer wave arrived, the number of stored data also increased exponentially through time. Even nowadays, there is more data has been created in the past two years than in the entire previous history.
  23. Data Analytics Evolution Evolved Tools and Big Data Emergence ACTORS

    STORAGE PAST NOW
  24. Data Analytics Evolution Evolution of Analytical Role Domain Science A

    science to interpret output to be outcome Decision Science A science to determine/select input to be processed Engineering Science A science to process input to be an output Data Science A combination of three sciences, end-to-end process from deciding input to produce outcome
  25. Past (Cow -> Milk) Now (Robotics ->Automation) Decision Science Determine

    which grass will produce best milk Determine which data will produce best behavior Engineering Science Determine which cow will produce best milk Determine which tools will produce best behavior Domain Science Determine which milk will produce highest money Determine which behavior will produce best outcome Data Analytics Evolution Data Science - Analogy Past and Now
  26. Data Analytics Evolution Data Science – Past and Now PAST

    Less data variation, store data on a traditional/old way Limited tools, build in mechanics/traditional way Finite business models, lesser choice to produce outcome NOW Abundant data, store data on a proper and vast database(s) So many tool variations due to technological advancement Almost infinite business models, more choice to produce outcome Decision Science Engineering Science Domain Science Decision Science Engineering Science Domain Science
  27. Industrial Revolution 4.0 A Conclusion of its Causality Automation Era

    Robotics & Computer (1970 – Now) Smart Device Era AI and IoT (2010 – Now) Disruptive Innovation VUCA World Data, Tools, & Analytics Evolution (Data Science)
  28. Data Science as an Emerging Sector

  29. Emerging Sector Data Science and Machine Learning Trend Over Time

    Andrew Ng, introduced GPU to Deep Learning
  30. Emerging Sector Data Science Venn Diagram

  31. Emerging Sector Data Science Functionalities How to make a powerful

    business decision, by having low risk and gain immense impacts both qual and quant How to make a worthwhile business campaign, spend less money to get huge revenue How to accurately market a business product, with minimum cost use to attract potential loyal user Decision Science Business Science Marketing Science
  32. Data Science as an Decision Science

  33. Decision Science From Data Science Functionality

  34. Decision Science From Data Science Functionality Dashboard by Descriptive Statistics

    (Mean, Median, Mode, Percentile, Box-plot, etc.) Alerting by Confidence Interval
  35. Decision Science From Data Science Functionality Data Forecasting by Time

    Series Analysis (Univariate/Multivariate) and Regression Analysis (Single, Multiple, Weighted, etc.)
  36. Decision Science From Data Science Functionality Experimenting by Pair Test

    (t-Test, Chi- Square,ANOVA, etc.) In the industry, this test also known as AB Testing
  37. Data Science as an Business Science

  38. Business Science From Data Science Functionality

  39. Business Science From Data Science Functionality Fraud Model by Multivariate

    Statistics (Cluster Analysis, Multidimensional Scaling, etc.) and Network Analysis
  40. Business Science From Data Science Functionality Churn Prediction by Regression

    Analysis (Multiple, Logistic) and Supervised Machine Learning (Random Forest, XGBoost, etc.)
  41. Business Science From Data Science Functionality Social Media Analysis by

    Descriptive Statistics (Mean, Median, Mode, Percentile, Box-plot, etc.) and Supervised Machine Learning (Topic Clustering, Sentiment Analysis)
  42. Data Science as an Marketing Science

  43. Marketing Science From Data Science Functionality

  44. Marketing Science From Data Science Functionality Segmentation by Multivariate Statistics

    (Cluster Analysis, Multidimensional Scaling, etc.). Such as LRFM models (Length, Recency, Frequency, and Monetary)
  45. Marketing Science From Data Science Functionality Recommendation Cluster by Matrix

    Factorization (Content-Based Filtering, Collaborative Filtering) and Multivariate Statistics (kNN segmentation, Multidimensional Scaling, etc.)
  46. Marketing Science From Data Science Functionality Research is conducted by

    Descriptive Statistics (Proportion) and Inferential Statistics (t-Test, Chi-Square Test, ANOVA, etc)
  47. Emerging Sector Data Science as Warfare Tools Smart Device Era

    AI and IoT (2010 – Now) Data Science Artificial Intelligence Internet of Things Machine Learning Deep Learning etc Data Warfare Among companies (2010 – Now)
  48. What is Next? Future of Data Science

  49. AI, Threats or Opportunities? A perspective of how AI will

    change the world
  50. AI, sometimes cannot be controlled Boat game AI – DeepMind

    Google Objective: Reach the Highest Score to Win the Game
  51. Problem of Machine Learning Lack of Model Explainability

  52. Problem of Machine Learning Black Box – Cannot be Explained

  53. Problem of Machine Learning Black Box – Sometimes Sexist

  54. Problem of Machine Learning Explainability vs Performance

  55. What is the solution? XAI (Explainable AI)

  56. XAI – Explainable AI Explainable as the future of ML

  57. XAI – Explainable AI Explainability will profit the business process

  58. XAI – Explainable AI Explainability as a human-centric solutions

  59. Thank you Data Science Fate: Past, Now, & Future Fiqry

    Revadiansyah