Career Coaching

D73dc2189cf378ae9088283c720d0331?s=47 Pacmann AI
September 05, 2020

Career Coaching

D73dc2189cf378ae9088283c720d0331?s=128

Pacmann AI

September 05, 2020
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Transcript

  1. Career Coaching By Pacmann AI Adityo Sanjaya, adit@pacmannai.com

  2. Hi! Please tell me about yourself!

  3. Contents 1. Data Industry in Indonesia 2. Tell Me about

    Your Career Progress 3. Skills Needed to Enter the Industry 4. Nano Degree Pacmann AI 5. Let’s Discuss Your Knowledge Gap 6. Biased Advice: Learn with Us!
  4. Data Industry in Indonesia 2013 - 2020

  5. Data Industry An industry related to: • prediction system, •

    Inference, • data gathering, • business optimization, by combining some of those processes above. Data Industry 2013 - 2014 Job - Quant, - Data Scientist, - ML Engineer - Marketing Analytics, - Social Researcher, - "Bigdata" analyst, - Social Media analyst, - Experimenter - Business Intelligence Analyst - Many more
  6. • In 2013, it was still rare to see “Data

    Analytics” in Indonesia. • Something similar was developed at that time, Social Media Analysis. • There was a social media usage boom at that time. People love to use social media. • Company want to cater their marketing according to people’s social media behavior. Data Industry 2013 - 2014 Social Media Analysis • Sentiment Analysis • Cluster in Twitter networks • User profiling untuk Marketing
  7. Most Common Jobs in 2013 • "Data Analyst" • Social

    Media Researcher • Statistician It’s a wrong generalization, but it can capture common pattern of job at that time. Skills Needed • R • SPSS • Statistics Barrier to Entry • Low Data Industry 2013 - 2014
  8. Data Industry 2013 - 2014 Big Data Analysis • Huge

    data from social media, say 10TB • It need to be stored and processed in a large scale. • People came up with “Big Data Solution” • It is a jargon • In 2014, the increase of social media usage was still huge. • Twitter gave their data for free, but limited. • No privacy issue/problem • People make many twitter bots to tackle that limitation. • Social Media Monitoring services increased in Indonesia
  9. Skills Needed • R • JAVA • Statistics • Software

    Engineering • Natural Language Processing Barrier to Entry • Medium Most Common Jobs in 2014 • BigData Engineer • Data Engineer • Social Media Researcher • Statistician Data Industry 2013 - 2014
  10. Data Industry in Indonesia 2015 - 2016

  11. • In 2015, there was a Machine Learning democratization. •

    People loved to talk about Machine Learning and AI. • But.. Machine Learning usage in industry was very rare. • Some of the prominent startups was hiring Data Scientist, e.g. Bukalapak and Traveloka Data Industry 2015 - 2016 Data Scientist • People without any skills and experience were claiming to be a Data Scientist. • No ML skills • No Statistics skills • Never deploy any ML services
  12. Skills Needed • Python • Statistics Barrier to Entry •

    Medium Most Common Jobs in 2015 • Data Scientist • Data Engineer Data Industry 2015 - 2016
  13. • In 2016, there was a Machine Learning democratization and

    Deep Learning boom! • People loved to talk about Deep Learning • But.. Deep Learning usage in industry was very rare. • Nonetheless industry became more technical. Deep Learning • People still don’t know how to install Tensorflow with CUDA • They didn’t understand Deep Learning • But.. they have understand Machine Learning in general • Never deploy any ML services Data Industry 2015 - 2016
  14. Skills Needed • Python • Machine Learning • Statistics Barrier

    to Entry • High • Salary increased • Portfolio • Hacker rank…. Most Common Jobs in 2016 • Data Scientist ◦ Data Scientist became a hot career! • Data Engineer Data Industry 2015 - 2016
  15. Data Industry in Indonesia 2017 - 2018

  16. Senior DS • In 2017 and 2018, people already understand

    ML dan DL. • Some startups already deployed their first ML services and have great success, e.g Salestock and Kata.ai • There was a huge increase in Data Scientist Supply • Most of these DS don’t understand Math, Stats, ML, and “deployment skills”. “Data Scientist” Data Industry 2017 - 2018
  17. Skills Needed • Python • Machine Learning • Mathematics •

    Statistics • Deployment, some SoftEng skills Barrier to Entry • Higher! • Salary increased • Portfolio • Hacker rank…. Most Common Jobs in 2017 • Data Scientist ◦ Data Scientist became a hot career! • Data Engineer • ML Engineer Data Industry 2017 - 2018
  18. Data Industry in Indonesia 2019 - 2020

  19. Tell Me about Your Career Progress

  20. What is your goal now?

  21. What is the driver of your current career change?

  22. Skills Needed to Enter the Industry

  23. Generally speaking: - Mathematics - Statistics - Programming - Business

    - Machine Learning
  24. Nano Degree Pacmann AI

  25. Data Analyst Introduction to Data Analysis Python Data Wrangling Visualization

  26. Business Intelligence Introduction to Business Intelligence Calculus: Single Variable Python

    Data Wrangling DataBase Visualization Visualization Project: Analytics Dashboard
  27. AB Tester, Experimenter Introduction to Experimenter Calculus I: Single Variable

    Python Data Wrangling Visualization DataBase Probability I Sampling Abtest AbtestProj
  28. Qualitative Researcher,Marketing Researcher Introduction to Qualitative Research Calculus I: Single

    Variable Python Data Wrangling Visualization Probability I Sampling Questionnaire Design Questionnaire Project
  29. Quantitative Social Scientist, Applied Statistician Introduction to Quantitative Social Science

    Calculus I: Single Variable Python Data Wrangling Visualization Probability I Applied Linear Algebra Probability II Econometrics Applied Linear Model Cases Time Series Causality Categorical Data
  30. Statistician Introduction to Statistics Calculus I: Single Variable Python Data

    Wrangling Visualization Probability I Sampling Abtest Questionnaire Design Probability II Calculus II: Multivariable Calculus Applied Linear Algebra Econometrics Categorical Data Time Series Causality Simulation Bayesian Applied Linear Model Cases
  31. Machine Learning Scientist Introduction to Machine Learning for Business Calculus

    I: Single Variable Python Data Wrangling Visualization DataBase Probability I Probability II Calculus II: Multivariable Calculus Applied Linear Algebra Introduction to Machine Learning Machine Learning Cases DevTools Optimization DeepLearning CompLinAlg RecSys ProbaML Time Series
  32. Decision Scientist Introduction to Decision Science Calculus I: Single Variable

    Python Data Wrangling Visualization Probability I Sampling Probability II Calculus II: Multivariable Calculus Abtest AbtestProj Questionnaire Design Questionnaire Project Applied Linear Algebra Econometrics Applied Linear Model Cases Time Series Causality Optimization
  33. Data Scientist Introduction to Data Science Calculus I: Single Variable

    Python Data Wrangling Visualization DataBase Probability I Probability II Calculus II: Multivariable Calculus Applied Linear Algebra Introduction to Machine Learning Machine Learning Cases DevTools Econometrics Applied Linear Model Cases Time Series Causality Optimization Deep Learning
  34. Machine Learning Engineer Introduction to Machine Learning Engineer Calculus I:

    Single Variable Python Data Wrangling Visualization DataBase Introduction to Machine Learning DevTools MLEng1 MLEng2
  35. Data Manager Specialization Introduction to Machine Learning for Business Project

    Management for Data Team Data Initiatives Building Data Product
  36. Let’s Discuss about Your Knowledge Gap

  37. What do you know now?

  38. What are the skills that you hope to understand for

    your next career?
  39. What is your obstacle to learn those skills now?

  40. Biased Advice: You Can Learn with Pacmann AI

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  49. 235 Applicants!

  50. Check our brosur: bit.ly/brosurpacmannai