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What your employees need to learn to work with data in the 21 st century

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Human Capital Media
Human Capital Media

The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.

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#CLOwebinar The presentation will begin at the top of the hour. A dial in number will not be provided. Listen to today’s webinar using your computer’s speakers or headphones. Welcome to the webinar!
#CLOwebinar Tools You Can Use Audio Control –  A dial in number will not be provided. –  The audio will stream through your headphones or computer speakers. –  Also check your computer’s volume for external speakers or headsets.
#CLOwebinar Questions and Handouts You can submit questions by clicking on this icon here. You can download a PDF of the slide deck by clicking here.
#CLOwebinar 1. May I receive a copy of the slides? YES! Click on the handouts located on the right portion of your screen. 2. May I review the webinar recording at a later date? YES! You may log in again using today’s link to review the presentation on-demand. 3. Is this webinar HRCI or SHRM certified? YES! The HRCI and SHRM certification codes will be sent in the follow up email. Frequently Asked Questions
07/23/2019 What Employees Need to Learn to Work With Data in the 21st Century Hugo Bowne-Anderson @hugobowne
Illustrations you can use, just copy/paste ➔  Hugo Bowne-Anderson, data scientist at DataCamp ◆  Undergrad in sciences/humanities (double math major) ◆  PhD in Pure Mathematics (UNSW, Sydney) ◆  Applied math research in cell biology (Yale University, Max Planck Institute) ◆  Python curriculum engineer at DataCamp ◆  Host of DataFramed, the DataCamp podcast A bit about me
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
“More than anything, what data scientists do is make discoveries while swimming in data.” -- Thomas Davenport & DJ Patil
“Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.” -- Wikipedia
Illustrations you can use, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
Where is data science in the Gartner Hype cycle?
1.  Innovation trigger 2.  Peak of inflated expectations 3.  Trough of disillusionment 4.  Slope of enlightenment POLL: Where do you think data science is in the Gartner Hype Cycle?
Where is big data in the Gartner Hype cycle?
Where is data science in the Gartner Hype cycle?
Where is data science in the Gartner Hype cycle?
What will the slope of enlightenment look like? “Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?” -- DataCamp in HBR
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
Why You Should Care About Data Science ➔  When so much data is informing decisions across so many industries, you need to have a basic understanding of the data ecosystem in order to be part of the conversation. ➔  On top of this, the industry that you work in will more than likely see the impact of data analytics.
Illustrations you can use, just copy/pasteWhy data science? Poised to Disrupt All Industries
1.  The drones are spraying pesticides 2.  The farmer’s children are using drone footage to add to their Instagram stories 3.  Footage captured by the drone is utilized to optimize crop yields POLL: How is data science being used in this image?
Illustrations you can use, just copy/pasteData Science in Finance Stock market prediction
Illustrations you can use, just copy/pasteData Science in Finance ➔  “Traders used to be first-class citizens of the financial world, but that’s not true anymore. Technologists are the priority now…” -- Robin Wigglesworth
Illustrations you can use, just copy/pasteData Science in Finance ➔  “...with a little bit of training, you can accomplish quite a lot compared to the traditional approaches in this field.” -- Yves Hilpisch
Illustrations you can use, just copy/paste Flatiron Health Data Science in Healthcare Cancer patient survival probability
Data Science in Healthcare ➔  Disease diagnosis from imaging data (e.g. MRI, PET)
Data Science in Healthcare ➔  Google AI have developed an algorithm to detect diabetic retinopathy as well as experts ➔  One of the leading causes of blindness globally. Around 400 million people have this disorder.
Illustrations you can use, just copy/paste Robert Chang, airbnb Data Science in Tech Value of homes on Airbnb
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
Skills Data Scientists Should Have ➔  Asking the right questions ➔  Turning business questions into data science questions (and answers!) ➔  Learning on the fly ➔  Explaining complex results to non-technical stakeholders ➔  Critical thinking and quantitative skills will remain in demand Skills To Focus On
Technologies to know ➔  Excel is a good place to start ➔  SQL for querying data ➔  Python or R for ◆  Analysis ◆  Data visualization ◆  Machine Learning Technologies To Focus On
Technologies to know Technical Skills To Focus On ➔  Data Collection and Cleaning ➔  Building dashboards and reports ➔  Data visualization ➔  Building models (statistical inference & machine learning) ➔  Communicate results to stakeholders ➔  Business decisions are then made!
Concepts to know: data intuition Data intuition ➔  How is data generated? ➔  How is data stored? ➔  What does data feel like?
Concepts to know: statistical intuition Statistical intuition ➔  Thinking probabilistically ➔  Statistical data visualization ➔  Statistical modeling ➔  Statistical biases
Concepts to know: machine learning Machine Learning ➔  Supervised learning ➔  Unsupervised learning ➔  Deep Learning ➔  Artificial Intelligence
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
What Data Scientist Actually Do ➔  Data Collection and Cleaning ➔  Building dashboards and reports ➔  Data visualization ➔  Building models (statistical inference & machine learning) ➔  Communicate results to stakeholders ➔  Business decisions are then made! Today’s data scientist...
Types Of Data Science 1.  Descriptive analytics (Business Intelligence) 2.  Predictive analytics (Machine Learning) 3.  Prescriptive Analytics (Decision Science) Jonathan Nolis breaks data science into 3 components ( Episode 28, DataFramed):
Types Of Data Science ➔  Taking data company already has ➔  Getting that data to the right people ➔  In form of dashboards, reports, emails 1.  Business Intelligence (descriptive analytics)
Types Of Data Science
Types Of Data Science ➔  Put models continuously into production ➔  E.g. LTV at airbnb 2. Machine Learning (predictive analytics)
Types Of Data Science ➔  Take the insight discovered from the data science work ➔  Use it to help company decision making ➔  E.g. what do you if your data science work tells you a particular type of customer will churn? 3. Decision Making (prescriptive analytics)
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
What is Machine Learning? ➔  “the science and art of giving computers the ability to learn to make decisions from data without being explicitly programmed.” ◆  Email: spam or not (supervised) ◆  Text clustering (unsupervised)
Unsupervised Learning ➔  the machine learning task of uncovering hidden patterns and structures from unlabeled data: ◆  E.g. Clustering texts, customers, genes. Hours on website per week Moneyspent(USD/1000)
Unsupervised Learning Hierarchical Clustering: Stock Movements
Supervised Learning ➔  automate a time-consuming or expensive manual task, such as a doctor's diagnosis or ➔  to make predictions about the future, say whether a customer will click on an ad or not.
Prediction ➔  MRIs ➔  CT Scans ➔  Mammograms Medical Imaging Diagnosis
Prediction ➔  How much is any given customer worth throughout their lifetime? AirBnb Customer Lifetime Value
Prediction ➔  How much will a house sell for, given location, number of rooms, area? Housing Prices
Prediction ➔  Given images of an accident, can you tell me the how much will be paid out? Insurance Payouts
Illustrations you can use, just copy/pasteNot Enough
Illustrations you can use, just copy/pasteMaybe Enough
➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
1.  J.A.R.V.I.S. 2.  A type of machine learning 3.  Conscious machines 4.  A catch-all that refers to generally creating systems capable of making intelligent decisions POLL: What do you think AI is?
What AI IS NOT
Illustrations you can use, just copy/pasteWhat is AI? ➔  Is a type of machine learning ➔  Is generally supervised learning ➔  Using algorithms known as deep learning ➔  Good for image classification & natural language processing (NLP)
Illustrations you can use, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
What do employees need to learn?
Skills Employees Should Have ➔  Communication ➔  Technologies (Excel, SQL, Python, R) ➔  Data thinking, data manipulation & visualization ➔  Statistical thinking ➔  Machine Learning Skills To Focus On
Why DataCamp?
DataCamp is an online learning platform that specializes in teaching coding for data science.
Learn by doing ➔  Short videos from expert instructors ➔  In-browser coding ➔  Real-time feedback 200+ Unmatched data science courses ➔  Languages: Python, R, SQL, Git, Shell, Spreadsheets ➔  Topics: Importing & Cleaning, Data Manipulation, Visualization, Probability & Statistics, Machine Learning, and more! Industry-leading instructors ➔  Learn from the authors of renowned code packages and the organizations that understand data science innovation Learn by Doing
Where Does DataCamp Fit In? Monica Rogati, The AI Hierarchy of Needs - Language Basics - Importing Data - Cleaning Data in Python - Data Manipulation (pandas) - Data Visualization - Case Studies & Reinforcement - Statistical Thinking - Supervised Learning - Unsupervised Learning - Deep Learning
Thank you! Email sales@datacamp.com with the subject “Webinar” for a free trial for your company Hugo Bowne-Anderson Data Scientist @hugobowne
#CLOwebinar Register for the next webinar! How do you Identify a Good Manager? Tuesday, July 30, 2019 Webinars start at 2 p.m. Eastern / 11 a.m. Pacific Register for all upcoming Chief Learning Officer Webinars at clomedia.com/webinars

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What your employees need to learn to work with data in the 21 st century

  • 1. #CLOwebinar The presentation will begin at the top of the hour. A dial in number will not be provided. Listen to today’s webinar using your computer’s speakers or headphones. Welcome to the webinar!
  • 2. #CLOwebinar Tools You Can Use Audio Control –  A dial in number will not be provided. –  The audio will stream through your headphones or computer speakers. –  Also check your computer’s volume for external speakers or headsets.
  • 4. #CLOwebinar 1. May I receive a copy of the slides? YES! Click on the handouts located on the right portion of your screen. 2. May I review the webinar recording at a later date? YES! You may log in again using today’s link to review the presentation on-demand. 3. Is this webinar HRCI or SHRM certified? YES! The HRCI and SHRM certification codes will be sent in the follow up email. Frequently Asked Questions
  • 5. 07/23/2019 What Employees Need to Learn to Work With Data in the 21st Century Hugo Bowne-Anderson @hugobowne
  • 6. Illustrations you can use, just copy/paste ➔  Hugo Bowne-Anderson, data scientist at DataCamp ◆  Undergrad in sciences/humanities (double math major) ◆  PhD in Pure Mathematics (UNSW, Sydney) ◆  Applied math research in cell biology (Yale University, Max Planck Institute) ◆  Python curriculum engineer at DataCamp ◆  Host of DataFramed, the DataCamp podcast A bit about me
  • 7. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 8. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 9. “More than anything, what data scientists do is make discoveries while swimming in data.” -- Thomas Davenport & DJ Patil
  • 10. “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.” -- Wikipedia
  • 11. Illustrations you can use, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
  • 12. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 13. Where is data science in the Gartner Hype cycle?
  • 14. 1.  Innovation trigger 2.  Peak of inflated expectations 3.  Trough of disillusionment 4.  Slope of enlightenment POLL: Where do you think data science is in the Gartner Hype Cycle?
  • 15. Where is big data in the Gartner Hype cycle?
  • 16. Where is data science in the Gartner Hype cycle?
  • 17. Where is data science in the Gartner Hype cycle?
  • 18. What will the slope of enlightenment look like? “Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?” -- DataCamp in HBR
  • 19. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 20. Why You Should Care About Data Science ➔  When so much data is informing decisions across so many industries, you need to have a basic understanding of the data ecosystem in order to be part of the conversation. ➔  On top of this, the industry that you work in will more than likely see the impact of data analytics.
  • 21. Illustrations you can use, just copy/pasteWhy data science? Poised to Disrupt All Industries
  • 22. 1.  The drones are spraying pesticides 2.  The farmer’s children are using drone footage to add to their Instagram stories 3.  Footage captured by the drone is utilized to optimize crop yields POLL: How is data science being used in this image?
  • 23. Illustrations you can use, just copy/pasteData Science in Finance Stock market prediction
  • 24. Illustrations you can use, just copy/pasteData Science in Finance ➔  “Traders used to be first-class citizens of the financial world, but that’s not true anymore. Technologists are the priority now…” -- Robin Wigglesworth
  • 25. Illustrations you can use, just copy/pasteData Science in Finance ➔  “...with a little bit of training, you can accomplish quite a lot compared to the traditional approaches in this field.” -- Yves Hilpisch
  • 26. Illustrations you can use, just copy/paste Flatiron Health Data Science in Healthcare Cancer patient survival probability
  • 27. Data Science in Healthcare ➔  Disease diagnosis from imaging data (e.g. MRI, PET)
  • 28. Data Science in Healthcare ➔  Google AI have developed an algorithm to detect diabetic retinopathy as well as experts ➔  One of the leading causes of blindness globally. Around 400 million people have this disorder.
  • 29. Illustrations you can use, just copy/paste Robert Chang, airbnb Data Science in Tech Value of homes on Airbnb
  • 30. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 31. Skills Data Scientists Should Have ➔  Asking the right questions ➔  Turning business questions into data science questions (and answers!) ➔  Learning on the fly ➔  Explaining complex results to non-technical stakeholders ➔  Critical thinking and quantitative skills will remain in demand Skills To Focus On
  • 32. Technologies to know ➔  Excel is a good place to start ➔  SQL for querying data ➔  Python or R for ◆  Analysis ◆  Data visualization ◆  Machine Learning Technologies To Focus On
  • 33. Technologies to know Technical Skills To Focus On ➔  Data Collection and Cleaning ➔  Building dashboards and reports ➔  Data visualization ➔  Building models (statistical inference & machine learning) ➔  Communicate results to stakeholders ➔  Business decisions are then made!
  • 34. Concepts to know: data intuition Data intuition ➔  How is data generated? ➔  How is data stored? ➔  What does data feel like?
  • 35. Concepts to know: statistical intuition Statistical intuition ➔  Thinking probabilistically ➔  Statistical data visualization ➔  Statistical modeling ➔  Statistical biases
  • 36. Concepts to know: machine learning Machine Learning ➔  Supervised learning ➔  Unsupervised learning ➔  Deep Learning ➔  Artificial Intelligence
  • 37. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 38. What Data Scientist Actually Do ➔  Data Collection and Cleaning ➔  Building dashboards and reports ➔  Data visualization ➔  Building models (statistical inference & machine learning) ➔  Communicate results to stakeholders ➔  Business decisions are then made! Today’s data scientist...
  • 39. Types Of Data Science 1.  Descriptive analytics (Business Intelligence) 2.  Predictive analytics (Machine Learning) 3.  Prescriptive Analytics (Decision Science) Jonathan Nolis breaks data science into 3 components ( Episode 28, DataFramed):
  • 40. Types Of Data Science ➔  Taking data company already has ➔  Getting that data to the right people ➔  In form of dashboards, reports, emails 1.  Business Intelligence (descriptive analytics)
  • 41. Types Of Data Science
  • 42. Types Of Data Science ➔  Put models continuously into production ➔  E.g. LTV at airbnb 2. Machine Learning (predictive analytics)
  • 43. Types Of Data Science ➔  Take the insight discovered from the data science work ➔  Use it to help company decision making ➔  E.g. what do you if your data science work tells you a particular type of customer will churn? 3. Decision Making (prescriptive analytics)
  • 44. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 45. What is Machine Learning? ➔  “the science and art of giving computers the ability to learn to make decisions from data without being explicitly programmed.” ◆  Email: spam or not (supervised) ◆  Text clustering (unsupervised)
  • 46. Unsupervised Learning ➔  the machine learning task of uncovering hidden patterns and structures from unlabeled data: ◆  E.g. Clustering texts, customers, genes. Hours on website per week Moneyspent(USD/1000)
  • 48. Supervised Learning ➔  automate a time-consuming or expensive manual task, such as a doctor's diagnosis or ➔  to make predictions about the future, say whether a customer will click on an ad or not.
  • 49. Prediction ➔  MRIs ➔  CT Scans ➔  Mammograms Medical Imaging Diagnosis
  • 50. Prediction ➔  How much is any given customer worth throughout their lifetime? AirBnb Customer Lifetime Value
  • 51. Prediction ➔  How much will a house sell for, given location, number of rooms, area? Housing Prices
  • 52. Prediction ➔  Given images of an accident, can you tell me the how much will be paid out? Insurance Payouts
  • 53. Illustrations you can use, just copy/pasteNot Enough
  • 54. Illustrations you can use, just copy/pasteMaybe Enough
  • 55. ➔  What is data science? ➔  Where will the data revolution occur? ➔  Why data science? ➔  What do employees need to learn? ➔  What are the moving parts of data science? ➔  What is machine learning? ➔  What is AI? Today’s topics of discussion
  • 56. 1.  J.A.R.V.I.S. 2.  A type of machine learning 3.  Conscious machines 4.  A catch-all that refers to generally creating systems capable of making intelligent decisions POLL: What do you think AI is?
  • 57. What AI IS NOT
  • 58. Illustrations you can use, just copy/pasteWhat is AI? ➔  Is a type of machine learning ➔  Is generally supervised learning ➔  Using algorithms known as deep learning ➔  Good for image classification & natural language processing (NLP)
  • 59. Illustrations you can use, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
  • 60. What do employees need to learn?
  • 61. Skills Employees Should Have ➔  Communication ➔  Technologies (Excel, SQL, Python, R) ➔  Data thinking, data manipulation & visualization ➔  Statistical thinking ➔  Machine Learning Skills To Focus On
  • 63. DataCamp is an online learning platform that specializes in teaching coding for data science.
  • 64. Learn by doing ➔  Short videos from expert instructors ➔  In-browser coding ➔  Real-time feedback 200+ Unmatched data science courses ➔  Languages: Python, R, SQL, Git, Shell, Spreadsheets ➔  Topics: Importing & Cleaning, Data Manipulation, Visualization, Probability & Statistics, Machine Learning, and more! Industry-leading instructors ➔  Learn from the authors of renowned code packages and the organizations that understand data science innovation Learn by Doing
  • 65. Where Does DataCamp Fit In? Monica Rogati, The AI Hierarchy of Needs - Language Basics - Importing Data - Cleaning Data in Python - Data Manipulation (pandas) - Data Visualization - Case Studies & Reinforcement - Statistical Thinking - Supervised Learning - Unsupervised Learning - Deep Learning
  • 66. Thank you! Email sales@datacamp.com with the subject “Webinar” for a free trial for your company Hugo Bowne-Anderson Data Scientist @hugobowne
  • 67. #CLOwebinar Register for the next webinar! How do you Identify a Good Manager? Tuesday, July 30, 2019 Webinars start at 2 p.m. Eastern / 11 a.m. Pacific Register for all upcoming Chief Learning Officer Webinars at clomedia.com/webinars