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What your employees need to learn to work with data in the 21 st century
The webinar, hosted by Hugo Bowne-Anderson, focuses on the importance of data science in the 21st century, outlining its definitions, applications, and necessary skills for employees. Key topics include the significance of understanding data to make informed decisions across industries and the skills data scientists should possess, such as communication, statistical thinking, and programming knowledge. Participants can access slides, recordings, and certification codes after the session.
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What your employees need to learn to work with data in the 21 st century
- 1. #CLOwebinar The presentation willbegin 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 YouCan 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.
- 3.
- 4. #CLOwebinar 1. MayI 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 Needto Learn to Work With Data in the 21st Century Hugo Bowne-Anderson @hugobowne
- 6. Illustrations you canuse, 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 isdata 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 isdata 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 isan 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 canuse, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
- 12. ➔ What isdata 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 datascience 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 bigdata in the Gartner Hype cycle?
- 16. Where is datascience in the Gartner Hype cycle?
- 17. Where is datascience in the Gartner Hype cycle?
- 18. What will theslope 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 isdata 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 ShouldCare 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 canuse, just copy/pasteWhy data science? Poised to Disrupt All Industries
- 22. 1. The dronesare 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 canuse, just copy/pasteData Science in Finance Stock market prediction
- 24. Illustrations you canuse, 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 canuse, 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 canuse, just copy/paste Flatiron Health Data Science in Healthcare Cancer patient survival probability
- 27. Data Science inHealthcare ➔ Disease diagnosis from imaging data (e.g. MRI, PET)
- 28. Data Science inHealthcare ➔ 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 canuse, just copy/paste Robert Chang, airbnb Data Science in Tech Value of homes on Airbnb
- 30. ➔ What isdata 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 ScientistsShould 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 TechnicalSkills 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 isdata 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 ScientistActually 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 DataScience 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 DataScience ➔ 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 DataScience
- 42. Types Of DataScience ➔ Put models continuously into production ➔ E.g. LTV at airbnb 2. Machine Learning (predictive analytics)
- 43. Types Of DataScience ➔ 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 isdata 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 MachineLearning? ➔ “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 ➔ themachine 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)
- 47. Unsupervised Learning Hierarchical Clustering:Stock Movements
- 48. Supervised Learning ➔ automatea 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 ➔ CTScans ➔ Mammograms Medical Imaging Diagnosis
- 50. Prediction ➔ How muchis any given customer worth throughout their lifetime? AirBnb Customer Lifetime Value
- 51. Prediction ➔ How muchwill a house sell for, given location, number of rooms, area? Housing Prices
- 52. Prediction ➔ Given imagesof an accident, can you tell me the how much will be paid out? Insurance Payouts
- 53. Illustrations you canuse, just copy/pasteNot Enough
- 54. Illustrations you canuse, just copy/pasteMaybe Enough
- 55. ➔ What isdata 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. Atype 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 ISNOT
- 58. Illustrations you canuse, 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 canuse, just copy/paste Monica Rogati, The AI Hierarchy of Needs Data Science Hierarchy of Needs
- 60. What do employeesneed to learn?
- 61. Skills Employees ShouldHave ➔ Communication ➔ Technologies (Excel, SQL, Python, R) ➔ Data thinking, data manipulation & visualization ➔ Statistical thinking ➔ Machine Learning Skills To Focus On
- 62.
- 63. DataCamp is anonline 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 DataCampFit 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.comwith the subject “Webinar” for a free trial for your company Hugo Bowne-Anderson Data Scientist @hugobowne
- 67. #CLOwebinar Register forthe 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