Building your data academy
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The document discusses building objectives for a data academy, emphasizing the need for data analytics training and the involvement of subject matter experts. It covers challenges in creating a formal data education structure, the importance of unifying processes and culture, and strategies for assessing and delivering effective data training. Additionally, it addresses the value of fostering a data-driven organization and future-proofing the data academy.
![PARSONS PROBLEMS
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Building your data academy
- 3. WHERE IS HE COMING FROM? • • •
- 4. LET'S TALK WHERE ARE YOU COMING FROM? DATA ANALYTICS TRAINING? 🤷♂️ SUBJECT MATTER EXPERTS CONTENT DEVELOPERS TECHNICAL WRITERS LEARNING & DEVELOPMENT INSTRUCTIONAL DESIGNERS
- 6. ROLL CREDITS? NOT SO FAST
- 7. STEP 2: AI- POWERED ENTERPRISE STEP 1: CONNECT COMPUTER TO PRINTER
- 8. IT’S NOT MAGIC, YET
- 9. PEOPLE DON’T LIKE IT
- 10. DATA ANALYSTS WANT BETTER
- 11. THE ANALYSIS VS THE SCIENCE
- 12. QUESTIONS?
- 15. WHY DO THIS IN-HOUSE? WHY DO THIS WHEN THERE’S YOUTUB… OUR BUSINESS DEPENDS ON UNIFIED PROCESSES AND CULTURE!
- 16. WHY BUILD SOMETHING FORMAL? WHY DO THIS WHEN GARY IN ACCOUNTING IS AN EXCEL WIZAR.. SO GARY IS HEAD OF A DATA ACADEMY NOW, TOO?
- 17. WHY NOT JUST HIRE GENIUSES? WHY DO THIS WHEN WE CAN MAKE A DATA SCIENCE TEA… BY THAT POINT WE COULD HAVE TRAINED 100 NEW DATA ANALYSTS FOR HALF THE COST!
- 18. CUTTING THE DATA BREADLINE • • • •
- 19. IT ALL GOES BACK TO MESSY DATA
- 20. QUESTIONS?
- 22. FINDING YOUR SUBJECT MATTER EXPERTS
- 23. IDENTIFYING YOUR DATA ACADEMITES
- 27. THE DATA ACADEMY STACK
- 28. CREATING THE DATA ACADEMY • • •
- 29. DATA EDUCATION AS ROBOT-PROOFER • • • • •
- 30. DATA EDUCATION AS MODE OF INQUIRY • • • • •
- 31. DELIVERING A UNIFIED EXPERIENCE • • • •
- 32. RESPECT EXTANT KNOWLEDGE WHY DO THIS WHEN WE CAN MAKE A DATA SCIENCE TEA… BY THAT POINT WE COULD HAVE TRAINED 100 NEW DATA ANALYSTS FOR HALF THE COST! UGH, THEY’RE USING EXCEL! HOW PEDESTRIA… DATA IS DATA! LEVERAGE THAT KNOWLEDGE RELATIONALLY!
- 33. BUY THE HOOK AND YOU’LL BUY THE... • • • • •
- 34. GOT GUI’S? GET GIF’S Ctrl + T
- 36. AVOID THE DREAD BLANK PAGE
- 37. PARSONS PROBLEMS return [a_list[0], a_list[-1]] return [a_list.first, a_list.last] def list_ends(a_list): def list ends(a_list):
- 38. FADED EXAMPLES (SCAFFOLDING) iris.csv import ____ as pd # Read in iris.csv ____ = pd.____(____) # Print first ten rows iris.____ import pandas as pd # Read in iris.csv iris = pd.read_csv(‘iris.csv’) # Print first ten rows iris.head(10)
- 40. PAIR PROGRAMMING: DOUBLE YOUR FUN WITH DATA
- 41. QUESTIONS?
- 43. IT’S YOUR WORLD… THE DATA IS LIVING IN IT • • •
- 45. YOU REALLY DIDN’T THINK YOU WOULDN’T SEE THIS, RIGHT?
- 46. OPEN-SOURCE YOUR DATA PROCESSES
- 47. CONCLUSION
- 48. • Creating a Data-Driven Organization by Carl Anderson
- 49. • Teaching Tech Together by Greg Wilson
- 50. • “Let’s start a data academy!” by Johns Hopkins/GovEx
- 52. LET'S TALK
- 53. QUESTIONS?