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Str u ct u re & optimal flo w C OU R SE C R E ATION AT DATAC AMP Da v id Campos Content De v eloper Contents Str u ct u re and o w of : Co u rse Chapters Lessons De v elopment o w COURSE CREATION AT DATACAMP Str u ct u re &


  1. Str u ct u re & optimal flo w C OU R SE C R E ATION AT DATAC AMP Da v id Campos Content De v eloper

  2. Contents Str u ct u re and � o w of : Co u rse Chapters Lessons De v elopment � o w COURSE CREATION AT DATACAMP

  3. Str u ct u re & flo w of co u rses A series of lessons that teach a distinct s u bject e . g . Introd u ction to the Tid yv erse e . g . Machine Learning for Finance in P y thon Co u rses are broken into chapters COURSE CREATION AT DATACAMP

  4. Str u ct u re & flo w of co u rses Chapter 1 Moti v ation Transition into core topics Cli � hanger Chapter 2 and 3 Core topics Chapter 4 Bringing it all together Congrat u lations , s u mmari z e , concl u de COURSE CREATION AT DATACAMP

  5. Str u ct u re & flo w of chapters A series of lessons Related topics Chapters are composed b y lessons COURSE CREATION AT DATACAMP

  6. Str u ct u re & flo w of lessons DataCamp lessons Video E x ercise s u btopics : A , B , C Interacti v e E x ercise ( Coding , M u ltiple Choice , etc ) s u btopic : A Interacti v e E x ercise ( Coding , M u ltiple Choice , etc ) s u btopic : B Interacti v e E x ercise ( Coding , M u ltiple Choice , etc ) s u btopic : C COURSE CREATION AT DATACAMP

  7. Str u ct u re & flo w of lessons Wh y ( problem ): Wh y am I learning this ? What real -w orld problem is this learning objecti v e a � empting to sol v e ? What ( sol u tion ): What sol u tion am I going to implement to sol v e s u ch real -w orld problem ? Ho w ( implementation ): Ho w do I implement the sol u tion ? E x amples COURSE CREATION AT DATACAMP

  8. Flo w of a Video e x ercise Problem (w h y) Dataset (w hat ) Technical sol u tion (w hat ) Sol u tion implementation ( ho w) COURSE CREATION AT DATACAMP

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  13. De v elopment flo w Lesson - b y- lesson Chronological order Accelerates co u rse de v elopment Ens u res narrati v e progression Engages learners COURSE CREATION AT DATACAMP

  14. Let ' s practice ! C OU R SE C R E ATION AT DATAC AMP

  15. Defining learning objecti v es C OU R SE C R E ATION AT DATAC AMP Hillar y Green - Lerman Senior C u rric u l u m Lead

  16. Clear learning objecti v es States meas u rable o u tcome " Learner w ill be able to <v erb > ..." Helps y o u scope y o u r lesson COURSE CREATION AT DATACAMP

  17. Meas u rable learning objecti v es Do This Not That Learner w ill be able to create a v ariable that represents a Learner w ill kno w w hat a string v ariable is COURSE CREATION AT DATACAMP

  18. Specific learning objecti v es Keep lessons concise and engaging Helps DataCamp be � er assess y o u r co u rse o u tline Do This Not That Learner w ill be able to classif y sentences as “ positi v e ”, “ negati v e ”, Learner w ill perform or “ ne u tral ” u sing NLTK ’ s sentiment anal y sis sentiment anal y sis COURSE CREATION AT DATACAMP

  19. Learning objecti v es sol v e a problem Moti v ate each learning objecti v e w ith a real -w orld application Do This Not That Learner w ill be able to constr u ct re u sable code blocks u sing Learner w ill be able to f u nctions create f u nctions Learner w ill be able to create histograms to compare datasets Learner w ill be able to w ith similar means , b u t di � erent distrib u tions create histograms COURSE CREATION AT DATACAMP

  20. Learning b y doing Theor y is nice ; practice is be � er Do this Not that Ask learners to b u ild and � t a linear regression Ask a m u ltiple choice q u estion abo u t model to a dataset of heights and w eights w hich linear model best � ts a dataset Use scikit - learn to train a decision tree model Code a decision tree Class from scratch COURSE CREATION AT DATACAMP

  21. E x pertise & ind u str y insights Choose engaging , real -w orld scenarios Do this Not that Create a histogram based on the radii of Create a histogram based on random di � erent t u mors from a Kaggle dataset n u mbers generated b y a normal distrib u tion COURSE CREATION AT DATACAMP

  22. E x pertise & ind u str y insights Do This : Not this : # Give variables relevant names # Give variables generic names everest_height = 29029 foo = 12 heightest_peaks = ['Mount Everest', my_list = ['item1', 'K2', 'item2', 'Kangchenjunga'] 'item3'] COURSE CREATION AT DATACAMP

  23. S u mmar y Write clear , meas u rable learning objecti v es Enco u rage learning b y doing Incorporate e x pertise and ind u str y e x perience COURSE CREATION AT DATACAMP

  24. Let ' s practice ! C OU R SE C R E ATION AT DATAC AMP

  25. Optimi z ing for digital learning C OU R SE C R E ATION AT DATAC AMP Mona Khalil C u rric u l u m Lead

  26. Online v s . offline learning One - sided engagement Di � erent st u dent demographics St u dent � t learning aro u nd b u s y sched u le COURSE CREATION AT DATACAMP

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  33. Prereq u isites Identif y learners w ith optimal q u ali � cations Select 1 - 3 prereq u isites Introd u ctor y co u rses ma y ha v e fe w er Case st u dies and ad v anced co u rses ma y ha v e more COURSE CREATION AT DATACAMP

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  35. Co u rse Roadmap COURSE CREATION AT DATACAMP

  36. Analog y : a similarit y or comparison bet w een t w o objects COURSE CREATION AT DATACAMP

  37. He u ristic : a mental shortc u t that eases the cogniti v e load of information COURSE CREATION AT DATACAMP

  38. Let ' s practice ! C OU R SE C R E ATION AT DATAC AMP

  39. E x citement is contagio u s ! C OU R SE C R E ATION AT DATAC AMP Adrián Soto Content De v eloper

  40. Yo u r best teachers What are their characteristics ? Kno w ledgeable and w ell - prepared Patient and w illing to help Able to t u rn di � c u lt things into simple things E � ecti v e at comm u nication Engaging and interacti v e E x cited and enth u siastic COURSE CREATION AT DATACAMP

  41. Yo u r best teachers What are their characteristics ? Kno w ledgeable and w ell - prepared Patient and w illing to help Able to t u rn di � c u lt things into simple things E � ecti v e at comm u nication Engaging and interacti v e E x cited and enth u siastic COURSE CREATION AT DATACAMP

  42. Wh y do y o u remember them ? COURSE CREATION AT DATACAMP

  43. General de v ices Use compelling e x amples DataCamp st u dents lo v e real - life applications ! Be c u rio u s . In v ite st u dents to join Use interesting datasets Use all u ring titles for slides for e x ercises for chapters COURSE CREATION AT DATACAMP

  44. Videos : Some tricks Moti v ate y o u r materials aro u nd the "w h y" Source: Knorr 2009 COURSE CREATION AT DATACAMP

  45. Videos : Some tricks Moti v ate y o u r materials aro u nd the "w h y" Congrat u late st u dents for their s u ccess Ask q u estions Tell stories ! It ' s oka y to be f u nn y In v ite st u dents to practice So u nd enth u siastic ! COURSE CREATION AT DATACAMP

  46. E x ercises : Moti v ation Moti v ate e x ercise u sef u lness COURSE CREATION AT DATACAMP

  47. E x ercises : Hints No room for condescendence Keep a positi v e tone COURSE CREATION AT DATACAMP

  48. E x ercises : S u ccess messages Praise , b u t not too m u ch Highlight interesting o u tcomes or � ndings COURSE CREATION AT DATACAMP

  49. E x citing times ! C OU R SE C R E ATION AT DATAC AMP

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