Video e x ercises : Slides & transcript C OU R SE C R E ATION - - PowerPoint PPT Presentation

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Video e x ercises : Slides & transcript C OU R SE C R E ATION - - PowerPoint PPT Presentation

Apr 11, 2024 300 likes •1.03k views

Video e x ercises : Slides & transcript C OU R SE C R E ATION AT DATAC AMP Hadrien Lacroi x Content De v eloper Video e x ercises First part of a lesson Learning objecti v e Presentation COURSE CREATION AT DATACAMP COURSE CREATION AT

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Video exercises: Slides & transcript

C OU R SE C R E ATION AT DATAC AMP

Hadrien Lacroix

Content Developer

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Video exercises

First part of a lesson Learning objective Presentation

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Structured scripts

Exact words recorded What → Why → How Sentences should ow naturally Be brief (ideally 400-500 words) Max 600 words

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Build a narrative

Storytelling Be ADEPT

  • 1. Use an Analogy
  • 2. Draw a Diagram
  • 3. Provide a concrete Example
  • 4. Describe it in Plain English
  • 5. Provide a Technical denition
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Timeless scripts

Don't Do Manipulating Time Series Data in Python course on time series manipulation in Python I'm a Data Scientist at Data Company X I'm a Data Scientist I'm writing a book on Machine Learning Follow me on Twier for updates The current version of this package... <Focus on the utility, not the implementation>

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Dynamic slides

Dynamic: Add animations to lists Add animations to code Avoid "deadtime" Add movement (at least) every 30 seconds Animate bullet points

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Bullet points: What you should NOT do

Machine learning workow Data exploration Data processing Modeling Evaluation Improvements

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Bullet points: What you should do

Machine Learning workow

  • 1. Data exploration {{1}} →
  • 2. Data processing {{2}} →
  • 3. Modeling {{3}} →
  • 4. Evaluation {{4}} →
  • 5. Improvements {{5}} →

Machine Learning workow

  • 1. Data exploration
  • 2. Data processing
  • 3. Modeling
  • 4. Evaluation
  • 5. Improvements
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Code: What you should NOT do

# Define trainControl function fitControl <- trainControl(method = "adaptive_cv", number = 3, repeats = 3, adaptive = list(min = 3, alpha = 0.05, method = "BT", complete = FALSE), search = "random") # Start timer tic() # Train model svm_model_voters_ar <- train(turnout16_2016 ~ ., data = voters_train_data, method = "svmPoly", trControl = fitControl,

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Code: What you should do

# Define trainControl function fitControl <- trainControl(method = "adaptive_cv", number = 3, repeats = 3, adaptive = list(min = 3, alpha = 0.05, method = "BT", complete = FALSE), search = "random") # Start timer tic() # Train model svm_model_voters_ar <- train(turnout16_2016 ~ ., data = voters_train_data, method = "svmPoly", trControl = fitControl, verbose = FALSE, tuneLength = 6) # Stop timer toc()

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Syntax highlighting

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Syntax highlighting

Input only: {python}, {r}

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Images: Building a diagram (1)

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Images: Building a diagram (2)

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Images: Building a diagram (3)

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Images: Building a diagram (4)

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Images: Building a diagram (5)

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Let's practice!

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Interactive exercises

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Sara Billen

Content Developer

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Exercise types

Multiple Choice exercise Multiple Choice with Console exercise Coding exercise Iterative exercise Sequential exercise

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Writing good sample code

Sample code

# Open a file: file file = open(____, mode='____') # Print it ____ # Check whether file is closed ____(file.closed) # Close file ()

Solution code

# Open a file: file file = open('moby_dick.txt, mode='r') # Print it print(file.read()) # Check whether file is closed print(file.closed) # Close file ()

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Let’s start exercising!

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Respect the guidelines

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Shon Inouye

Content Quality Analyst

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Objective of content guidelines

Goal: Enable students to learn data science in a fun and engaging way Content Guidelines: Ensure that all content we develop is in sync with our goal Provide users with the best possible user experience

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Reasoning: Consistency

DataCamp has a rapidly expanding user base and content library Consistency across content to maintain product vision in a scalable fashion

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Reasoning: Optimization for engagement

Data collection on student interactions with DataCamp allows us to explore: Completion rate Percentage of students asking for hints/solutions Course rating Insight from pedagogical (teaching-related) research MIT study recommending video length to be < 6 minutes

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Instruction Length % Asked Hint % Asked Solution Exercise Completion Rate % of Exercises (0, 100] 0.08 0.05 0.9971 5 (100, 200] 0.14 0.10 0.9926 16 (200, 300] 0.17 0.13 0.9922 21 (300, 400] 0.21 0.16 0.9898 20 (400, 500] 0.24 0.18 0.9889 15 (500, 600] 0.26 0.20 0.9886 10 Drives completion rates and % students asking for hints/solutions 99 71% i l ti

(0 9971)

84% l ti

60

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Other guidelines

Guideline Reasoning Aim for courses to be completable within 4 hours Shorter courses are more digestible and have higher completion rates Limit exercises to 15 lines of sample/solution code Ensures that instructions t well within a page and limits scrolling Limit the number of multiple choice questions in a course to no more than 5 Ensures learners do most of their learning by doing

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Resources

Content Guidelines Course Editor features for Content Guidelines

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Let's practice!

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GitHub for course review

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Jeroen Hermans

Head of Content Engineering

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Back to the repo

GitHub Guides

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Pull requests (PR)

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Pull requests (PR)

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Pull requests (PR)

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Pull requests (PR)

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Merge conflicts

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Diffs

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Review comments

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Resolve comments

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Let's practice!

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