Str u ct u re & optimal flo w C OU R SE C R E ATION AT DATAC - - PowerPoint PPT Presentation

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Str u ct u re & optimal flo w C OU R SE C R E ATION AT DATAC - - PowerPoint PPT Presentation

May 31, 2023 278 likes •784 views

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 &

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Structure & optimal flow

C OU R SE C R E ATION AT DATAC AMP

David Campos

Content Developer

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COURSE CREATION AT DATACAMP

Contents

Structure and ow of: Course Chapters Lessons Development ow

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Structure & flow of courses

A series of lessons that teach a distinct subject e.g. Introduction to the Tidyverse e.g. Machine Learning for Finance in Python Courses are broken into chapters

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Structure & flow of courses

Chapter 1 Motivation Transition into core topics Cli hanger Chapter 2 and 3 Core topics Chapter 4 Bringing it all together Congratulations, summarize, conclude

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Structure & flow of chapters

A series of lessons Related topics Chapters are composed by lessons

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Structure & flow of lessons

DataCamp lessons Video Exercise subtopics: A, B, C Interactive Exercise (Coding, Multiple Choice, etc) subtopic: A Interactive Exercise (Coding, Multiple Choice, etc) subtopic: B Interactive Exercise (Coding, Multiple Choice, etc) subtopic: C

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Structure & flow of lessons

Why (problem): Why am I learning this? What real-world problem is this learning objective aempting to solve? What (solution): What solution am I going to implement to solve such real-world problem? How (implementation): How do I implement the solution? Examples

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Flow of a Video exercise

Problem (why) Dataset (what) Technical solution (what) Solution implementation (how)

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Development flow

Lesson-by-lesson Chronological order Accelerates course development Ensures narrative progression Engages learners

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Let's practice!

C OU R SE C R E ATION AT DATAC AMP

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Defining learning

  • bjectives

C OU R SE C R E ATION AT DATAC AMP

Hillary Green-Lerman

Senior Curriculum Lead

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Clear learning objectives

States measurable outcome "Learner will be able to <verb> ..." Helps you scope your lesson

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Measurable learning objectives

Do This Not That Learner will be able to create a variable that represents a string Learner will know what a variable is

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Specific learning objectives

Keep lessons concise and engaging Helps DataCamp beer assess your course outline Do This Not That Learner will be able to classify sentences as “positive”, “negative”,

  • r “neutral” using NLTK’s sentiment analysis

Learner will perform sentiment analysis

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Learning objectives solve a problem

Motivate each learning objective with a real-world application Do This Not That Learner will be able to construct reusable code blocks using functions Learner will be able to create functions Learner will be able to create histograms to compare datasets with similar means, but dierent distributions Learner will be able to create histograms

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Learning by doing

Theory is nice; practice is beer Do this Not that Ask learners to build and t a linear regression model to a dataset of heights and weights Ask a multiple choice question about which linear model best ts a dataset Use scikit-learn to train a decision tree model Code a decision tree Class from scratch

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Expertise & industry insights

Choose engaging, real-world scenarios Do this Not that Create a histogram based on the radii of dierent tumors from a Kaggle dataset Create a histogram based on random numbers generated by a normal distribution

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Expertise & industry insights

Do This:

# Give variables relevant names everest_height = 29029 heightest_peaks = ['Mount Everest', 'K2', 'Kangchenjunga']

Not this:

# Give variables generic names foo = 12 my_list = ['item1', 'item2', 'item3']

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Summary

Write clear, measurable learning objectives Encourage learning by doing Incorporate expertise and industry experience

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Let's practice!

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Optimizing for digital learning

C OU R SE C R E ATION AT DATAC AMP

Mona Khalil

Curriculum Lead

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Online vs. offline learning

One-sided engagement Dierent student demographics Student t learning around busy schedule

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Prerequisites

Identify learners with optimal qualications Select 1 - 3 prerequisites Introductory courses may have fewer Case studies and advanced courses may have more

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Course Roadmap

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Analogy: a similarity or comparison between two objects

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Heuristic: a mental shortcut that eases the cognitive load of information

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Let's practice!

C OU R SE C R E ATION AT DATAC AMP

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Excitement is contagious!

C OU R SE C R E ATION AT DATAC AMP

Adrián Soto

Content Developer

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Your best teachers

What are their characteristics? Knowledgeable and well-prepared Patient and willing to help Able to turn dicult things into simple things Eective at communication Engaging and interactive Excited and enthusiastic

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Your best teachers

What are their characteristics? Knowledgeable and well-prepared Patient and willing to help Able to turn dicult things into simple things Eective at communication Engaging and interactive Excited and enthusiastic

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Why do you remember them?

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General devices

Use compelling examples DataCamp students love real-life applications! Be curious. Invite students to join Use interesting datasets Use alluring titles for slides for exercises for chapters

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Videos: Some tricks

Motivate your materials around the "why"

Source: Knorr 2009

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Videos: Some tricks

Motivate your materials around the "why" Congratulate students for their success Ask questions Tell stories! It's okay to be funny Invite students to practice Sound enthusiastic!

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Exercises: Motivation

Motivate exercise usefulness

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Exercises: Hints

No room for condescendence Keep a positive tone

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Exercises: Success messages

Praise, but not too much Highlight interesting outcomes or ndings

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Exciting times!

C OU R SE C R E ATION AT DATAC AMP