Introduction

English or Danish?

• Purpose and background
• Plan / topics
• Structure
• Introduction to Jupyter Notebooks

About

• Purpose: your learning
  Applicable tools and useful knowledge in studies, research, and professionally
• My background: Own company, consultant, and software development. Computer Scientist from KU, including library and information science and teaching. I am here, because I want to pass on my experience
• Your Background: Semester? IT-experience? Interests/passions? Projects?

Topics / plan

• Welcome and introduction to Jupyter
• Fetching and working with data
• Wordcloud visualisation (and learning)
• Computer language - (and tactics vs strategy)
• Structured data (JSON)
• WWW / Internet - HTTP
• Data Science - literature case study*
• Digital images*
• Your topic!*
• Conclusion

Structure

Exploring a series of topics.

• Theory / lecture / perspective
• Examples - follow along
• Exercises, free experimentation, including break
• Follow-up

Do interrupt and ask questions!

What is Jupyter Notebook

• Tool for working with data
• Primary tool within science
• Web application, cloud + local

Examples

• Opening Jupyter Notebooks
• Jupyter tour: modes, cells, text and code, builtin help, errors, kernel
• Calculation with data

Exercises

• User interface tour
• Exercises from Introduction to Jupyter (in Danish, from data-science-workshop.com)

Follow up on exercises

Computer language

Analogy - a different country:
Pointing and gestures vs language

GUI vs CLI

Examples of computer languages

Examples

Information overload...

• calculating with data
• debugging
• importing and using functionality: request, frequency, random
• random word
• random jargon entry
• word frequencies
• naming

Exercises

• fetch urls, and print content
• generate random sentences, - like random words, but with subject-verb-object words
• random fortune-quote
• word frequencies from online book of choice

Follow up on exercises

Blooms taxonomy

Examples

• wordcloud
  • with own words
  • with popular words from book
  • with stop-words

Exercises

• Wordcloud
  • with own words
  • with popular words from data source of choice

Follow up on exercises

What is data

• Computer = calculator
• All is numbers
• Recipies

Tactics and Strategy

• Overview vs detail
• Pair programming
• Tip for working together

Examples

• definitions
• for loops
  • all word combinations
• list comprehension
• refactoring code

Exercises

• repeated random sentences / words
• refactor code from previous exercises

Follow up on exercises

JSON

• strings
• numbers
• true / false (Boolean values)
• lists
• dictionaries

Data structures

Nested lists and dictionaries

• bibliography example
• person / social media example

Examples

• Fetching and accessing JSON-data:
  • wikipedia
  • reddit

Exercises

• Wordcloud of linked articles from wikipedia
• Wordcloud from popular words on reddit

Follow up on exercises

Web and HTTP

• How the internet works (whiteboard)
• Look at brows http-requests
• Web data

Examples

• YouTube OGP
• RSS-DR
• Creative Commons photots

Exercises

• List headlines
• Show pictures from search

Follow up on exercises

The Scientific method

• Question
• Hypothesis
• Prediction
• Testing
• Analysis

Data Science

• Whiteboard examples
  • Clustering
  • Models - linear regression
• Supervised vs unsupervised

Examples

• Topic-space
• Recommender
• Clustering
• Meta-data analysis

Exercises

• implement the examples yourself
• explain / discuss the examples to your neighbour

Follow up on exercises

What are images

• Images as numbers
• What is color

Examples

• Fetching image
• Scale image
• Composition

Exercises

• Random collage
• Find images via API and compose

Follow up on exercises

• Image color analysis

Live coding example and exercise

Discussion

• Neighbours: what is the primary thing you remember / have learned?
• Brainstorm of cases for next time?

Futher studies

• Thinking in Python
• Code Combat
• Data Camp
• Code Academy

NB: Python 3 vs 2.