Required Software
I.
Data in Journalism
1.
What is Data Journalism?
2.
Why Data and Journalism Need Each Other
3.
The Data-Driven Storytelling Process
II.
Data Formats
4.
Types of Data Sources
5.
Structured Data and Data Formats
6.
Working With Messy Data
7.
Extracting Data from PDFs
8.
Cleaning Datasets
III.
Introduction to R
9.
Setting Up R and RStudio
10.
R Notebooks and Reproducibility
11.
The Basics of R
12.
Loading Data Into R
IV.
Introduction to Exploratory Analysis
13.
Making Sense of What's in a Dataset
14.
Interviewing Data
15.
Describing Central Tendencies and Spread
16.
Tidyverse Functions (Part 1)
17.
Exploratory Data Analysis in R
18.
Exploratory Data Visualization in R
V.
Intermediate Exploratory Analysis
19.
Census and Sampling
20.
Correlation, Causation, and Change
21.
Generating Story Ideas from Data
22.
Tidyverse Functions (Part 2)
23.
Intermediate Exploratory Data Analysis in R
24.
Intermediate Exploratory Data Visualization in R
VI.
Finding Data
25.
Evaluating Data Sources
26.
Working with Multiple Datasets
27.
Finding Data Online
28.
Combining Data in R
VII.
Humans in Data-Driven Stories
29.
Sources, Expertise, and the Source Mix
30.
Humans in Data-Driven Stories
31.
Contacting Sources and Arranging Interviews
32.
Generating Interview Questions
33.
Conducting Interviews
34.
Verifying Information
VIII.
Telling Data-Driven Stories
35.
Making Data Comprehensible
36.
Leads and Nut Grafs
37.
Quotes and Attribution
38.
Story Structures
39.
Ethics in Data-Driven Storytelling
IX.
Journalistic Visuals
40.
Context and Chart Types
41.
The Working Parts of a Chart
42.
Chart Design Principles
43.
One Dataset, Many Charts
44.
Creating Production-Quality Charts with R
45.
Creating Interactive Charts with Flourish
46.
Creating Interactive Charts with Infogram
X.
Cartography and Journalism
47.
Maps as Storytelling Devices
48.
The Mechanics of Maps
49.
Creating Interactive Maps with Datawrapper
50.
Creating Static Maps with R
51.
Creating Animated Maps with R
52.
Creating Interactive Maps with R
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