Introduction to R and Visualization

Getting started with R (6:09)

2 Topics

Installing R and RStudio (3:12)

Introduction to RStudio (3:00)

Overview of data science and R (31:06)

4 Topics | 7 Quizzes

What is data science? (9:27)

Concept review 1: what is data science?

A data scientist’s approach (7:02)

Concept review 2: a data scientist’s approach

Introduction to R (5:40)

Concept review 3: introduction to R

Getting started in R (8:57)

Exercise 1: Basic operations in R (4:49)

Exercise 2: Working with variables (4:53)

Concept review 4: getting started in R

Feedback for “Overview of data science and R”

Working with data in R (36:41)

4 Topics | 11 Quizzes

Understanding data types (8:24)

Concept review 5: understanding data types

Exercise 3: Working with vectors (5:00)

Exercise 4: Working with matrices (6:08)

Exercise 5: Operations with matrices (4:08)

Exercise 6: Working with lists (7:03)

Manipulating data (8:43)

Exercise 7: Working with data (8:13)

Subsetting data (8:32)

Concept review 7: subsetting data

Exercise 8: Working with data sets (4:38)

Summarizing data (11:17)

Concept review 8: summarizing data

Exercise 9: Working with packages (4:58)

Feedback for “Working with data in R”

Basic visualizations (38:12)

5 Topics | 10 Quizzes

Basic plotting in R (4:51)

Exercise 10: Basic plotting (7:45)

Concept review 9: basic plotting

Basic plotting in ggplot2 (10:55)

Concept review 10: basic plotting in ggplot2

Exercise 11: basic plotting with ggplot2 (7:57)

Building a graph (7:08)

Concept review 11: building a graph

Customizing graphs (11:27)

Concept review 12: customizing graphs

Exercise 12: Customizing graphs (10:04)

Creating a heatmap (3:51)

Concept review 13: creating a heatmap

Feedback for “Basic visualizations”

Exercise 13: creating a heatmap (6:36)

Advanced visualizations (26:54)

3 Topics | 6 Quizzes

Advanced plotting in ggplot2 (9:35)

Concept review 14: advanced plotting in ggplot2

Advanced plotting, part 2 (10:54)

Exercise 14: Building a complex graph (9:15)

Concept review 15: advanced plotting, part 2

3D plotting (6:25)

Exercise 15: Creating a 3D scatterplot (5:05)

Concept review 16: 3D plotting

Feedback for “Advanced visualizations”

Visualizing data with the Google API (21:13)

4 Topics | 5 Quizzes

Mapping DC crime (11:10)

Exercise 16: Mapping data (5:30)

Concept review 17: mapping DC crime

Creating reusable functions (7:44)

Concept review 18: creating reusable functions

Exercise 17: creating functions (7:21)

Tips and additional resources (2:19)

Feedback for “Visualizing the Google API”

End of course feedback survey!

Concept review 2: a data scientist’s approach

Introduction to R and Visualization Overview of data science and R (31:06) A data scientist’s approach (7:02) Concept review 2: a data scientist’s approach

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Question 1 of 5

1. Question
3. What is supervised machine learning?
- Classifying data based on pre-determined categories
- Analysis done under a superior’s supervision
- Data analysis with non-obvious outputs
- An iterative process that creates an accurate model
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Question 2 of 5

2. Question
2. Why do we need to validate the model?
- To make sure it works with other data
- To prove that it works with the test data
- To determine what type of data we’re working with
- To manipulate the data
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Question 3 of 5

3. Question
4. What is unsupervised machine learning?
- Data analysis that leads to new patterns and conclusions
- Classification and regression
- A way to identify who will vote Democrat and Republican
- Data analysis that classifies data based on pre-determined categories
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Question 4 of 5

4. Question
5. Which of these is an example of exploratory data analysis?
- Using customer attributes to group customers and find new patterns
- Grouping customers into groups that have been created already
- Identifying outliers in the data based on the model
- Analyzing the data to match expected outcomes
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Question 5 of 5

5. Question
1. Put the six data science control cycle steps in order, starting with “Ask”.
- Test
- Model
- Research
- Interpret
- Validate
- Ask
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