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

Time limit: 0

Quiz Summary

0 of 5 questions completed

Questions:

Information

You have already completed the quiz before. Hence you can not start it again.

Quiz is loading…

You must sign in or sign up to start the quiz.

You must first complete the following:

Results

Quiz complete. Results are being recorded.

Results

0 of 5 questions answered correctly

Your time:

Time has elapsed

You have reached 0 of 0 point(s), (0)

Earned Point(s): 0 of 0, (0)
0 Essay(s) Pending (Possible Point(s): 0)

Categories

Not categorized 0%

1
2
3
4
5

Current
Review
Answered
Correct
Incorrect

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
Correct

Incorrect
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
Correct

Incorrect
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
Correct

Incorrect
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
Correct

Incorrect
Question 5 of 5

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

Incorrect

Log Into Data Society

Make better decisions with data.

Sign Up
Log In

Sign Up

Click an icon to connect with:

Log In

Click an icon to connect with:

Username or Email

Password

Remember Me

Forgot Password?

Lost your password?

Login

Accessing this course requires a login. Please enter your credentials below!

Username or Email Address

Password

Remember Me

Forgot Password?

Lost Your Password?