Foundations

Question 1

What is Data Analysis?

Answer 1

the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making

Question 2

What are the 6 steps of the Data Analysis process?

Answer 2

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

Question 3

What does analytical thinking involve?

Answer 3

involves identifying and defining a problem, then solving it by using data in an organized, step-by-step manner.

Question 4

What are the 5 key aspects of thinking analytically?

Answer 4

1. Visualization
2. Strategy
3. Problem-orientation
4. Correlation
5. Big-picture and detail-oriented thinking

Question 5

What are analytical skills?

Answer 5

qualities and characteristics associated with solving problems using facts

Question 6

What are the 5 essential aspects of analytical skills?

Answer 6

1. Curiosity
2. Understanding context
3. Having a technical midset
4. Data Design
5. Data Strategy

Question 7

What are the 6 stages of the data lifecycle?

Answer 7

1. Planning
2. Capture
3. Manage
4. Analyze
5. Archive
6. Destroy

Question 8

What is the difference between the data life cycle and data analysis cycle?

Answer 8

Data analysis is not a lifecycle; it’s a process of analyzing data. Data life cycle provides a generic/common framework for how data is managed.

Question 9

What are 3 common data analyst tools?

Answer 9

1. Spreadsheets
2. Query languages for databases
3. Visualization tools

Question 10

What is the purpose of spreadsheets? (3)

Answer 10

To collect and organize data
Help identify pattern and piece data for each specific data project
Create data visualizations (like graphs and charts)

Question 11

Define query language.

Answer 11

a computer programming language that allows you to retrieve and manipulate data from a database

Question 12

What does query language help analysts do? (3)

Answer 12

• Allow analysts to locate specific info from database(s)
• Easier for you to learn & understand requests made to databases
• Allow analysts to select, create add or download data from database

Question 13

Define data visualization.

Answer 13

Graphical representation of data

Question 14

(Spreadsheets) What are “columns” also known as?

Answer 14

Attribute

Question 15

(Spreadsheets) What is a “row” also known as?

Answer 15

Observation

Question 16

(SQL) What does SQL allow analysts to do?

Answer 16

SQL allows data analysts to talk to their database

Question 17

(SQL) What can SQL do?

Answer 17

SQL can store, organize, and analyze data

Question 18

SQL needs a database that understand it’s language?

Answer 18

True.

Question 19

(SQL) What is a query?

Answer 19

A request for data or information from a database. When you query databases, use SQL to communicate your question/request

Question 20

What is the basic syntax of a SQL query? And what do each statement do?

Answer 20

SELECT - choose the columns you want to return
FROM - choose the tables where the columns you want are located
WHERE - to filter for certain information

Question 21

(SQL) How would you request multiple columns in query?

Answer 21

SELECT
Column A,
Column B,
Column C,

Question 22

(SQL) How would you format multiple conditions in the WHERE clause?

Answer 22

WHERE
Condition 1,
AND Condition 2,
AND Condition 3,

Question 23

(SQL) What does LIKE do?

Answer 23

allows you to tell the database to look for certain pattern

Question 24

(SQL) What is “%”?

Answer 24

Wild card. Used to match one or more characteristics.

Question 25

(SQL) How to add a comment in a query?

Answer 25

Use /* text */ or – text

Question 26

(SQL) What does the AS clause do?

Answer 26

Alias: assigns a new name to a column or table names

Question 27

(SQL) What does “<>” mean?

Answer 27

Does not queal

Question 28

What do data analysts use data visualizations for?

Answer 28

To explain complex data quickly, reinforce data analysis, and create interesting graphs and charts

Question 29

What are the 3 steps to plan a data visualization?

Answer 29

1. Explore data for patterns
2. Plan your visuals
3. Create your visuals

Question 30

How to ensure fairness when collecting data?

Answer 30

Considering inclusive sample populations, social context, and self-reported data enable fairness in data collection.