Section 3

Question 1

Descriptive analytics questions: What happened?

Answer 1

Questions help organizations better understand historical trends and patterns in their data. By analyzing data meaningfully organizations can identify opportunities for improvement, make informed decisions, and track progress over time

Question 2

Diagnostic analytics questions: Why did it happen?

Answer 2

These diagnostic questions aim to uncover an issue root cause by asking probing questions that ultimately reveal the core of the problem

Question 3

Predictive analytics questions: What is likely to happen?

Answer 3

Uses data statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Question 4

The predictive model

Answer 4

Learns to identify the patterns and relationships between the features and the target variable and then uses this knowledge to make predictions on new, unseen data.

Question 5

Prescriptive analytics questions: What is the best course of action?

Answer 5

Identifies the best course of action in a given situation. Involves predicting future outcomes and recommending actions to achieve a desired outcome

Question 6

How may a company use prescriptive analytics?

Answer 6

To identify the optimal product inventory levels and delivery schedules based on customer demand, production capabilities, and delivery costs

Question 7

Exploratory analytics questions: What patterns are present?

Answer 7

Helps decision-makers understand a problem’s underlying cause and take appropriate actions to reverse the sales decline

Question 8

Exploratory analysis

Answer 8

Is an open-ended and flexible data analysis that uncovers patterns, insights, and relationships that are not immediately apparent

Question 9

Exploratory analysis

Answer 9

Typically combines data visualization, statistical analysis, and data mining techniques to explore the data and uncover trends and patterns

Question 10

Benefits of exploratory analysis?

Answer 10

It can help identify data errors and inconsistencies. These issues may not be apparent through other analysis methods but can significantly influence the results, accuracy, and reliability

Question 11

Engagement:

Answer 11

The level of interaction between users and a particular platform or piece of content; can include website traffic; social media likes, shares, and comments; click-through rates, and other measurable actions that indicate user interest and involvement.

Question 12

Algorithm

Answer 12

A set of step-by-step instructions for solving a problem or completing a task; in data analytics, algorithms process and analyze large amounts of data to extract valuable insights and patterns for making informed business decisions, including clustering algorithms, decision tree algorithms and more.

Question 13

Revenue growth

Answer 13

Measures the increase in revenue over a specific period

Question 14

Engagement rate

Answer 14

Measures the level of engagement with content or advertisements, such as likes,comments, and shares on social media

Question 15

Time on site

Answer 15

Measures a users time on a website

Question 16

Bounce rate

Answer 16

Measures the percentage of website visitors who leave after viewing one page

Question 17

Return on investment

Answer 17

Measures the profitability of an investment

Question 18

Customer aquisition cost

Answer 18

Measures the cost of aquiring a new customer

Question 19

Churn rate

Answer 19

Measures the percentage of customers who stop doing business with a company over a certain period

Question 20

Customer-lifetime-value

Answer 20

Measures the total value of a customer to a business throughout their relationship

Question 21

Click-through-rate

Answer 21

Measures the percentage of people who click on a link or advertisment

Question 22

Conversion rate

Answer 22

Measures the percentage of website visitors who complete a desired action, such as purchasing a filling out form

Question 23

A graph that represents data in a visually appealing and easy-to-understand format

Answer 23

Bar chart

Question 24

Can help visualize customer behavior on a website or app, identifying which areas of the site are receiving the most engagement

Answer 24

Heat map

Question 25

A graph that visually displays the relationship between two varibles

Answer 25

Scatterplot

Question 26

A graph that represents data over time

Answer 26

Line chart

Question 27

Can help the company identify potential areas for improvement of growth and make data-driven decisions to maintain the companies competitive advantage

Answer 27

Regression analysis

Question 28

Are a type of machine learning algorithm designed to recognize patterns and relationships data. The human brain structure and function serve as a model for this, using layers of interconnected nodes or neurons to process information and make predictions

Answer 28

Neural networks

Question 29

Requires data in the form of unstructured text, such as social media posts or customer reviews. Structured texts follows a specific format, such as tables or spreadsheets

Answer 29

Text mining

Question 30

A statistical method that measures the degree of association or relationship between the variables

Answer 30

Correlation analysis

Question 31

Compares the means of two independent samples to determine whether they are different from each other

Answer 31

T-test

Question 32

A data analysis technique used to analyze and improve business processes.
Extracts data from various sources, such as; event logs, or transactional databases, then uses specialized software to visualize and analyze the extracted data to identify patterns, bottlenecks, and other process inefficiencies

Answer 32

Process Mining

Question 33

A data mining technique that identifies patterns in customer purchasing behavior based on the idea that certain products are frequently purchased together

Answer 33

Market basket analysis

Question 34

The finance industry commonly uses this to analyze historical stock prices and forecast future trends using this technique.
This technique can develop informed investment strategies while also mitigating financial risk. It can also identify trends and patterns in data.

Answer 34

Time-series analysis

Question 35

Organizations can uncover hidden relationships, make accurate predictions, and better understand complex systems.
This technique can help build predictive models or clustering models to identify patterns or anomalies in the data.

Answer 35

Machine learning

Question 36

When used with data analysis techniques, it can extract insights and patterns from large datasets that would be difficult or impossible for humans to identify through manual analysis

Answer 36

Machine learning techniques

Question 37

Companies use this to identify patterns and relationships between products or services. They identify relationships and patterns in large datasets.

Answer 37

Association rules

Question 38

A data analytic technique that groups similar objects or data points based on the object characteristics or attributes.

Answer 38

Clustering

Question 39

A statistical method that identifies the relationship between a dependent variable and one or more independent variables.

Answer 39

Regression analysis

Question 40

Some commonly used data analytics techniques in business include the following

Answer 40

Regression analysis
Decision trees
Clustering
Association rules
Machine learning
Time series
Market basket
Process mining
T-test
Correlation analysis
Text mining
Neural networks