Module 1 - Introduction to Data Science

Question 1

What’s some key words for Industry 4.0?

Answer 1

Cyber-physical systems

Question 2

Key enabling technologies in industrial digitalization?

Answer 2

• Big data & AI
• Connectivity & 5G
• Additive Manufacturing
• Collaborative Automation

Question 3

What’s OEE short for?

Answer 3

Overall Equipment Effectiveness

Question 4

Describe AI

Answer 4

Any technique to make a machine mimic a human decision

Question 5

Describe ML

Answer 5

Part of AI, make a machine learn a pattern, decision based on historical data.

Question 6

Describe Deep Learning

Answer 6

Mimic the actual way of a human brain, neural networks

Question 7

Mention 3 challenges in the maintenance area which can be solved with data science

Answer 7

• Automated production systems must work
• Even more technology to maintain
• Upgrading old production equipment

Question 8

List the four dimensions of smart maintenance.

Answer 8

• Data driven decisions (DDD)
• Human Capital Resource (HCR)
• Internal integration (INI)
• External integration (EXI)

Question 9

Which dimension of smart maintenance should you invest in?

Answer 9

Investment in the wrong dimension (non bottleneck) is a waste of resources

Question 10

What’s impact-frequency mapping used for?

Answer 10

To decide what decisions which needs to be data driven. High impact (6-9 on a scale of 1-9) and high frequency (on a daily basis)

Question 11

Name four analytics techniques

Answer 11

• Classification
• Regression
• Clustering
• Association

Question 12

Name the four levels of data analytics

Answer 12

• Descriptive
• Diagnostic
• Predicitve
• Prescriptive

Question 13

What does descriptive analysis mean?

Answer 13

Description of what has already happened, WHAT happened

Question 14

What does diagnostic analysis mean?

Answer 14

Exploration of meaningful information, WHY did it happen

Question 15

What does predictive analysis mean?

Answer 15

Prediction of future outcomes based on historical data, WHAT WILL happen

Question 16

What does prescriptive analysis mean?

Answer 16

Recommendations on predictive model output, WHAT SHOULD we do

Question 17

Describe the six steps of CRISP-DM

Answer 17

• Business understanding
• Data understanding
• Data preparation
• Modeling
• Evaluation
• Deployment

Question 18

Describe the Business understanding step in CRISP-DM briefly.

Answer 18

Determine business objectives and analysis goals.
Assess situation (resources, requirements, costs).
Produce a project plan.

Question 19

Describe the Data understanding step in CRISP-DM briefly.

Answer 19

Collect, describe and explore the data.

Verify quality in a data quality report.

Question 20

Describe the Data preparation step in CRISP-DM briefly.

Answer 20

Select, clean, construct, integrate and format data

Question 21

Describe the Modeling step in CRISP-DM briefly.

Answer 21

Select modeling technique, generate test design, build model and then assess model

Question 22

Describe the Evaluation step in CRISP-DM briefly.

Answer 22

Evaluate results (with regard to success criteria), review the process and determine next steps

Question 23

Describe the Deployment step in CRISP-DM briefly.

Answer 23

Make deployment plan, plan monitoring and maintenance, and review the project