L11 - Thinking like a Data Scientist

Question 1

What is the essence of thinking like a data scientist?

Answer 1

• Step-by-step approach to solving real-world data acentric problems
• you learn to combine analytic programming and business perspective into a repeatable process for extracting real knowledge from data

Question 2

The framework of thinking like a data scientist?

Answer 2

Question 3

How will economics impact affect AI?

Answer 3

• Productivity gains from businesses automating the process
• Productivity gains from businesses augmenting their existing labour forces with AI technology (assisted and augmented intelligence)
• Increased consumer demand resulting from higher-quality Ai enhanced products and services

AI could contribute 17.7 trillion to the global economy in 2030

• but has a failure rate of 87% for analytics, AI and big data projects

Question 4

Why do data analysis project and AI fail so much?

Answer 4

• Wrong skills
• Wrong business objectives –> apply the technologies to the wrong projects –> throwing all the data at AI as if it can solve all problems
• Starting with the wrong question!
  
  • ​ too often data science and Ai projects being with analysing the data
  • THE BETTER APPROACH IS TO INITIATE THE PROJECT WITH AN ESTABLISHED GOAL THAT MAPS DIRECTLY TO CREATING BUSINESS VALUE
  • Starting with the right question sets the stage or a successful data science project through increase accuracy and efficiency, resulting in purposeful insight

Question 5

What are we trying to achieve with Data science?

Answer 5

Data science serves two important but distinct sets of goals: improving products our customers use and improving the decision our business make

1. Data products: use data science and engineering to improve product performance, typically in the form of better search results recommendations and automated decisions
2. Decision science: use data to analyse business metrics - such as growth engagement, profitability drivers, and user feedback - to inform strategy and key business decisions

Question 6

How can you understand your customers?

Answer 6

Question 7

What is the decision first data last planning chain?

Answer 7

1. first decide what business decision needs improving
2. what new insights will unlock this?
3. What analytics will reveal the insight
4. Finally, gather what data and technology is needed to drive the analytics

Question 8

Consulting as a best practice when start and working on a data science project?

Answer 8

Question 9

Communication as a best practice when start and working on a data science project?

Answer 9

• very important

Question 10

Technology as a best practice when start and working on a data science project?

Answer 10

Question 11

Technological skill as a best practice when start and working on a data science project?

Answer 11

Question 12

Reference guide for data scientists engaging in machine learning process?

Answer 12