Design Thinking 2

Question 1

Problem decomposition

Answer 1

steps 1. Understand the problem and then restate the problem in your own words
Know what the desired inputs and outputs are
Ask questions for clarification

step 2. Break the problem down into a few large pieces.

Step 3. Break complicated pieces down into smaller pieces. Keep doing this until all of the pieces are small.

step4. Code one small piece at a time.
1. Think about how to implement it
2. Write the code/query
3. Test it… on its own.
4. Fix problems, if any

Question 2

Time series decomposition

Answer 2

Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components.

Question 3

Decomposition

Answer 3

Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting.

Question 4

Components for decomposition

Answer 4

Level: The average value in the series.
Trend: The increasing or decreasing value in the series.
Seasonality: The repeating short-term cycle in the series.
Noise: The random variation in the series.

Question 5

Analytic Approach

Answer 5

Those who work in the domain of AI and Machine Learning solve problems and answer questions through data every day.
They build models to predict outcomes or discover underlying patterns, all to gain insights leading to actions that will improve future outcomes.

Question 6

Foundational methodology for data science

Answer 6

Every project, regardless of its size, starts with business understanding, which lays the foundation for successful resolution of the business problem.
The business sponsors needing the analytic solution play the critical role in this stage by defining the problem, project objectives and solution requirements from a business perspective.
And, believe it or not—even with nine stages still to go—this first stage is the hardest.

After clearly stating a business problem, the data scientist can define the analytic approach to solving it.
Doing so involves expressing the problem in the context of statistical and machine learning techniques so that the data scientist can identify techniques suitable for achieving the desired outcome.
Selecting the right analytic approach depends on the question being asked.
Once the problem to be addressed is defined, the appropriate analytic approach for the problem is selected in the context of the business requirements. This is the second stage of the data science methodology

Question 7

Type of model

If the question is to determine probabilities of an action

Answer 7

Predictive model

Question 8

Type of model

If the question is to show relationships

Answer 8

Descriptive approach

Question 9

Statistical analysis applies to problems that require counts:

Answer 9

if the question requires a yes/ no answer, then a classification approach to predicting a response would be suitable.