Design Thinking 3

Question 1

Methodology in Data Science is the best for?

Answer 1

Methodology in Data Science is the best way to organize your work, doing it better, and without losing time

Question 2

Business Understanding

Answer 2

The Business Understanding stage is crucial because it helps to clarify the goal of the customer. In this stage, we have to ask a lot of questions to the customer about every single aspect of the problem; in this manner, we are sure that we will study data related, and at the end of this stage, we will have a list of business requirements.

Question 3

Analytic Approach

Answer 3

• The next step is the Analytic Approach, where, once the business problem has been clearly stated, the data scientist can define the analytic approach to solve the problem.
• This step entails expressing the problem in the context of statistical and machine-learning techniques, and it is essential because it helps identify what type of patterns will be needed to address the question most effectively

Question 4

Data Requirements

Answer 4

• Data Requirements is the stage where we identify the necessary data content, formats, and sources for initial data collection, and we use this data inside the algorithm of the approach we chose.
• Prior to undertaking the data collection and data preparation stages of the methodology, it’s vital to define the data requirements for decision-tree classification. This includes identifying the necessary data content, formats and sources for initial data collection.

Question 5

Data Collection Stage

Answer 5

In the Data Collection Stage, data scientists identify the available data resources relevant to the problem domain.
Usually, datasets are CSV files or Excel

Question 6

Data Understanding stage,

Answer 6

In the Data Understanding stage, data scientists try to understand more about the data collected before. We have to check the type of each data and to learn more about the attributes and their names.

Question 7

Data Preparation stage,

Answer 7

In the Data Preparation stage, data scientists prepare data for modeling, which is one of the most crucial steps because the model has to be clean and without errors. In this stage, we have to be sure that the data are in the correct format for the machine learning algorithm we chose in the analytic approach stage.