AI-900: What Step Follows Defining a Business Problem in the Data Science Process?

Discover why setting the analytic approach is the critical next step after defining a business problem in the data science process. Learn more here!

Question

In the data science process, which step needs to be performed after the business problem has been defined?

A. Tune the model.
B. Set the analytic approach.
C. Clean the data.
D. Model the data.

Answer

B. Set the analytic approach.

Explanation

Setting the analytic approach is the next step required after defining the business problem in the data science process. This is crucial before data acquisition, as it determines what kind of data is needed and how it is to be analyzed. Defining the problem helps identify the appropriate approach, whether it is descriptive, diagnostic, predictive, or prescriptive. This informs the data acquisition process and the types of data to be collected.

Data science processes have the following outline:

1. Define the Business Problem – Collaborate with stakeholders to clearly define the problem, objectives, and solution requirements.
2. Define the Analytic Approach – Choose an analytic approach based on the business problem.
3. Obtain the Data – Identify and acquire the necessary data from various sources. This includes querying databases, extracting information from websites (web scraping), obtaining data from files, purchasing data if required, and collecting new data if necessary.
4. Clean the Data (Scrubbing) – This involves converting data into a consistent format, organizing data, removing unnecessary information, and replacing missing data.
5. Explore the Data – This involves understanding cleaned data using statistical analytical techniques and revealing relationships between data features.
6. Model the Data – This involves building and training prescriptive or descriptive models and testing and evaluating the model’s performance. This also involves adjusting the model’s hyperparameters to optimize its performance (tuning the model) and testing its performance.
7. Deploy the Model – This involves delivering the final model with documentation and deploying the new dataset to production after thorough testing.
8. Visualize and Communicate Results – This involves using visualization tools (e.g., Microsoft Power BI, Tableau, Apache Superset, Metabase) for data exploration.

Cleaning and modeling the data come later in the process, after the data has been acquired and explored.

Tuning the model happens even later, after the model has been built and trained with the acquired data.

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