Data Scientist

Business Understanding

Ability to ask a lot of questions to the customer about every single aspect of the problem; in this manner, we will have a list of business requirements.

Analytic Approach

Once the business problem has been clearly stated, we 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.

Data Requirements

Identifying the necessary data content, formats, and sources for initial data collection, and we use this data inside the algorithm of the approach we chose.

Data Collection, Understanding & Preparation

Identifying the available data resources relevant to the problem domain. To retrieve data, we can do web scraping on a related website, or we can use repository with premade datasets ready to use. Trying 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. Prepare data for modeling, which is one of the most crucial steps because the model has to be clean and without errors.

DS

Modeling

Modeling focuses on developing models that are either descriptive or predictive, and these models are based on the analytic approach that was taken statistically or through machine learning.

Deployment

This stage depends on the purpose of the model, and it may be rolled out to a limited group of users or in a test environment. A real case study example can be for a model destined for the healthcare system; the model can be deployed for some patients with low-risk and after for high-risk patients too.

Feedback

This stage is usually made the most from the customer. Customers after the deployment stage can say if the model works for their purposes or not.

Projects

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