Data Science. The tending course for the past few years. Many software engineers had to re-invent themselves to learn about Data Science to compete with the latest technologies in the market. However, we have seen a great change in the market’s perception and improvement toward the customer-centric.

Be it a product, process, or business, the drastic change in fulfilling the customer needs would be possible only when we know what the customer is looking for! So, here comes the actual need of looking deeply at their historic data.

When we talk about Data, it has vast importance to make a lot of decisions on predictive models. We will discuss more on what are predictive models in future posts. But, these predictive models can be built by collecting a wide range of data from previous transactions, purchases & sales.

Every business or company must have a structured way of storing its data. A data scientist is responsible for storing, retrieving, wrangling, manipulating, and analyzing this data for meaningful insights. Finally, they would build a model out of this data to predict future improvements.

Data Science Methodology

• It is a process that drives the activities within a given domain
• It does not depend on technologies or tools. It is not a set of techniques or recipes
• It is the process with defined input to achieve a defined output

It is an iterative, simple yet powerful methodology. It creates the leverage in producing repeatable and successful results.

The initial process involves business understanding:

The methodology consists of various stages that form an iterative process to uncover insights.

Business Understanding – Analytical Approach

From business understanding to an analytical approach, knowing about the business sponsors, steering committee, as well as internal corporate partners is the first step. Many questions provide insights and answers as part of the analytical approach and business understanding. A data scientist must question a few things while understanding the business approach as follows.

• Is it inclusive of the method of determining who requires the analytical solution?
• What is the business requirement?
• What is the customer’s requirement?
• What are the advantages?

The problem, project objectives, and solution requirements are defined by business understanding, which serves as the foundation for a solution and the end result.

It determines business and data requirements, such as analytical methods, data content, formats, and representations, as well as hardware and software for business goals.

Requirements and collections

Before working with data, a data scientist must first understand the data collection and requirements. From comprehension to preparation, data collection incorporates all of these insights and requirements. Make use of descriptive statistics as well as visualization techniques. Following data collection, a data scientist must comprehend data content and assess data quality to discover preliminary data insights.

From modeling to Evaluation:

 

Data Scientists usually study neural connections. Programmers write codes for the machine brain, vector illustrations for artificial intelligence, and predict the results by creating a model using machine learning, and data science concepts.

The modeling stage focuses on developing predictive or descriptive models. The data Scientist employs historical data predictive analysis to provide the final result. It is iterative in nature, with intermediate insights and predictions for the future. It consists of several algorithms, parameters, and the best model for a given data set.

The evaluation stage, where diagnostic measures are computed, will indeed follow the modeling stage. The data mining result evaluates all outputs. The following are the results of the assessment process. The steps in the evaluation process are listed below.

• Review the whole evaluation process
• Highlight activities that are missed
• Ensure that the model is correctly built
• Identify failures
• Determine the plan of action based on the findings

From deployment to feedback

The next step includes the deployment and feedback. In this phase, limit your deployment to the production environment. Create a report with recommendations, as well as integrate the model with a complex workflow. It should disseminate information to users and deploy model results correctly. Once the model is deployed, it must be monitored to see what changes may occur in the environment, how to monitor the model’s accuracy, and whether the business goals will change over time. It also questions, when should the data mining model be abandoned. The data analyst would choose the type of report, set goals, and identify the report’s target groups.

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Key Takeaways

• Data Science methodology refers to the process of promoting an analytical approach within a specific domain
• Data Science methodology consists of several stages. From business understanding to analytic requirements to the collection, comprehension to preparation, modeling to evaluation, and deployment to feedback
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