Life Cycle of Data Science

Life Cycle of Data Science

Life Cycle of Data Science. An overview on the various stages of a Data Science project

An inevitable part of today’s world is to up-skill oneself in order to either kick start their career or move ahead to another phase. A well-planned skill enhancement always pays off. Before jumping into any technology or a field of study, it is necessary to perform the groundwork to gain awareness of what is ahead of us. One of the best ways is to get a grip on the end-to-end process. A firm idea on where we start and where we finish sets the road for our journey. It creates a smooth learning path and also provides an opportunity to set short term goals and milestones. Data Science as a field of study is no different.

The project life cycle of Data Science consists of six major phases. Each has its own significance.

  1. UNDERSTANDING THE PROBLEM STATEMENT

The first and probably the most important step is to understand the business problem. This involves constant communication and listening skills in order to understand the problem at hand. If you are someone new to the field, the problem statement will obviously not be as simple as something we encountered while learning the concepts. In real world, the complexity of the problem statement increases multiple folds. It is imperative to understand the problem statement to fulfill the business needs and also for a data scientist to understand the end goal. Usually, there are three types of firms that exist in the field of Data Science/Analytics

  • *Captive Analytics Firm: *There are no actual clients but a problem statement is already formulated. The firm aims to constantly work and improve on it
  • *Non-Captive Analytics Firm: *These firms look for a client to provide their analytics services. The problem statement needs to be formulated well by the clients.
  • Product Based Analytics Firm: These firms do not have clients nor do they have a problem statement. They focus on building analytics tool which will be sold to the required clients. The primary focus is to build an extensive product/tool to satisfy multiple clients.

data-science data-modeling exploratory-data-analysis data-visualization data-collection

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