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.
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
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.
Global Terrorism Database Analysis was a quick project for understanding and implementing various descriptive statistics and exploratory data analysis techniques.
Analysis, Price Modeling and Prediction: AirBnB Data for Seattle. A detailed overview of AirBnB’s Seattle data analysis using Data Engineering & Machine Learning techniques.
Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.