A data sceince project is completed by following certain steps. The sequences of the phases come under the life cycle of the projects. Although the certain steps must be done the data sceince life cycle is a recursive process and can be modified as per the requirements and aspects of any organization.
For a succesful project co,p;etion in an organization project manager should take numbers sequences of its life cycle. The phases have a starting phase, ending phase, and control point. There are some steps in data science project life cycle:
Business understanding. Data collection. Data preparation. Data modeling. Evaluation and deploement.
Business understanding:
Business understanding is getting the problem statement from the data. It is the very first step in the data science project. The business insight team works on the analytics solution by defining the problems, project objectives, and the solution requirements from the business point of view.
Data collection:
Data collection is gathering the data to solve business problems. It can be done by the web scraping, 3rd party API and Big data engineer then it goes to the data scientist to know that what kind of data is required for which problem statements, how to collect them and how to prepare for it to get the desired outcome.
Data preprocessing:
Data preparation is the phase of cleaning the data and make it suitable for business purposes. It is a process for transforming raw data before processing and analysis. It is necessary to reformate the raw data, make it correct and combine with datasets to get enrich data. This process becomes lengthy to get the best data for business users, but it is an essential process in the project life cycle for the context to turn into insights and eliminate the poor quality of the datasets.
Data modeling:
This process is used to find the behavior in data. These data help individuals in two different manners. If we want to use these data for descriptive modeling where we work for recommended systems for example if a person liked any song so would they like the songs? recommended on the basis of older songs they have liked before. And another one is predictive modeling that we use for predicting the outcome and forecast it for future trends. We can create the regression model, Classification model, and clustering model.
Evaluation and deployment:
Evaluation is the validation of the projects. It is an assessment of a project that is ongoing or of completed projects. The main purpose of the evaluation is to find the level of achievement that what was our expectation and what model have given. It gives the achievements of project objectives, development effectiveness, and accuracy. After getting the product we deploy the project to the client.