A Simple Guide to Data Science Life Cycle
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Today in this technology era, data science is one progressing field these days. The strong improvements seen in computational world databases give the best analysis of users’ behaviour and pattern. The whole data science life cycle begins with analyzing a problem and giving out the solution to it. Generally, a whole process to analyze and collect data is set by data scientists.
Data science has an immense role in the business field. It lets the HR managers job easily by automatically selecting job applicants as per their requirements by producing sales forecasts and business analytics. Data science is not only in the corporate field but also in healthcare, manufacturing, entertainment, and logistics. The best part in the process of data science scientists follows the same framework as there are always some steps in general in the process of data cycle irrespective of the specifications of the project.
Life Cycle Steps
The basic common steps taken by data scientists for each project are as follows:
- Problem Identification
All data science life cycle projects start with the process of identifying problems. For setting any goal for any project, the first thing is to understand the problem we are trying to solve. Identifying the problem can be effortless for the data science life cycle. You need to come up with the problem and goal which is needed to achieve. This goal or problem will act as a base for the whole data science life cycle model.
- Choice Of Representative Sample
It is crucial to pick up the right data science life cycle project, especially when it is a big data plan. Here, representative samples are created by determining the correct variable suitable for solving a problem and answering the question.
- Data Gathering
- It is also called data mining. Here, necessary data are collected for the project. According to the location and approach, data scientists need to decide the source for collecting data it can be collecting from mobile applications, data off websites, and third-party warehouses. Here data science team also performs another step of establishing databases that can store all the information.
- Data Cleaning
- According to the representative sample, the life cycle of data science involves transforming the collected data in the same format. Firstly, they had to find the data that is not matching then by making proper calculations and algorithms for matching it. Foul Data can lead to disappointing results. If the data is not correct, one cannot get correct results even if algorithms for examining data are perfect.
- Development of Data Model
- To meet the project goal and analyze the results, data models are developed based on what the scientist wants to achieve. What scientists want to achieve will decide the development of data science. There are distinct choices for the data model, but the ultimate aim is to generate a solution that can be utilized and analyzed to meet the project’s ultimate goal.
- Data Analysis
- The aim to conduct this step is to promote engineering. Features refer to measurable attributes -they give quantifiable results which can be used for machine learning or predictive learning. Data scientists often use several features to come at the best predictions possible.
- Data Visualization
- It is a part where the results of data are explained through tables, graphs, and charts. It assists analysts and scientists with patterns related to data. It also helps to interpret compound results for those who belong to some other background.
- Data scientists need to examine the system to ensure it performs the required tasks correct or provide because deployment involves making use of the results and analyze to perform predictive explanation, executing a program for ML, and contribute insights into the tools which are used in the organization that was a result of the whole study.
- Long Term Monitoring
These are for projects which are continuous for the long run. Here data scientists need to design a system that mines regularly and produces data sets as variable changes in time and need extended data cleaning, data mining, and analysis.
Re-evaluation may occur in different forms. Here, data scientists assess each step and choose if any rectification is needed to enhance the project before stepping to the next step of the whole cycle. This concludes the entire data science process life cycle.
These are the ten steps in the whole data science life cycle. A key skill set to have is providing a comprehensive and practical description.
The display of the data obtained and transformed must be brief and penetrable enough for the crowd. Information, data content, existing goal, and systematic method are whole, called the heart of the entire data science life cycle. Jigsaw Academy offers data science certification courses that can help you gain your data science certificate.