What is a Data Science and Its Life Cycle?
What Is Data Science?
The field of data science studies how to use large amounts of data using contemporary tools and methods to uncover patterns, extract valuable information, and make business decisions. Data science creates predictive models by utilizing sophisticated machine learning techniques. The information utilized for analysis can be found in a variety of formats and originate from a wide range of sources. After learning what data science is, let’s examine the data science way of life.
Data Science Life cycle
Now that you are aware of what data science is, let’s move on to discuss the data science life cycle. The lifespan of data science has five distinct stages, each with specific duties to perform:
- Capture: Data extraction, signal reception, data entry, and data acquisition. Obtaining unstructured and raw structured data is the focus of this step.
- Maintain: Data Processing, Data Architecture, Data Staging, Data Cleaning, and Data Warehousing. This phase involves transforming the unprocessed data into a format that may be utilized.
- Process: Data Modeling, Data Summarization, Data Mining, and Clustering/Classification. To assess the prepared data’s suitability for predictive analysis, data scientists look at its ranges, patterns, and biases.
- Analyze: Qualitative analysis, text mining, regression, exploratory/confirmatory analysis, and predictive analysis. This is where the life cycle really gets juicy. At this point, the data are subjected to numerous analytics.
- Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.