People and companies who gather and analyse a large amount of data and use it to make business decisions are interested in Data Science. Any company may benefit from a large amount of data, but only if it is processed effectively. After the advent of big data, the storage requirements skyrocketed. This important data was stored on state-of-the-art infrastructure prior to 2010, where it would subsequently be used to extract business insights. The attention has switched to processing this Data now that frameworks like Hadoop have taken care of the storage component.If I may suggest, you may enroll in an online Advanced PGP in Data Science and Machine Learning (Full-Time). You may now do it online via a variety of reputable providers, such as LinkedIn Learning, SimpliLearn, and the National Institute of Information Technology, to name a few. I would suggest NIIT because of their outstanding reputation. There are frequent updates to their courses so that they are up to date with current advances in the business. Their knowledgeable academicians give in-depth education on topics such as Python programming, Linux setup and configuration, and many more topics.

Hello. As per your query

Here is what data science is all about

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

Data science's li fecycle consists of five distinct stages

1)Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.

2)Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.

3)Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.

4)Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.

5)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