What is data science? Explain about their Lifecycle

What is data science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

The Data Science Lifecycle

Data Science lifecycle consists of five distinct stages, each with its own tasks:

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

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.

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.

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.

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.

Advantages of Pursuing Data Science Course

The following are the benefits of pursuing a data science course from a reputed institute:

1. Ample Job Opportunities

2. Increasing Demand

3. Attractive Salary Packages

Skills needed to become a Data Scientist

A Data scientist is supposed to possess in-depth knowledge of statistics, computer programming in Python, SAS, R, Machine learning and AI. Knowledge of Big data is not required but it may be useful in some cases. You can learn more about these skills in our Skills required for Data Scientist here.