What is data science?

         

What is data science?

To discover the hidden actionable insights in an organization's data, data scientists mix math and statistics, specialized programming, sophisticated analytics, artificial intelligence (AI), and machine learning with specialized subject matter expertise. Strategic planning and decision-making can be guided by these findings.

Data science is one of the fields with the quickest growth rates across all industries as a result of the increasing volume of data sources and data that results from them. As a result, it is not surprising that Harvard Business Review named the position of data scientist the "sexiest job of the 21st century" (link resides outside of IBM). They are relied upon more and more by organizations to analyze data and make practical suggestions to enhance business results.

Analysts can gain practical insights from the data science lifecycle, which includes a variety of roles, tools, and processes. A data science project often goes through the following phases:

Data ingestion: 

The data collection phase of the lifecycle involves gathering raw, unstructured, and structured data from all pertinent sources using a number of techniques. These techniques can involve data entry by hand, online scraping, and real-time data streaming from machines and gadgets. Unstructured data sources like log files, video, music, photos, the Internet of Things (IoT), social media, and more can also be used to collect structured data, such as consumer data.

Data processing and storage: 

Depending on the type of data that needs to be captured, businesses must take into account various storage systems. Data can have a variety of formats and structures. Creating standards for data storage and organization with the aid of data management teams makes it easier to implement workflows for analytics, machine learning, and deep learning models. Using ETL (extract, transform, load) jobs or other data integration tools, this stage involves cleaning, deduplicating, transforming, and merging the data. Prior to being loaded into a data warehouse, data lake, or other repository, this data preparation is crucial for boosting data quality.

Data analysis:

In this case, data scientists perform an exploratory data analysis to look for biases and trends in the data as well as the ranges and distributions of values. 

The generation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to evaluate the data's applicability for modeling purposes in predictive analytics, machine learning, and/or deep learning. Organizations may depend on these insights for corporate decision-making, enabling them to achieve more scalability, depending on the model's accuracy.

Communicate: 

Lastly, insights are presented as reports and other data visualizations to help business analysts and other decision-makers better understand the insights and how they will affect the organization. In addition to using specialized visualization tools, data scientists can create visualizations using components built into programming languages for data science, such as R or Python.