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What is Data Science

What is Data Science?

by GangBoard Admin, November 8, 2018

What is Data Science?

Data science is a field of study that deals with the identification, classification, and the extraction of meaningful information from random unstructured data. This multidisciplinary field includes a combination of analytical, programming, and business skills. At the core of data science, is data. Today, the amount of data accumulated in enterprise data warehouses within a year is more than the total amount of data generated in the entire human history. These data are random and unstructured; post-mining, advanced capabilities can be built that can facilitate the business with better decision making, provide a solution to complex problems, and more.

Importance for Data Science

The primary objective of data science is to extract meaningful information from a large and complex set of data. Below are some of the reasons for the need of data science.

  1. Traditionally, Business Intelligence (BI) was used to derive information from data and analysts did this. The data were mostly structured; however, over the past few years, most of the data accumulated is semi-structured or unstructured. It is estimated that by the end of the year 2020, most of the data generated will be unstructured data. Data is accumulated from various sources such as age and income of the consumer, browsing history, purchase history, and several other sources. Traditional Business Intelligence tools fail at deriving meaningful insights from such complex data sets. Therefore, advanced analytical tools and algorithms were the need of the hour. This paved the way for the emergence of data science.
  2. Attenuate fraud and risk. Data science with its advanced algorithms is designed to identify unusual patterns in data sets. They create models that help create notifications that send alerts when the system detects unusual data.
  3. Data science enables the precise recognition of demand and helps supply adjust to the requirements. This benefit of data science is changing the dynamics of production worldwide. It helps companies determine when and where to sell the products. It also makes predictions such as which time of the year the demand of a particular product might go up.
  4. Designing individual customer experience is one of the most beneficial things brought by data science. It allows the sales and marketing team of a company to understand the customers; design marketing strategies and customer experience that meets the individual needs of the target audience.

Data science helps answer tough business questions such as what is the number of customers that may go the competitor, future threat to the product, change in the preferences of customers, and more.

What is the Difference between Data Science and BI?

Business intelligence has a more hindsight approach towards data. It looks at the past data from external and internal sources to assess the business trends. BI has the capability to predict events in the near future. Whereas data science has a forward-looking approach, it analyses the past and present data with the objective of facilitating decision-making. The following table presents the difference between data science and business intelligence.

Features Business Intelligence (BI) Data Science
Source of Data Structures data such as data warehouses and SQL Structured and unstructured data such as SQL, logs, cloud data, text, noSQL.
Approach Statistics and visualization Neuro-linguistic Programming (NLP), Machine Learning, Statistics, Graph analysis.
Tools Present & Past Present & Future
Focus Microsoft BI, QlikView, R, Pentaho Weka, RapidMiner, R, BigML

 

Why Data Scientists are Valuable to Business?

A data scientist brings numerous benefits to a business. Below we have listed the top six reasons businesses need a data scientist.

  1. With the help of algorithms and advanced analytics, data scientists empower the management to make better decisions.
  2. Set a clear path for operation based on trends to improve the performance, productivity, and efficiency of the workforce.
  3. Help the staff in the organization to understand the analytical tools to derive insights and drive actions.
  4. Identify opportunities.
  5. Identifying the potential customer group
  6. Selecting the right talent for a job.

Conclusion

Data science is a new and innovative field of study. It holds a great potential for businesses as well as other domains. Anyone pursuing data science as a career choice has tones of opportunity in the future. A successful data scientist is adept in R Programming, Hadoop, SAS, Spark, and similar software.

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