Data Science

The process of obtaining information and deriving insights out of raw datasets

What is Data Science?

Data science is the approach of obtaining information and deriving insights out of raw datasets. It is a combination of different tools, machine learning concepts, and algorithms to find hidden patterns in the raw data.


Data Science


Data science is typically used for making decisions and forecasts using machine learning, descriptive analytics, and predictive analytics. Data is taken from various industries, networks, and websites, including mobile phones, e-commerce pages, social media, internet searches, and health surveys. However, continuously increasing data lacks structure and needs interpretation for making appropriate decisions.



  • Data science refers to obtaining information and deriving insights out of raw datasets.
  • Data science is typically used for making decisions and forecasts using machine learning, prescriptive analytics, and predictive analytics.
  • Corporations can use data science to innovate in their markets, develop innovative goods, and make the environment around them much more effective.


Data Science Lifecycle

Different lifecycle stages of data science require different programs, skill sets, and techniques. Data science typically includes the following main lifecycle stages:


  1. Capturing data: Involves the acquisition of data, entry of data, signal reception, and extraction of data. Data is assessed whether the required resources are available for supporting the project.
  2. Maintaining data: Involves data cleansing, data processing, data warehousing, and data architecture.
  3. Processing data: Involves data mining, data modeling, data classification, and data summarization. In this stage, techniques and methods to draw relationships between variables are determined.
  4. Communicating data: Includes data reporting, business intelligence, data visualization, and decision making.
  5. Analyzing data: Involves predictive analysis, text mining, regression, and qualitative analysis.


Uses of Data Science

Below are a few examples illustrating how corporations use data science to innovate in their markets, develop innovative goods, and make the environment around them much more effective.



Data science is a major contributor to a variety of breakthroughs in the healthcare field. With a massive network of data currently accessible across everything from EMRs (electronic medical records) to health databases to individual fitness trackers, health professionals are discovering new ways of understanding sickness. Data science also helps in practicing preventive medicine, diagnosing disorders more efficiently, and exploring new care options.



The finance industry saved millions of dollars and an unmeasurable length of effort with data science and machine learning. Financial institutions use Natural Language Processing (NLP) to process and retrieve crucial data from over 12,000 commercial credit contracts a year. What would’ve taken over 360,000 hours of physical labor to complete is now completed in a couple of hours using data science.



Data science is useful in every field; however, it exerts the most significance in the practice of cybersecurity. International cybersecurity company Kaspersky uses computer science and machine learning to identify more than 360,000 new malware samples regularly.

Being able to discover and learn new cybercrime techniques through data science quickly is important for our protection and security in the future.


Self-driving cars

Automotive companies like Tesla, Volkswagen, and Ford are also introducing predictive analysis in a new wave of autonomous vehicles. The vehicles use thousands of sensors and tiny cameras to transmit information in real-time.

Using machine learning, data science, and statistical intelligence, self-driving vehicles can adapt to speed limits, prevent risky lane shifts, and even carry passengers on the fastest lane.


Data Science and Business Intelligence

Business Intelligence (BI) essentially analyzes past data to find perspective and hindsight for explaining various business patterns. Here, BI allows us to take data from internal and external sources, plan and run queries on it, and build dashboards to address questions such as quarterly sales analysis or market issues. BI will be able to determine the effects of such incidents in the immediate future.

On the other hand, data science is a more forward-looking approach that focuses on evaluating historical or present data and forecasting possible results to make educated decisions. It asks broad questions on “how” and “what” incidents arise.

While BI is designed to manage stable and highly organized data, data science can manage high-volume, high-speed, and multi-structured, dynamic data from multiple data sources.


More Resources

CFI is the official provider of the global Certified Banking & Credit Analyst (CBCA)™ certification program, designed to help anyone become a world-class financial analyst. To keep advancing your career, the additional CFI resources below will be useful:

  • Big Data in Finance
  • Data Analytics
  • Power BI – Uses in Finance
  • Python (in Machine Learning)

Financial Analyst Certification

Become a certified Financial Modeling and Valuation Analyst (FMVA)® by completing CFI’s online financial modeling classes and training program!