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Data Science: The Famous Buzzword of Tech World

Data Science


Data science is something that is used by almost every other industry today. The question is why? The answer is all the customer-oriented product creation. The data created by consumers and various entities involved in a business is huge. But then to understand and search for meaning inferences from them can be difficult. This is where data science helps, using various tools and algorithms to explore it and use it for strategic purposes.

The main objective of data science is to create value for the business. And value for business can be created by gauging the market risks and opportunity on time, knowing demands for new products and services, and most importantly customer satisfaction and retention.


It has a variety of applications in different industries. Industries indulged in it are:

  • Medical industry: used for collecting and using various patients’ data and timely disbursing reports.
  • Retail and commerce: various E-commerce websites use customer satisfaction activities and also for warehousing and logistics.
  • Banking and financial institutes: one of the pioneers in using it for detecting credit risk and frauds.
  • Entertainment and social media: they use it for getting customer insights and content optimization.
  • Transportation industry: to understand travel insights, route planning, and shipment management.

Data science is applied in making optimized search engines, recommendatory systems, gaming, robotics, voice and image recognition software, etc.


Data science is a logical step by step process, which takes both time and patience. Getting understandable inferences from massive amounts of raw data can be difficult.

  • Collecting data: involves collecting data from various sources and storing them in data frameworks.
  • Cleaning data: data usually have lots of flaws and gaps, these inconsistencies are to be removed and cleaned.
  • Exploring data: exploring data includes analyzing the data using visualizing tools and statistical models to find meaningful patterns.
  • Modeling of data: modeling usually involves creating algorithms using machine learning to use data as a strategic and predictive tool.
  • Communicating the results: this is where one needs to interpret the inferences and communicate with others so that it can be used for further business decision making.


There are two aspects of becoming a data scientist:

  • Technical aspect
  • Business aspect

In the technical aspect, one should be skilled in:

  • Mathematics
  • Statistics
  • Programming
  • Data mining, cleaning, exploring
  • SQL databases, C/C++, Java
  • Python, R, SAS
  • Algorithms and data structure
  • Hadoop, Apache Flink, Apache Spark, Hive, etc.
  • Database management
  • Machine learning tools and techniques.

Business skills one should have are:

  • Presentation skills
  • Communication skills
  • Analytical decision-making skills
  • Problem-solving skills

To be a successful data scientist, along with technical and business skills one should have a curiosity to see new problems and ask new questions and try to solve them in an analytical way.


Source by Shalini M

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