Exploring the World of Data Science

Computer science can be a big topic and you can’t cover it all at once. But let’s try to understand it in a very simple and easy way.

Every corner of today’s world is filled with data in its raw form. When shopping, taking a medical test, watching a movie or show, using the internet or taking a survey. Everything feeds lots and lots of data. But why is this data so important?

Science is when you try to understand something using scientific tools. And data is a set of qualitative and quantitative variables for any topic. So comprehensive both of these definitions can be said; data science is a field where data is used as raw material and then processed using scientific tools to extract an end result. This end result helps increase business value and customer satisfaction.


You see its products every day of your daily life. Products that result from fighting huge amounts of unstructured data and using them to find solutions to business and customer-related problems. Some of them are:

  • Digital Advertising: At the same time, two different people can see different ads on their computer screens. The reason is data science that recognizes one’s preferences and displays ads that are relevant to them.

  • Image and voice recognition: whether the automatic tagging of Facebook or Alexa, Siri, etc., recognizes your voice and does exactly what you asked them to do, again it is data science.

  • Recommendation systems: When you shop on an online site or search for a show on any entertainment app, you get suggestions. These suggestions are created using data science by tracking activities from past activities and the like.

  • Fraud detection: many financial institutions use it to know the financial and credit standing of clients, to know in time whether to borrow them or not. This reduces credit risk and bad loans.

  • Search Engines: These search engines deal with the huge amount of data and it may be impossible to search for what you asked for a second if the algorithms were not there just to help with this huge task.


It is a big topic, it consists of several different stages and steps before reaching the final conclusion. They are:

  • Retrieving data from multiple sources.

  • Saving data categorically

  • Cleaning data for discrepancies.

  • Examine the data and find trends and patterns in them.

  • Machine learning that models the found patterns for algorithms.

  • And then eventually the algorithms interpret and communicate them.


There are several different techniques and all of these techniques must be learned by a data science aspirant.

  • SQL or NoSQL for database management

  • Hadoop, Apache Flink and Spark for storage.

  • Python, R, SAS, Hadoop, Flink and Spark for data angling, scripting and processing.

  • Python libraries, R libraries, statistics, experimental design to explore and search the data to find the necessary conclusions.

  • Machine learning, multivariate calculation, linear algebra for modeling the data.

  • Communication and presentation skills along with business skills to make the endings useful in strategic decision making.