Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves the use of techniques from statistics, data analysis, machine learning, and computer science to extract insights and knowledge from data. Data science can be applied in a wide range of fields, including business, healthcare, finance, and government, among others. The goal of data science is to turn raw data into actionable insights that can inform decision-making and improve outcomes.

Data science is the study of data. Like biological sciences is a study of biology, physical sciences, it’s the study of physical reactions. Data is real, data has real properties, and we need to study them if we’re going to work on them. Data Science involves data and some signs.

It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.

It is the skill of unfolding the insights and trends that are hiding (or abstract) behind data. It’s when you translate data into a story. So use storytelling to generate insight. And with these insights, you can make strategic choices for a company or an institution.

We can also define data science as a field that is about processes and systems to extract data of various forms and from various resources whether the data is unstructured or structured.
The definition and the name came up in the 1980s and 1990s when some professors, IT Professionals, scientists were looking into the statistics curriculum, and they thought it would be better to call it data science and then later on data analytics derived.

But the biggest question and confusion in the world is what is Data Science?

We’d see data science as one and from one to many attempts to work with data, to find answers to questions that they are exploring. On summarizing all, we can say that it’s much more about data than about science. If you have proper or improper data, and you have curiosity for working with data, and you’re manipulating it according to your needs, you’re exploring it according to your needs, the very exercise of going through analyzing data, trying to get some answers or fulfill the society need from your explored, manipulated and exercised Data – it is Data Science.

Data Science is relevant today because we have millions of data available on single data or for single data. We didn’t use to worry about the lack of data. Now we have tons of data. In the past, we didn’t have defined algorithms, now we have algorithms. In the past, the software was not affordable by everyone because it was too expensive, so only industries with big-bucks can use it but now it is open source and freely available. In the past, we didn’t even think about storing a large amount of data, because the storage facilities are also very costly and now it is available for a fraction of a cost, we can have gazillions of data sets for a very low cost. Also, internet connectivity was not common and too costly. So, the tools to work with data, the variability of data, the ability to store, analyze data and last and most important Connectivity, it’s all cheap, it’s all available, it’s all ubiquitous, it’s here. There’s never been a better time to be a data scientist than now.


Data science is a field that involves using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It can be used in a variety of industries and applications such as:

  1. Business: Data science can be used to analyze customer data, predict market trends, and optimize business operations.
  2. Healthcare: Data science can be used to analyze medical data and identify patterns that can aid in diagnosis, treatment, and drug discovery.
  3. Finance: Data science can be used to identify fraud, analyze financial markets, and make investment decisions.
  4. Social Media: Data science can be used to understand user behavior, recommend content, and identify influencers.
  5. Internet of things: Data science can be used to analyze sensor data from IoT devices and make predictions about equipment failures, traffic patterns, and more.
  6. Natural Language Processing: Data science can be used to make computers understand human language, process large amounts of text or speech data and make predictions.

Overall Data Science is a multidisciplinary field that involves the use of statistics, machine learning, and computer science to extract insights and knowledge from data.

Applications of Data Science:

Following are some of the applications that make use of Data Science for their services:

  • Internet Search Results (Google)
  • Recommendation Engine (Spotify)
  • Intelligent Digital Assistants (Google Assistant)
  • Autonomous Driving Vehicle (Waymo)
  • Spam Filter (Gmail)
  • Abusive Content and Hate Speech Filter (Facebook)
  • Robotics (Boston Dynamics)
  • Automatic Piracy Detection (YouTube)

Who is Data Scientist?

Is he/she someone struggling with data all day and night or experimenting in his/her laboratory with complex mathematics? After all, ‘Who is a Data Scientist’?

There are many definitions available in the market for Data Scientists. In simple words, a Data Scientist is one who knows and practices the art of Data Science. The super-popular term ‘Data Scientist’ was coined by DJ Patil and Jeff Hammerbacher. Data Scientists are those scientists who crack complex data problems with their strong expertise in certain scientific disciplines. They work with many elements related to mathematics, statistics, probability, Quantitative and Qualitative forecasting, computer science, etc. (though they may not be an expert in all these fields).

We can say that Data Scientists are Business Analysts and Data Analysts, with a difference!. Though the initial training or basic requirements are similar for all these disciplines, Data Scientists require:

  • Strong Business Acumen
  • Strong Communication Skills
  • Exploring Big Data

Just like an agricultural scientist wants to know the percentage increase in the yield of wheat this year as compared to last year’s (also the reasons associated with it) or if a financial company wants to classify its customers based on their creditworthiness (before granting loans) or whether a retail organization wants to reward extra points to its loyal customers, all need data scientists to process a large volume of both structured and unstructured data in order to make crucial business decisions.

In today’s dynamic and vast world, the main challenge that today’s Data Scientists face is to find solutions to the existing business problems and above it, to identify the problems that are most relevant and crucial to the organization and its success.

Why Data Scientists are called ‘Data Scientists’?

The term “Data Scientist” has been in existence after considering the fact that a Data Scientist collects a huge amount of information from the scientific fields and applications whether the information is statistical, mathematical, or computer science. They make use of the latest technologies and tools in finding the solutions and reaching the conclusions that are important for an organization’s growth and development. Data Scientists present the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.

Just like any other scientific piece of training, data scientists always need to ask and find answers of What, How Who, and Why that data is available to them. They are required to make a clearly defined plan and work towards achieving the results within a limited time, effort and money.


There are many advantages of using data science in various industries and applications. Some of the key advantages include:

  1. Improved decision-making: Data science can be used to analyze large amounts of data and extract valuable insights that can inform business decisions and improve organizational performance.
  2. Predictive modeling: Data science can be used to build predictive models that can forecast future events and outcomes, such as sales or customer behavior.
  3. Automation: Data science can be used to automate repetitive tasks, such as data cleaning, feature engineering, and model selection, which can save time and resources.
  4. Personalization: Data science can be used to personalize experiences for customers, such as recommending products or tailoring advertising campaigns.
  5. Cost reduction: Data science can be used to identify inefficiencies and reduce costs in various industries, such as supply chain management and healthcare.
  6. Fraud Detection: Data science can be used to analyze large amounts of transaction data and identify fraudulent activities, which can reduce financial losses.
  7. Improved customer service: Data science can be used to analyze customer data and understand their needs, preferences and behavior which can improve the overall customer service.
  8. Improved product innovation: Data science can be used to analyze data from research and development, customer feedback, and market trends to identify new product opportunities.

Overall, data science can be used to gain insights and make predictions that can drive improvements in various industries and applications, and help organizations make better decisions, improve efficiency and drive growth.