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What is Data Science
Updated on 22nd May, 23 21881 Views

In an emerging digital economy, data is creating a buzz in each and every domain that one can think of. With a consistent flow of information in the form of unstructured data, the need to convert the same into actionable insights is more prominent than ever before.

With a forecast of immense data points that will be released in the market for the next decade, one can only imagine the impact of actionable insights that can be drawn from that data.

In this article, we will learn about Data Science in its entirety to understand how one can create a roadmap to excelling in their career in this field.

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What is Data Science?

Data Science can be explained as the entire process of gathering actionable insights from raw data that involves various concepts that include statistical analysis, data analysis, machine learning algorithms, data modeling, preprocessing of data, etc.

To put it in Layman’s terms – Let’s consider an example. A case study that also went on to become a Hollywood feature film “Moneyball”.

In the movie, they have shown how an underdog team went on to compete at the highest level of the baseball tournament by analyzing the statistical data points of each player and quantifying their performances to win the game. It can be aligned with how data science actually works.

Another example would be, how search engines gather user data, and based on their choices(data points), recommendations are put forward for them. Organizations use recommendation engines made using various machine learning algorithms on streaming websites to predict recommendations that will serve best the user’s history. 

All in all, Data science is the domain of study where data is processed through advanced statistical and mathematical concepts using machine learning techniques to gather actionable insights to cater to problem statements or business problems.

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How does Data Science Work?

The working can be explained as follows:

  1. Raw data is gathered from various sources that explain the business problem.
  2. Using various statistical analysis, and machine learning approaches, data modeling is performed to get the optimum solutions that best explain the business problem.
  3. Actionable insights that will serve as a solution for the business problems gathered through data science.

Let’s understand this with an example, Suppose there is an organization that is working towards finding out potential leads for their sales team.  They can follow the following approach to get an optimal solution using Data Science:

  1. Gather the previous data on the sales that were closed.
  2. Use statistical analysis to find out the patterns that were followed by the leads that were closed.
  3. Use machine learning to get actionable insights for finding out potential leads.
  4. Use the new data on sales lead to segregate potential leads that will be highly likely to be closed.

Let’s discuss the history of data science, and how it has evolved into an emerging domain for the years to come.

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History of Data Science

Data Science has evolved over the years and didn’t start as how we know it today. Let’s take a look at the timeline to understand how Data Science evolved over the years.

  1. 1962 – Inception
    a. Future of Data Analysis – In 1962, John W Tukey wrote the “Future of Data Analysis” where he first mentioned the importance of data analysis with respect to science rather than mathematics.
  2. 1974
    a. Concise Survey of Computer Methods – In 1974, Peter Naur published the “Concise Survey of Computer methods that surveys the contemporary methods of data processing in various applications.
  3. 1974 – 1980
    a. International Association For Statistical Computing –
    In 1997, The committee was formed whose sole purpose is to link traditional statistical methodology with modern computer technology to extract useful information and knowledge from the data.
  4. 1980-1990
    a. Knowledge Discovery in Databases – In 1989, Gregory Piatetsky-Shapiro chaired the Knowledge Discovery in Databases that later went on to become the annual conference on knowledge discovery and data mining.
  5. 1990-2000
    a. Database Marketing –
    In 1994, BusinessWeek published a cover story that explains how big organizations are using the customer data to predict the likelihood of a customer buying a specific product or not. Kind of like how targeted ads work in the modern era for social media campaigns.
    b. International Federation of Classification Society – For the first time in 1996, the term “Data Science” was used in a conference held in Japan.
  6. 2000-2010
    a. Data Science – An Action Plan for Expanding the Technical Areas of the Field of Statistics – In 2001, William S Cleveland published the action plan, that majorly focused on major areas of the technical work in the field of statistics and coined the term Data Science.
    b. Statistical Modeling – The Two Cultures – In 2001, Leo Breiman wrote “There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown”.
    c. Data Science Journal – April 2002 saw the launch of a journal that focused on management of data and databases in science and technology.
  7. 2010-Present
    a. Data Everywhere – In February 2010, Kenneth Cukier wrote a special report for The Economist that said a new professional has arrived – a data scientist. Who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data.
    b. What is Data Science? – In June 2010, Mike Loukides described data science as combining entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution.

Data Science Life Cycle

The Data Science lifecycle comprises of the following:

  1. Formulating a Business Problem
    Any data science problem will start their journey from formulating a business problem. A business problem explains the issues that may be fixed with insights gathered from an efficient Data Science solution. A simple example of a business problem is – You have past 1 year’s sales data for a retail store. Using machine learning approaches, you have to predict or forecast the sales for the next 3 months that will help the store to create an inventory that will help in reducing the wastage of products that have lesser shelf life than the other products.
  2. Data Extraction, Transformation, Loading
    The next step in the data science life cycle is to create a data pipeline where the relevant data is extracted from the source and transformed into machine readable format, and eventually the data is loaded into the program or the machine learning pipeline to get things started.
    For the above example – To forecast the sales, we will need data from the store that will be useful for formulating an efficient machine learning model. Keeping this in mind, we would create separate data points that may or may not be affecting the sales for that particular store.
  3. Data Preprocessing
    The third step is where the magic happens. Using statistical analysis, Exploratory data analysis, data wrangling and manipulation, we will create meaningful data. The preprocessing is done to assess the various data points and formulate hypotheses that best explain the relationship between the various features in the data.
    For example – The store sales problem will require the data to be in a time series format to be able to forecast the sales. The hypothesis testing will test the stationarity of the series and further computations will show various trends, seasonality and other relationship patterns in the data.
  4. Data Modeling
    This step involves advanced machine learning concepts that will be used for feature selection, feature transformation, standardization of the data, data normalization, etc. Choosing the best algorithms based on evidence from the above steps will help you create a model that will efficiently create a forecast for the said months in the above example.
    For example – We can use the Time Series forecasting approach for the business problem where the presence of high dimensional data could be the case. We will use various dimensionality reduction techniques, and create a Forecasting model using AR, MA, or ARIMA model and forecast the sales for the next quarter.
  5. Gathering Actionable Insights
    The final step from the data science life cycle is gathering insights from the said problem statement. We create inferences and findings from the entire process that would best explain the business problem.
    For example – From the above Time series model, we will get the monthly or weekly sales for the next 3 months. These insights will in turn help the professionals create a strategy plan to overcome the problem at hand.
  6. Solutions For the Business Problem
    The solutions for the business problem are nothing but actionable insights that will solve the problem using evidence based information. For example – Our forecast from the Time series model will give an efficient estimate for the store sales in the next 3 months. Using those insights, the store can plan their inventory to reduce the wastage of perishable goods.

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Prerequisites for Data Science

There are several prerequisites that must be fulfilled in order to efficiently drive data science solutions in an organization. Some of the prerequisites are as follows:

  1. Programming Knowledge
    For the statistical analysis and computations that are required for the Data Science processes, it is necessary for the professionals to be familiar with Programming languages such as Python or R programming. The library support and scripting knowledge helps you create machine learning models from scratch with ease. Scikit-learn, Tensorflow, pandas, matplotlib, seaborn, scipy, numpy, etc, are some of the inbuilt python programming libraries that can be used for Data Science using Python.
  2. Statistics, Probability, And Linear Algebra
    The knowledge of descriptive statistics, inferential statistics is a must if you really want to make a career in data science. With the help of statistical analysis, you are able to create various inferences and understand the data at hand. One example would be how we discussed performing hypothesis testing to test whether a time series is stationary or not.

Probability and linear algebra also plays an important role in shaping the understanding of complex machine learning algorithms. If you’re familiar with these concepts, it will be easier for you to understand the internal functioning of various machine learning algorithms.

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  1. SQL, Excel And Visualization Tools
    The visualization tools such as PowerBI, Tableau, etc, can provide a great interactive interface to represent various data points, that can help in performing initial analysis or just to understand the data.
    SQL and Excel on the other hand can help you in understanding the representation of data in tabular format or data frames that help in data manipulation, wrangling, etc.
  2. Big Data And Cloud
    A machine learning model deployed at scale is where the cloud comes into the picture, to be able to magnify the learnings and outcomes for any business problem we use machine learning on cloud. And big data gives a better perspective on how to handle large and complex data for out business problems and for creating data pipelines for continuous development and training of various machine learning models at scale.

“Introduction to Data Science in Python: A Beginner’s Guide” – Provide a high-level overview of what data science is and how Python is used in data science, along with basic concepts and tools.

Who is a Data Scientist?

Data scientists are IT professionals whose main role in an organization is to perform data wrangling on a large volume of data—structured and unstructured—after gathering and analyzing it. Data scientists need this voluminous data for multiple reasons including building hypotheses, analyzing market and customer patterns, and making inferences.

Career Transition

What Does a Data Scientist Do?

The role and responsibilities of a data scientist can vary from organization to organization, based on this, we can segregate them in the following manner.

A data scientist’s role in any organization will involve the following:

  1. Data Extraction, Loading, Transformation
  2. Exploratory Data Analysis
  3. Data Manipulation
  4. Statistical Analysis
  5. Visualization
  6. Data Modeling
  7. Gathering Actionable Insights

This modified data is further used for the prediction of results that can help organizations to come up with efficient plans that need to be executed for the growth of the organizations.

Although in some organizations, some of the responsibilities will be divided amongst the data engineers and data analysts who will wrangle the data and transform the features for it to be provided to the machine learning engineers to perform various modeling techniques to get the solutions.

And finally, the data scientist will make sense of the inferences to get the solutions for the business problems. But, in some organizations, a data scientist might have to cover all these aspects in order to drive solutions for the business problems.

Real-World Examples

  1. A team of statisticians and data scientists was able to somewhat predict the various waves and their outcomes in the world using the data from previous catastrophic events of the same scale when the world encountered COVID-19 for the first time. As more data was available, they were able to predict the outcomes with more precision and were forecasting the COVID-19 outbreak on a daily basis with much more efficiency and accuracy.
  1. Recommendation engines for various streaming websites take account of the historical data of the users that has various features. Based on these data points, data scientists have built recommendation engines using machine learning algorithms that can give the users recommendations that they are most likely to watch based on their previous choices.
  1. Autonomous cars and how teams at the likes of Tesla have used computer vision technology in a way to navigate through the traffic keeping pedestrians and other vehicles in mind.

Similar to creating world-class solutions for business problems that may seem impossible at first, the other responsibilities that a data scientist takes care of are as follows:

  1. Leadership skills to manage teams and keep the entire Data Science process running with efficiency for any given business problem.
  1. Project Management Skills to plan entire end-to-end projects from inception to conclusion with optimized problem-solving approaches.
  1. Stakeholder Management to be able to convey the requirements to the concerned teams and be on the same page while delivering the solutions to the respective stakeholders.

Since we have discussed the various roles and responsibilities of a data scientist, let us also discuss why becoming a data scientist is a good roadmap for your career.

Why Data Science?

Currently, across industries, there is a huge need for skilled and certified data scientists. They are among the highest-paid professionals in the IT industry. According to Glassdoor, a data scientist is the best job in America with an average annual salary of $110,000. Only a few people process the skills to derive valuable insights out of raw data.

data science

Furthermore, looking at the ever-increasing requirements, McKinsey has predicted that there will be a 50 percent gap in the demand and supply of data scientists in the upcoming years.

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In recent years, there has been huge growth in the field of the Internet of Things (IoT), which has led to the generation of 90 percent of data being generated today. Every day, 2.5 quintillion bytes of data are generated, and it is accelerated with the growth of IoT.

This data comes from all possible sources such as

  • Sensors used in shopping malls to gather the shoppers’ information
  • Posts on social media platforms
  • Digital pictures and videos captured on phones
  • Purchase transactions made through e-commerce

This data is known as big data.

Organizations and companies are flooded with tremendous amounts of data. Thus, it is very important to know what to do with this data and how to utilize it.

Data Science Path

The preceding picture represents the concept of Data Science. It brings together a lot of skills such as statistics, mathematics, and business domain knowledge, and helps organizations find ways to:

  • Reduce costs
  • Get into new markets
  • Tap into different demographics
  • Gauge the effectiveness of marketing campaigns
  • Launch new products or services

And the list is endless!

Therefore, regardless of the industry vertical, data science is likely to play a key role in your organization’s success.

Look at the following infographic to better understand the scope of data science.

Data Science

Google is by far the biggest company that is on a hiring spree for trained data scientists. Since Google is mostly driven by Data Science and Artificial Intelligence, and Machine Learning these days, it offers one of the best salary packages to its data science employees.

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Importance of Data Science

Data is a valuable asset for various industries to help make careful and sound business-related decisions. Data science has the ability to churn raw data into meaningful insights.

An expert data scientist has the capability of digging out meaningful information from whatever data is available to them. They lead organizations in the right direction through sound data-driven decisions and suggestions.

Uncover the distinctions between BI & Data Science to optimize your business strategy.

Data Science Applications

Below-mentioned is some of the applications of Data Science:

  • Fraud and risk detection: Over the years, financial organizations have learned to analyze the probabilities of risks and defaults through customer profiling, past expenditures, and other variables available through data.
  • Healthcare: Data science makes it possible to manage and analyze very large diverse datasets in healthcare systems, drug development, medical image analysis, and more. Recently Data Science approaches were brought in to combat the COVID-19 pandemic. Data Scientists helped in digital contact tracing, diagnosis, risk assessment, resource allocation, estimating epidemiological parameters, drug development, social media analytics, etc.
  • Internet search: All search engines, including Google, use data science algorithms to deliver the best result for searched queries within seconds.
  • Targeted advertising: Digital ads have a higher call-through rate (CTR) than traditional ads because targeted advertising is based on a user’s past behavior with the help of data science algorithms.
  • Recommendation systems: Internet giants as well as other businesses have fervidly made use of recommendation engines to promote their products based on users’ previous search results and their interests.
  • Advanced image, speech, or character recognition: Facial recognition algorithms on Facebook, speech recognition products, such as Siri, Cortana, Alexa, etc., and Google Lens are all perfect examples of data science applications in image, speech, and character recognition.
  • Gaming: Today, games use machine learning algorithms to improve or upgrade themselves as players move up to higher levels. In motion gaming, the opponent (computer) is able to analyze a player’s previous moves and accordingly shape up its game.
  • Augmented reality (AR): Augmented reality promises an exciting future through Data Science. A VR headset, for example, contains algorithms, data, and computing knowledge to offer the best viewing experience.

Check out our blog on Data Science tutorial to learn more about Data Science.

Use of Data Science

Let’s take a look at some use cases of Data Science.

  • Amazon: Amazon uses a personalized recommendation system to improve customer satisfaction. This is majorly dependent on predictive analytics. Amazon analyzes the user’s purchase history to recommend more products.
  • Spotify: Spotify utilizes Data Science to offer personalized music recommendations to the users. In 2013, Spotify made predictions about the Grammy Award Winners by analyzing what music its users listen to. Out of the 6 predictions, 4 came true.
  • Uber: Uber utilizes big data to gain better insights and provide better service to the users. With its huge database of drivers, it can suggest to users the most suitable one. Uber charges the customers based on the time it takes to get to the destination. This prediction is helped by various algorithms.

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Business Intelligence vs Data Science

The following table states the key differences between business intelligence and data science:

FactorsBusiness IntelligenceData Science
ConceptIt is a collection of processes, tools, and technologies that help a business with data analysis.It consists of mathematical and statistical models used for processing the data, discovering hidden patterns, and predicting future actions based on those patterns.
DataIt deals mainly with structured data.It accepts both structured and unstructured data.
FlexibilityData sources should be planned before the visualization.Data Sources can be added anytime based on the requirements.
ApproachIt has both statistical and visual approaches toward data analysis.Graph analysis, NLP, machine learning, neural networks, and other methods can be used to process the data.
ExpertiseIt is made for business users to visualize raw business information without any technical knowledge.It requires sound knowledge of data analysis and programming.
ComplexityFor a single user, compared to data science, business intelligence is much simpler to use and visualize data.Data science is much more complex when compared to business intelligence.
ToolsBusiness intelligence tools include MS Excel, Power BI, SAS BI, MicroStrategy, IBM Cognos, Throughput, and more.Some of the most popular Data science tools are Python, Hadoop, Spark, R, TensorFlow, BigML, MATLAB, Excel, and more.

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How Do Top Industry Players Use Data Science?

In this section of the blog, we will look at how top industry players, such as Google, Amazon, and Visa, use data science. IT organizations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimize themselves efficiently. Here, the deciding factor for an organization is what value they extract from their data repository using analytics and how well they present it. Some of the biggest companies that are hiring data scientists at competitive salaries are listed below:



Google is by far the biggest company that is on a hiring spree for trained data scientists. Since Google is mostly driven by data science, artificial intelligence, and machine learning, it offers one of the best salary packages to its employees.



Amazon is a global e-commerce and cloud computing giant that is hiring data scientists on a large scale. Amazon needs data scientists to find out customer mindset and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.



An online financial gateway for most companies, Visa does transactions worth millions in a single day. Due to this, the need for data scientists is huge at Visa to generate more revenue, check fraudulent transactions, customize products and services as per customer requirements, etc.

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Salaries and Jobs Available in Different Countries

Data Science is expanding at a mind-blowing rate, resulting in increased demand for skilled data scientists around the globe. According to PayScale, the average annual salary of a skilled data scientist is US$94,491. However, the salary offered may differ based on location and experience.

Below-mentioned is five countries with the most opportunities for data scientists:

  • United States (US): The US has the highest demand for skilled data scientists. In the US, companies have spent more than a billion dollars to acquire data scientists from different countries. The average annual salary of an entry-level data scientist in the US is US$85,000; the salary can go up to US$136,000 p.a. based on your expertise and experience in the field.
  • Germany: Data scientists in Germany can earn about €5,960 per month. The salary of a data scientist in Germany ranges from €2,740 per month to €9,470 per month. Germany offers the most lucrative salary packages for the role of a data scientist.
  • United Kingdom (UK): Similar to Europe and the US, various industries in the UK are now hiring skilled professionals to manage, maintain, and analyze large amounts of data. A data scientist in the UK can earn up to £50,000 p.a.
  • China: China is planning to lead the world in artificial intelligence by the year 2030 by investing in IT industries and making government policies more accommodating. An experienced data scientist in China can earn up to ¥350,000 p.a.
  • India: India has the fastest-growing industries in several sectors such as healthcare, defense, logistics, and artificial intelligence. Similar to the rest of the world, India too is facing acute challenges in finding skilled data scientists. So, if you have the right skills and experience as a data scientist, you can earn up to ₹1,000,000 p.a.

How does Intellipaat help you in making a career in Data Science?

Intellipaat provides many opportunities to aspirants or learners who are willing to establish themselves as all-rounders in the domain of data science. Hence, getting trained in data science technologies through the courses offered by Intellipaat will be a great career move. Intellipaat offers a wide range of courses dedicated to providing you with end-to-end knowledge about trending and in-demand data science skills.

Go through these Data Science Interview Questions and Answers to excel in your interview.


Today, if any digitally-driven organization is starved of data even for a short duration, then the organization loses its competitive edge. Data scientists help organizations make sense of their business, customers, and markets.

If you want to become a Google Data Scientist with the best salary, then you need to be at the top of your game. If you are wondering how to learn Data Science and the scope of Data Science, then Intellipaat is the right place to start your incredible Data Science journey.


What is the difference between data science, artificial intelligence, and machine learning?

With Data Science you can analyze, visualize, and predict data using statistical techniques. Artificial Intelligence makes machines act like humans. The machine is made to imitate human behavior. Machine Learning is a part of AI that makes machines learn using the data provided.

What is Data Science in simple words?

Data Science helps in finding meaningful insights from data using various techniques.

What does a Data Scientist do?

A Data Scientist helps businesses by analyzing large amounts of data and extracting meaning out of it.

What is Data Science with an example?

Data Science uses various tools and techniques to process and analyze data. For example, it can optimize road routes using traffic data and location data from various users. This can help in reducing fuel consumption.

What kinds of problems do Data Scientists solve?

Data Scientists can solve issues like forecasting events, revamping search engines, predicting crime, traffic prediction, etc.

What is the Data Science course eligibility?

You can check out Intellipaat’s Data Science Course for more details.

Can I learn Data Science on my own?

Data Science could be daunting to learn by oneself. It is recommended that you learn it with the help of a structured program.

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20 thoughts on “What is Data Science? Applications, Use Cases, Process, & Lifecycle”

  1. I am Data scientist from past three years… “Data Science” is performs research and analysis on data and helps companies to improve business by predicting growth, trends and business insights based on huge amounts of data

    1. Hi Anmol, can I knw more about Data science and Data analytics? As I am thinking to do a course, can u plz suggest me?

  2. It’s a great post. It’s such a wonderful read on Training. Keep sharing such kind of worthy information. It’s a great moment today proffesonals in this field.

  3. Hi, I am an ETL developer, have experience in ETL tools like ODI, Informatica and has good knowledge of SQL..Is it good for me to get into Data Science..Does ETL knowledge will help me if I move to Data science

  4. It’s a great post. It’s such a wonderful read on Training. Keep sharing such kind of worthy information.

  5. Most useful blog.
    It explains with a detailed description and diagram which is easy to understand
    Also, the YouTube video in this blog covers the important topics
    The table of content helps to navigate easily to the targeting topics
    The course in this blog has a structured learning path for getting a job

  6. This blog has the perfect career path for Data Science. Learned the complete overview of Data Science in this blog. Specifically, the addons tutorial video is more helpful to learn more about data science.

  7. Actually, before reading this blog, I don’t know anything about Data Science. But after reading this I got to know everything about Data Science like what is Data Science and its applications and how to learn Data Science. Thanks for this amazing blog, keep sharing.

  8. This was awesome! The best blog I have ever read before. Explained everything in a detailed manner. This is simple and easily understandable. I am looking forward to more blogs from the website. Thanks for sharing.

  9. It was really useful for a fresher like me. This blog gave me an idea about Data Science and what to learn, where to study, and how to become a Data Scientist in 2022. The special thing about this blog it covers all the basics to advanced. Thanks for the knowledgeable content.

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