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What is Data Science? Process, Importance, and Examples

05th Sep, 2023
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    What is Data Science? Process, Importance, and Examples

    What is Data Science?

    Data Science is the domain of study where researchers and industry professionals leverage data to build state-of-the-art frameworks and solve business problems, respectively. It is an interdisciplinary field where experts from any domain could identify challenges in their current work stream and solve them by utilizing data to their fullest. To get a deeper understanding of Data Science, refer to Data Science certification.  

    Why is Data Science Important?

    Data Science is being leveraged heavily by industries across multiple domains.  

    • A financial firm faces million in losses by approving a bad loan or when a customer defaults. Since it’s difficult for a representative to manually look after each case, having a predictive model could significantly reduce the manual effort required and help banks prevent such incidents from occurring.  
    • Another example could be a manufacturing firm in which several unforeseen failures of equipment result in huge losses. Data Science could be used here to build a health monitoring system to get alerted of those incidents. There are several such cases applicable across retail, e-commerce, healthcare, and other sectors.  
    • There is always a challenge in e-commerce to recommend relevant products to customers. Industries often develop a robust product recommendation system using Data Science. 

    There are several such cases applicable across retail, healthcare, and other sectors as well. 

    How Does Data Science Work?

    As mentioned earlier, Data Science is domain agnostic i.e., if your work has data available, you could leverage analytics to simply solve any problems pertaining to that industry. In Data Science, developers, analysts, scientists, researchers, managers, and others take advantage of several open-source tools and technologies to mine diverse data sources to capture meaningful patterns and generate inference. The KnowledgeHut Data Science Bootcamp lays out a clear path on how Data Science works. 

    Data Science Process and Life Cycle

    To build a complete end-to-end pipeline, there are several data science stages that must be followed by a team. Before we discuss those processes, you need to understand that building a Data Science pipeline is not the responsibility of an individual; rather, it’s a team’s effort. Having said that, the phases of Data Science life cycle consist of the following steps:

    Data Science Lifecycle

    1. Problem Formulation

    The product managers or the stakeholders need to understand the problems associated with a particular operation. It is one of the most crucial aspects of a Data Science pipeline. To frame a use case as a Data Science problem, the subject matter experts must first understand the current work stream and the nitty-gritties associated with it. Data Science problem needs a strong domain input, without which coming up with a viable success criterion becomes challenging. 

    2. Data Sources

    Once the problem is clearly defined, the product managers, along with the Data Scientist, need to work together to figure out the data required and the various sources from which it may be acquired. The source of data could be IoT sensors, cloud platforms like GCP, AWS, Azure, or even web-scraped data from social media. 

    3. Exploratory Data Analysis

    The next process in the pipeline is EDA, where the gathered data is explored and analyzed for any descriptive pattern in the data. Often the common exploratory data analysis steps involve finding missing values, checking for correlation among the variables, performing univariate, bi-variate, and multivariate analysis. 

    4. Feature Engineering

    The process of EDA is followed by fetching key features from the raw data or creating additional features based on the results of EDA and some domain experience. The process of feature engineering could be both model agnostic such as finding correlation, forward selection, backward elimination, etc., and model dependent such as getting feature importance from tree-based algorithms. 

    5. Modelling

    It largely depends on whether the scope of the project deems the usage of predictive, diagnostic, or prescriptive modeling. In this step, a Data Scientist would try out multiple experiments using various Machine Learning or Deep Learning algorithms. The trained models are validated against the test data to check its performance. 

    6. Deployment

    The models developed need to be hosted on an on-premises or cloud server for the end users to consume it. Highly optimized and scalable code must be written to put models in production. 

    7. Monitoring

    After the models are deployed, it is necessary to set up a monitoring pipeline. Often the deployed models suffer from various data drift challenges in real time which need to be monitored and dealt with accordingly. 

    8. User Acceptance

    The data science project life cycle is only completed once the end-user has given a sign-off. The deployed models are kept under observation for some time to validate their success against various business metrics. Once that’s validated over a period, the users often give a sign-off for the closure of the project.

    Pre-requisites for Data Science

    Before getting started with Data Science, there are certain prerequisites that need to be fulfilled.  

    1. Machine Learning: It is used to find hidden patterns in data that are otherwise impossible for a human to decode. 

    • Machine Learning could be further categorized into Supervised, Semi-Supervised, and Un-supervised learning. 
    • Supervised learning includes linear and non-linear algorithms. 
    • Un-supervised deals with several clustering methods.  
    • Semi-supervised learning is a mixture of both where little labeled data is consumed, along with a considerable number of un-labeled data points. 

    2. Modelling: We can use Machine Learning as well as Deep Learning for modeling purposes. There are use cases where even statistical and optimization models are highly leveraged to build solutions.

    3. Statistics: An important field in Data Science that could solve multiple business problems without even the need to use Machine Learning. Statistics is universally used in financial sectors for their use cases. Some key concepts in statistics are  

    • Hypothesis testing  
    • Central limit theorem,  
    • z-test, and t-test 
    • Correlation coefficients,  
    • Sampling techniques

    and many more. 

    4. Programming: Working in technology deems writing programs and Data Science is no exception. The most used programming languages in Data Science are Python and R. Starting from model development to monitoring, programming is necessary to build any pipeline.

    5. Databases: As a Data Scientist, you would be working with various sources of data you need to form a basic understanding of different databases and how to extract data from them. Henceforth, it becomes necessary to learn SQL. To know more about the importance of Data Science, refer to the KnowledgeHut Data Scientist certification. 

    Benefits of Data Science

    • In 2016, Business Insider reported that Netflix’s recommendation engine is worth $1 billion per year. The secret sauce behind its popularity is the smart usage of data which lets every user get their personalized recommended tv shows. All these would not have been possible without leveraging Data Science at its core.  
    • According to Forbes, Alibaba has leveraged AI and Machine Learning to build products such as Tmall Smart Selection, Dian Xiaomi, etc., which has resulted in $25 billion sales in Single’s Day in the year 2017. 

    Challenges in Data Science

    Despite all the benefits that Data Science brings to any company, it is no short of challenge. 

    1. Lack of clarity in defining project scope: Often the business use case is not thoughtfully planned, and the metrics are not defined. Such a lack of clarity creates problems down the line.
    2. Relevant or lack of data: This is a major challenge faced by Data Scientists where either the data is not available or it’s not good enough to build any solutions. 
    3. Not meeting business objectives: There are cases where the results produced by the Data Science pipeline are not in line with the business objectives which causes delays in project completion. 
    4. Infrastructure issues: Data Science projects often face issues with infrastructure which results in a project not moving to completion.
    5. Budget Constraints: In many cases, projects are scrapped or put on hold because of budget constraints faced by the company.

    Data Science Technologies, Techniques, and Methods

    As a Data Scientist, there a bunch of tools, techniques, and methods that need to leverage for building scalable solutions.


    1. Tools like Jupyter notebook, vscode, etc. are used.
    2. The programming Languages used are Python, R, and SAS.
    3. A database such as MySQL, Oracle, etc., are used.
    4. Cloud Platforms like GCP, AWS, and Azure are heavily used across industries.
    5. Machine Learning methods like supervised, un-supervised, and semi-supervised are leveraged. 
    6. Mostly used ML libraries and frameworks such as scikit-learn, TensorFlow, Keras, PyTorch, and XGBoost.
    7. Python libraries like Pandas, NumPy, matplotlib are used extensively. 
    8. Deep Learning techniques are used for text, speech, and image use cases.

    Data Science Examples

    Example 1:  

    Think of a day without Data Science; Google would not have generated results the way it does today.

    Example 2: 

    Suppose you manage an eatery that churns out the best for different taste buds. To model a product in the pipeline, you are keen to know what the requirements of your customers are. Now, you know they like cheese on the pizza more than jalapeno toppings. That is the existing data that you have, along with their browsing history, purchase history, age, and income. Now, add more variety to this existing data. With the vast amount of data that is generated, your strategies to bank upon the customers’ requirements can be more effective. One customer will recommend your product to another outside the circle; this will further bring more business to the organization. 

    Consider this image to understand how an analysis of the customers’ requirements helps: 

    Example 3: 

    Data Science plays its role in predictive analytics too. 

    I have an organization that is into building devices that will send a trigger if a natural calamity is soon to occur. Data from ships, aircraft, and satellites can be accumulated and analyzed to build models that will not only help with weather forecasting but also predict the occurrence of natural calamities. The model device that I build will send triggers and save lives too. 

    Consider the image shown below to understand how predictive analytics works:

    Example 4: 

    A lot many of us who are active on social media would have come across this situation while posting images that show you indulging in all fun and frolic with your friends. You might miss tagging your friends in the images you post but the tag suggestion feature available on most platforms will remind you of the tagging that is pending. 

    The automatic tag suggestion feature uses the face recognition algorithm. 

    Who Oversees the Data Science Process?

    An entire Data Science process is managed by individuals of varying roles. 

    1. Stakeholders: They are the ones who define the problem statement for the broader team. 
    2. Product Managers: These are individuals who possess a strong domain understanding of a particular operation which contributes a lot during technical solution build-up. 
    3. Data Scientist: They are mostly the developers who fetch data, perform analysis, build models, validate metrics, and share findings and insights with the business. 
    4. Data Science Managers: Mostly responsible for managing the team of Data Scientists and looking over the requirements. 

    Who is a Data Scientist?

    Within a broader team that is responsible for delivering solutions to a business use case, a Data Scientist is the one who builds the entire pipeline and provides insights and results to the team. They ask interesting questions from the data and use the latest tools and technologies to answer those questions. 

    What Does Data Scientist Do?

    Now, the big question is What is a Data Scientist? A Data Scientist would gather the requirements from the Data Science manager as well as the product manager to build an end-to-end solution pipeline for the problem statement. The primary role of a Data Scientist involves the following. 

    1. Fetch relevant data from a variety of sources: Data could be present in different shapes and forms across multitude of sources like the internet, cloud servers etc. Identifying the right source is an important task. 
    2. Perform data quality check: Real-world data are often messy and hence it becomes impossible to build models on top of that. It is necessary to perform several data quality checks, like missing data, duplicate rows, and so on to create a proper dataset. 
    3. Analyze and share descriptive insights with the business: Descriptive statistics is an important aspect in Data Science. A lot of users are only interested in knowing the evident facts present in the historical data. Thus, it becomes necessary to perform several exploratory analyses on the data and share findings with the team. 
    4. Experiment and build predictive models One approach never fit the bill. Hence it is crucial to perform multiple experiments on the data to try and improve the performance. Several modeling methods could be tested on the dataset by tuning various hyperparameters and comparing the results of each one of them. 
    5. Validate models against both different metrics: It is important to define the metrics to validate your predictive model. A well-defined metric could go a long way in using the right model with the correct set of hyper-parameters. There would be both model and business metrics. We need to validate our results against them both. 
    6. Work with the engineering team to productionize models: Data Science projects don’t end with only the development phase. A Data Scientist is required to deploy models either by himself or work with the engineering team to put the codebase and model into production.  

    Why Become a Data Scientist?

    You become a Data Scientist if you are passionate about numbers. Working as a Data Scientist gives you the scope to leverage statistics, mathematics, and probability extensively to solve high-value business problems. Moreover, this is an ever-growing field that allows you to explore several topics alongside getting paid handsomely. 

    According to LinkedIn, there are more than 800k Data Scientists open positions right now. The Glassdoor data suggest that a Data Scientist in India could earn up to 24 lakhs INR per year. 

    How Industries Rely on Data Science?

    Data Science is used heavily across various industries. Below are some data science examples applicable in those companies. 

    1. Healthcare: Among top data science project examples, healthcare is one of the key areas. You use data to detect cancer, pneumonia, Covid, and so on. The use of Data Science in this sector is rapidly progressing. 
    2. Banking: From preventing loan default to new customer acquisition, data is heavily mined to generate predictions. A lot of statistical-based models are used in the financial sector. 
    3. Manufacturing: Some of the popular applications of Data Science are health monitoring systems, process control, optimization, etc. There is a huge scope for Data Science in the current market. 
    4. Retail: From estimating the price of a product to forecasting demand, Data Science is heavily leveraged in this sector. 
    5. E-commerce: Many e-commerce companies are using Data Science to build personalized search experiences for their customers. 

    Companies Using Data Science

    To address the issues associated with the management of complex and expanding work environments, IT organizations make use of data to identify new value sources. The identification helps them exploit future opportunities and to further expand their operations. What makes the difference here is the knowledge you extract from the repository of data. The biggest and the best companies use analytics to efficiently come up with the best business models. 

    Following are a few top companies that use Data Science to expand their services and increase their productivity. 

    • Google 
    • Amazon 
    • Procter & Gamble 
    • Netflix 


    Data Science is a broad field, and it is only getting stronger with time. The article demonstrates several data science applications in real life and how they are impacting society. It is the right moment to become a Data Scientist and hone your analytical skills to solve multitude of business problems across industries. You would need a problem-solving attitude and a love for numbers to be successful in this field. 

    Frequently Asked Questions (FAQs)

    1. What Do Data Scientists Do?

    Data Scientists solve business problems by mining data using various tools and technologies. They come from various backgrounds like computer science, statistics, economics, and so on.

    2. What are the 3 main concepts of data science?

    Data Quality checks, Exploratory analysis, and Modelling are the 3 main concepts in Data Science. These three form the core components of any Data Science project in an industry.

    3. Does data science have a future?

    Yes, it is a highly in-demand field. More opportunities would come in the future. Moreover, Data Science is also a very lucrative domain to work in. 

    4. What job will I get after data science?

    Data Analyst, Data Scientist, Machine Learning Engineer are some of the roles you could get after Data Science. All these roles are interrelated and more or less bring value to the business. 

    5. What is the data science course eligibility?

    There is no fixed eligibility. A zeal to learn and a passion for exploring numbers is required.  


    Suman Dey


    Suman is a Data Scientist working for a Fortune Top 5 company. His expertise lies in the field of Machine Learning, Time Series & NLP. He has built scalable solutions for retail & manufacturing organisations.

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