Getting Started With Data Science – A Guide For 2022

In the past decade, the biggest challenge faced by most industries was a lack of infrastructure to store the ever-increasing amount of data. All companies were competing against each other in the race to build frameworks and solutions to store data. But soon enough, Hadoop and several other new frameworks successfully solved the lack of storage crisis. In this article, we are going to cover all the important aspects of data science which will help you in getting started with data science.

In the year 2012, the total amount of data in the world was 2.7 zettabytes, but, in 2020, this number has already gone up to 44 zettabytes (Builtin). Just in the past eight years, the amount of data that we have created has increased at an exponential rate. Studies have shown that over 90% of the total data to ever exist in the world, was created in the last two years. Data science has a crucial role to play in the generation and storage of our data. Therefore, understanding how it works is essential.

Data Science Market Size | Mindbowser

How Data Science Works?

Data science is a field that uses a wide array of scientific methods to gain insights and extract knowledge from data. Data scientists take raw data and refine it using sophisticated techniques and expertise in various disciplines. A Data Science team must be skilled in a wide range of fields such as mathematics, engineering, computing, visualizations and statistics.

This expertise allows them to extract useful insights and information from massive volumes of data. This data can consist of the most crucial bits of information for your business. Hence, Data Science helps drive innovation and decipher new opportunities.

Data Science Life-Cycle Process

The life-cycle of the data science process consists of the following 5 stages:-


The process of data warehousing stores data collected from different sources. Then, inaccurate, unreliable, duplicate and missing data is removed from the database.


Data mining is used to identify trends and future patterns in a data set. Processed data is then classified into groups on the basis of similar traits.


Using data analytics can help make predictions based on the data. It can also be analyzed by using regression, text mining, and qualitative analysis methods.


It is essential to display the results of your analysis to gain utility from the data. This can be done using reports consisting of the results of research and analysis of the data.


It is essential to display the results of your data analysis to gain utility from the data. This can be done using reports consisting of the results of research and analysis of the data.

Data Science Life-Cycle | Mindbowser

Getting Started With Data Science

To build a Data Science-driven company, you will need strategic thinking and accurate planning to capture and maintain a wide range of data modes from multiple sources and then instantly analyze that data for a greater understanding.

If you are a company that is looking to build a Data Science solution and want to decide whether it will be helpful or not, these sector-wise examples show the prominence of Data Science among different fields nowadays.


The availability of an abundance of medical data has resulted in medical professionals and researchers finding new ways of comprehending diseases, practicing preventive medical care, and using advanced diagnostic techniques.


Data Science has allowed the finance industry to complete tasks that took thousands of manual labor hours to complete in just a couple of minutes. As a result, the industry has saved millions of dollars and an immeasurable amount of time.


Popular streaming services such as Netflix and Spotify used Data Science to recommend to their users based on what they are currently watching or listening to.

Cyber Security

The combination of Data Science and Machine Learning has allowed cybersecurity firms to detect over 360,000 samples of malicious viruses every day (Builtin). This has allowed them to instantly learn and identify new forms of cybercrime.


Automobile giants such as Ford, Volkswagen, and Tesla, are implementing predictive analytics to drive research for their autonomous vehicles. Information collected by the sensors of these cars is relayed to data analytics algorithms in real-time.

How To Execute Successful Data Science Strategies?

Even though Data Science is rapidly gaining popularity among businesses and IT leaders, many companies are having a hard time implementing and executing their Data Science strategies.

The following steps will help you effectively execute your Data Science strategy:-

Identify Key Business Drivers

Before starting a Data Science initiative, you have to understand why you need the Data Science process in your business. There are several areas where the innovation of Data Science could contribute to business success.

Build An Effective Team

You need to create a stable team that brings together the multidisciplinary knowledge blending technology and business. The team would work closely with you to straighten your business goals with the data and figure out what is possible technologically.

Emphasize Communication Skills

The insights provided by Data Science analytics are of no value unless their value can be properly articulated. Communication is the most critical element that contributes to the success of an organization.

Continuous Improvement to Data Science Processes

Your Data Science team must continually focus their efforts on experiments and finding ways to improve the efficiency of models.

Protection of Your Data

Implement governance policies to ensure the security of sensitive data during Data Science processes. Personally, identifiable information should not fall into the wrong hands under any circumstances.

Data Science Strategies | Mindbowser

How To Select The Right Data Science Tools For Your Company?

After you start tracking your KPIs, your company is ready to begin the implementation of Data Science using analytics tools. But now, you are faced with the challenge of finding the right analytics tool for your company. It is difficult to find one tool that can do everything but integration among tools is possible. You can pick a tool based on the task you want to perform and create a stack of different tools for various functions.

Here are some recommendations for tools based on categories of tasks:

Data Abstraction

Segment and mParticle can be used to simplify your data implementation requirements.

Strategic Development

Google Analytics, Appsflyer or Branch can be used to identify the best marketing campaign to acquire more customers.

Usage Measurement

Heap and Amplitude can be used to understand what users are doing with your product.

Revenue Metrics

Recurly and Chargify can be used to track your SaaS revenue metrics.

Qualitative Data

Hotjar and Appsee can be used to track session recordings, surveys and other qualitative data.

Meet Our Tech expert

Sandeep Natoo

Sandeep is a highly experienced Python Developer with 15+ years of work experience in developing heterogeneous systems in the IT sector. He is an expert in building integrated web applications using Java and Python. With a background in data analytics. Sandeep has a knack for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing a valuable prediction with a good eye for detail.

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Different Aspects Of Data Science

Data science is used as a rather broad generic term for data-related work such as Data Analytics, Data Visualization and Data Scraping. Each field is unique in its own way and performs its own tasks and functions.

What Is Data Visualization?

Data visualization is a method that uses visuals, both static and interactive, to help people understand the large amount of data being collected. Data visualization is an important skill in applied Statistics and Machine Learning. It can be helpful when you need to get information from some datasets.

The information we can mine from datasets can be about finding patterns and around identifying outliers, and much more. With some prior domain knowledge, visualization can be used to find relationships between the data, which can be insightful to you and your audience.

Data Visualization Use Cases For An E-Commerce Company

Mindbowser Helped An eCommerce Company Optimize Their Supply Chain Using Data. Below we have highlighted the problem statement of the company, along with our solution and the outcome achieved through the implementation of our solution.

Problem Statement

A subscription box-based eCommerce platform that ships organically grown succulents from a SoCal nursery to any address in the United States was looking to optimize their shipping process to be in line with daily weather forecasts the deliveries at the best port and at the best time of the day.

They were wondering about a solution that can help their logistics team be better abreast of climate updates while scheduling deliveries. The company ships 100K+ of succulents each week, to every state, all year long.


The data visualization process helped us carefully curate a long list of Airports and added all the current temperature-related information in one place. This database helped the team to predict seven days of weather information in advance. If any weather issues like harsh, humid, and muggy were reported, they could hold the delivery process.

For weather data, we used the Darksky API to deliver hyperlocal weather information, with down-to-the-minute forecasts that show exactly when the rains will start or stop.

Our APIs delivered the following:

Data Visualization Use Cases | Mindbowser


Backed with the power of data the company now achieves greater savings and higher customer satisfaction. The forecast enables the team to deliver fresh on-demand succulents every time across the U.S getting them to rave reviews such as these.

What Is Web Scraping?

The data displayed by most of the sites are viewed using web browsers. The web browser does not offer to save the data in a user-friendly format. The data can be saved only as a web page, and most web pages only give one option to the user- to manually copy and paste the data. Web Scraping is a smart technique that can be utilized to extract vast amounts of information from the target websites.

The extracted data can then be saved to a local file on your system or spreadsheet format. Web scraping automates the processes of extracting data from the website using scripts.

Use Case Of Web Scraping

Mindbowser helped in Connecting Emerging and Established Designers With Manufacturers Using Data Scraping. Below we have highlighted the problem statement of the company, along with our solution and the outcome achieved through the implementation of our solution.

Problem Statement

We worked with a B2B marketplace that connects creative professionals with quality materials and qualified vendors globally. Sitting on a trillion-dollar global industry whose supply chain is antiquated, opaque, and offline, the company approached Mindbowser to improve speed-to-market, responsiveness, and sustainability with the support of data scraping.


One of our first tasks was to perform web scraping on the sites that the client provided, in order to capture product reviews. This task may sound simple, but it wasn’t, there were dozens of pages to be scraped and not only all of them were unique, but they also these were technically complex and bot-aware websites (like Amazon).

Impact Of Our Solutions

Scraping reports help the customer map key indicators such as retail insights, market share, competitors’ activities, pricing analytics, promotional monitoring, etc.

Data Analytics What Is It?

Data analytics solutions help customers identify and obtain the most valuable and meaningful insights from the data, and turn them all into competitive advantages. We Produce 100+ Analytic Roadmaps Every Year Saving Costs For Our Clients With The Help Of Our Expert Developers, Agile Methodology, And More.

Use Cases Of Data Analytics

Mindbowser implemented data analytics and ML solutions to predict the purchasing nature of a customer. Below we have highlighted the problem statement of the company, along with our solution and the outcome achieved through the implementation of our solution.

Problem Statement

We worked with a real estate company with a substantial online presence. The company was looking for a reliable partner who could deliver an end-to-end data analytics solution for them. The objective was to run an EDA (Exploratory Data Analysis) to come up with ideas that will help business owners to make real-time business decisions.

Key Results Achieved

  • Exploratory Data Analytics (Insights based on descriptive-analytical results and Ideation) with the goal of coming up with ideas on how data owners could potentially use the data to drive their business.
  • Explore ideas on how data can be used to train certain ML models that could result in increased capabilities.
  • Identify factors that customers consider before making a decision.

During this engagement, we implemented an end-to-end data analytics process and ML model that helped the customer to comprehend their business growth and enabled them to predict their customer behavior effectively.

Data Science Use Cases In Various Industries

Data Science In Real Estate

Data science applies analytics and Machine Learning models to evaluate information and enhance decision-making in the development process of the real estate arena. With its help, consumer behavior can be understood, business strategies can be optimized, emerging market trends can be assessed and any predicted risks can be artfully evaded and handled. Hence, the benefits of data science are to use data to help buyers and sellers for a smoother journey.

Data Science Use Cases In Real Estate | Mindbowser

The Real Estate sector is one of the most lucrative and fastest-growing industries in India. However, the lack of data and analytics has been a major roadblock for businesses to grow. Data analytics solution allows to reduce turnaround time for property buyers and sellers, get more accuracy in financial calculations, etc. Data analytics is a process that enables real estate developers to know what their customers want, how they buy and why they choose one product over another.

When combined with business intelligence suites and data visualization tools, Real Estate service providers can understand various business processes and analyze the output better. In Real Estate, it has been found to be useful for giving clients a clear view of what they are investing in. Mindbowser portfolio companies from real estate are using data scraping to gain intelligence and scale operations.

With scraping, real estate companies can scrape property listings, home listings details, pricing and other crucial data. Data scraped at scale can work as a great input for further initiatives like building AI Algorithms and Machine Learning.

Data Science In Healthcare

Almost all healthcare centers across the globe have adopted data visualization benefits to manage their routine in-house operations. From patient profiling and recording patient information to maintaining and managing satisfaction surveys and complaint registers, hospitals have started to completely rely on data visualization for a better understanding of data. Some of the reasons why visualization techniques in the healthcare segment must be practiced are listed below.

Healthcare analytics is the process of analyzing current and historical industry data to predict trends, improve outreach, and even better manage the spread of diseases. The field covers a broad range of businesses and offers insights on both the macro and micro levels. It can reveal paths to improvement in patient care quality, clinical data, diagnosis, and business management.

When combined with business intelligence suites and data visualization tools, healthcare analytics help managers operate better by providing real-time information that can support decisions and deliver actionable insights.

Mindbowser portfolio companies like Kinesiometrics, TurtleHealth, MangoMirror are helping connect individual data with providers and then use it to gather analytics and uncover latent trends.

Data Science For SaaS Companies

The implementation of data science in SaaS companies helps ensure that your business is moving in the right direction and to check the efficiency of your strategy. To make data science effective, the company must have a robust understanding of its business problem. Before implementing data science, the company must track specific KPIs and metrics using a tracking plan.

Finally, after choosing the right tools and creating a report, the company can start thinking about advanced methods to extract more value from their data.

Customer Demographic

Information such as the user’s location, the devices that they use, will tell you where and how the user spends most of their time. It will help you narrow down the users that are interested in your services and follow a more targeted strategy to make your marketing campaign more effective.

Marketing Attribution

Whenever you acquire a new user, always keep track of the marketing channel or campaign that brought the customer to you. This data will help you identify which marketing strategy is most useful for your organization, and which is the least effective. After performing this analysis, you can decide to pour more resources into the campaign or channel that has generated the most leads. Thus, allowing you to maximize your profits and improve ROI.

User Behavior

Identify what features and functionalities of your product are the most used functions amongst your users. The analysis of this data can help you determine the best features of your product. Then, you can enhance those features to increase user engagement with your service.

When data analytics is implemented in SaaS companies, it leads to superior customer satisfaction. This results in increased revenue and reduced costs. Data analytics is one of the most powerful tools that can help a SaaS company to keep pace with its rapidly growing business. Data science implementation can bring about high ROI for the companies by helping them make better decisions and take the right actions.

For Mindbowser portfolio SaaS companies such as OurOffice, ProofPilot, CodeGrip etc, we have built a complete analytics cycle by using a combination of ready tools such as Mixpanel, Clarity, Clevertap, Mautic etc to analyze funnel behavior, smart segmentation and understand user behavior and customized it with our own solutions.

Why Having A Data Science Partner Makes Sense?

Given the shortage of data scientists and the dispersed range of skills required to execute a data science problem, it is often hard to build a Data Science team for most companies. Here is how Mindbowser can build a data advantage for you quickly.

Data Science Partner | Mindbowser

Why Mindbowser For Data Science Services?

Our lean and agile team of full-stack data scientists, engineers and application developers accelerates the innovation and implementation of data-driven solutions. As a Data Science services company, we bring extensive cross-industry expertise backed by scientific rigor and deep knowledge of state-of-the-art techniques to design, build and deploy bespoke data solutions.



Ready models that result in time and cost-saving

solution accelerators


We help you identify & validate viable use cases across your business, guide your model through to deploying your models into production

domain experts


A strong team led by Ph.D. holders in data science and AI

In-house experts


Our solutions are independent of the framework. You can continue to explore proprietary tools as well as choose among open source options

Framework Agnostic


We cover the full spectrum of ML and AI that is required to get the right ROI for your business

360 degree service

Sandeep Natoo

Head Of Emerging Trend

Sandeep is a highly vigorous Machine learning expert with over 12+ work of experience with developing heterogeneous systems in the IT sector. He is an expert in building Java integrated web applications and Python data analysis stack. He has been known for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable prediction with a good eye for detail. He is highly optimistic and avid nature, for various challenges is his major strength.

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