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 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 will cover all the important aspects of data science that will help you get started with data science.

In 2012, the total amount of data in the world was 2.7 zettabytes, but in 2020, this number already went up to 44 zettabytes (Builtin). Just in the past eight years, the amount of data we have created has increased exponentially. Studies have shown that over 90% of the total data worldwide was created in the last two years. Data science has a crucial role in generating and storing our data. Therefore, understanding how it works is essential.

Data Science Market Size | Mindbowser

How Does Data Science Work?

Data science is an expansive field that uses many scientific methods to gain insights and extract knowledge from data. Data scientists take unprocessed data and use sophisticated techniques in various disciplines to make it useful.

An effective data science team is skilled in many areas, such as mathematics, engineering, computing, visualizations and statistics. This expertise allows them to draw meaningful conclusions and information from large data sets. This data can be your business’s most vital piece of information.

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 identifying trends and patterns in data sets to predict future behavior. Data is classified based on similar characteristics, allowing businesses to make better decisions about targeting their products and services.


Data analytics can help you predict what might happen next based on what has happened in the past. You can analyze data 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.


Reports are essential for displaying the results of your data analysis and putting it to good use. Reports should consist of the effects of research and analysis of the data, presented clearly and concisely.

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 abundant 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 few 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 use Data Science to recommend to their users based on what they are 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 learn and identify new forms of cybercrime instantly.


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 have difficulty 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 must understand why your business needs the Data Science process. There are several areas where the innovation of Data Science could contribute to business success.

Build An Effective Team

You must create a stable team that combines 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.

Finding one tool that can do everything is difficult, 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.

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

What Is Data Visualization?

Data visualization is a method that uses static and interactive visuals 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, 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 grew succulents from a SoCal nursery to any address in the United States was looking to optimize its 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 to help their logistics team be better informed of climate updates while scheduling deliveries. The company ships 100K+ of succulents each week to every state annually.


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 predict seven weather information days in advance. They could hold the delivery process if weather issues like harsh, humid, and muggy were reported.

We used the Darksky API to deliver hyperlocal weather information for weather data, with down-to-the-minute forecasts showing when the rains will start or stop.

Our APIs delivered the following:

Data Visualization Use Cases | Mindbowser


Backed by 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 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 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 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 these were also 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, turning them 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 would help business owners to make real-time business decisions.

Key Results Achieved

  • Exploratory Data Analytics (Insights based on descriptive-analytical results and Ideation) to come 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 customers 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, data science’s benefits are using 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 India’s most lucrative and fastest-growing industries. However, the lack of data and analytics has been a major roadblock for businesses to grow.

Data analytics solutions reduce turnaround time for property buyers and sellers, get more accurate 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 is 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.

Hospitals have started to rely completely on data visualization to better understand data, from patient profiling and recording patient information to maintaining and managing satisfaction surveys and complaint registers.

Some reasons why visualization techniques in the healthcare segment must be practiced are listed below.

Healthcare analytics analyzes 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, and MangoMirror are helping connect individual data with providers and then use it to gather analytics and uncover latent trends.

Data Science For SaaS Companies

Implementing data science in SaaS companies helps ensure that your business is moving in the right direction and checks 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, the company can start thinking about advanced methods to extract more value from their data after choosing the right tools and creating a report.

Customer Demographic

Information such as the user’s location and the devices they use will tell you where and how the user spends most of their time. It will help you narrow down the users 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, keep track of the marketing channel or campaign that brought the customer to you. This data will help you identify the most useful marketing strategy for your organization and 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. Analyzing 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 to help a SaaS company keep pace with its rapidly growing business. Data science implementation can bring about high ROI for 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 solutions.

Why Does Having A Data Science Partner Make Sense?

Data science is a rapidly growing field, but there is a shortage of data scientists with the necessary skills to meet the demand. This can make it difficult for companies to build an effective data science team. Mindbowser has the experience and expertise to build a data advantage for your company quickly.

We have a team of skilled data scientists who can help you with all aspects of data science, from data collection and analysis to modeling and deployment. We also have a wide range of tools and technologies that we can use to help you achieve your data science goals.

Data Science Partner | Mindbowser

Why Choose Mindbowser As Your Data Science Partner?

Our company has a lean and agile team of full-stack data scientists, engineers and application developers who work together to accelerate the innovation and implementation of data-driven solutions.

As a data science services company, we have a lot of experience across different industries. We use 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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