Top 10 Successful Data Science Companies in 2022

on Data Analytics, Data Driven, Data Processing, Data Science • July 14th, 2021 • Write for Hevo

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A quick look at today’s corporate and commercial sector, and you are bound to come across the term Data Science or Data Analytics more than once. This is simply because Data has become a vital component of any successful 21st-century organization. Data Science is a field in Information Technology that focuses on extracting insights from Data (Structured and Unstructured Data) and applying the knowledge and actionable insights in solving problems. 

On a complete walkthrough of this article, you will gain a decent understanding of Data Science along with its needs and applications, key steps, and skills involved in the Data Science Project and come across the top 10 successful Data Science Companies in 2021.

Table of Contents

Introduction to Data Science

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Data Science is an interdisciplinary topic that combines Statistics, Computer Science, and Machine Learning techniques to extract insights from Structured and Unstructured Data. Data Science is one of the decade’s fastest-growing, most complex, and well-paying careers. Upon understanding its importance, numerous Data Science Companies have emerged to provide Data-Driven solutions across industries.

Key Components of Data Science

A majority of the top Data Science Companies follow a similar pattern to proceed in a Data Science Project. The key components in any Data Science Project are: 

Step 1: Data Exploration 

Data Exploration is the most crucial phase as it takes the most time for all the Data Science Companies. Data Exploration takes up around 70% of the complete project duration. Since Data is the most important component of Data Science, Data is rarely available in a well-formatted way. The Data often contains a significant amount of noise. The term “noise” refers to a large amount of unnecessary Data. 

This process entails Data Sampling and Transformation, during which evaluation of Observations (Rows) and Features (Columns) are done and Statistical methods are used to reduce noise. This stage is also used to evaluate the relationship between Distinct Features in the Dataset; “relationship” means whether the Features are dependent or independent of one another, and whether or not there are missing values in Data. All the Data Science Companies spend most of their time in Data Exploration.

Step 2: Data Modelling

So far, the Data has been prepared and is ready to go. This is the second step taken by Data Science Companies, in which Machine Learning Algorithms come into the picture. This is where the Data is fitted into the model. The Model is chosen based on the type of Data selected and the business requirement to be fulfilled. For example, the model chosen for proposing an article to a consumer will be different from the model necessary for estimating the number of articles sold on a given day. 

Step 3: Testing the Model

This is the following step, and it is critical to the model’s success. The model is tested with Test Data to ensure that it is accurate and has other desirable properties, and necessary changes are made to the model to achieve the intended result. If we do not achieve the requisite precision, we can return to Step 2 (Data Modelling), choose an alternative model, and then repeat Step 3 and then select the model that produces the best results for the business. This is a very important step for Data Science Companies as the accuracy of the model is found at this stage.

Step 4: Deploying Models

The model which provides the best result based on test findings is completed and deployed in the production environment whenever the desired result is achieved through proper testing as per the business needs.

Key Skills Required in Data Science

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All Data Science Companies expect the ideal candidate to know some skills. Below, are the skills one should know before applying to any Data Science Organization:

1) Statistics and Probability 

Data Science is built on the foundations of Statistics and Probability. Probability Theory is quite useful in formulating predictions. Data Science relies heavily on Estimations and Projections. We make Estimations for further examination with the use of Statistical approaches. As a result, Statistical approaches are heavily reliant on Probability Theory. Statistics and Probability are solely based on Data.

2) Programming Skills

Python is the most prevalent coding language required in the Data Science profession, however other programming languages such as R, Perl, C/C++, SQL, and Java are also used. Data Scientists can use these programming languages to arrange Unstructured Data Collections.

3) Data Visualization Skills

People skim and skip the most essential stories in the newspaper, but the ones that people read are mostly Sketches. It is a human concept to see something and have it registered in one’s mind. The complete Dataset, which may number in the hundreds of pages, can be reduced to two or three Graphs or Plots. To create Graphs, one must first visualize the Data Patterns. Microsoft Excel is a fantastic program that generates the appropriate Charts and Graphs based on our requirements. Tableau, Metabase, and Power BI are some examples of other Data Visualisation and Business Intelligence tools.

4) Machine Learning and Deep Learning 

Machine Learning is a must-have ability for any Data Scientist. Predictive Models are created using Machine Learning. For example, if you want to forecast how many clients you’ll have in the upcoming month based on the previous month’s data, you’ll need to employ Machine Learning techniques. The Data Science Companies are always trying to improve the accuracy of Machine Learning and Deep Learning Models they build. 

5) Communication Skills

You must communicate your findings to a group of Teammates or Senior Management. Communication allows us to rise above what everyone is fighting for. Being a competent communicator allows you to convey ideas and identify any Data Contradictions. In a Project, in Data Science Companies presentation skills are crucial for showcasing Data Discoveries and planning the future.

Understanding the Need for Data Science

All Data Science Companies understand the need for Data Science and try to provide their services across all industries. Below are the two main needs which non-Data-driven companies should look upon:

  • Historical Data: With Data Science housing strong tools, it assists us in extracting insights from past Data. It aids in the optimization of the business, hiring the right people, and the generation of more money, as Data Science enables you to make better business decisions in the future.
  • Business Strategy: Data Science Companies can better create and advertise their products by narrowing down their target market. Consumers can use Data Science to find better products, especially on E-Commerce websites that use a Data-driven recommendation engine.

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Top 10 Successful Data Science Companies in 2022

The Data Science Companies listed below are amongst the best companies for Data Science services.

1) MuSigma

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With a Unicorn status in the United States, MuSigma is amongst the world’s largest pure-play Big Data Analytics and Decision Sciences companies. Through a unique ecosystem that brings together People, Processes, and Platforms, MuSigma collaborates with over 140 Fortune 500 firms. There are currently 3500 Data Scientists working for them around the world. MuSigma has been named Walmart’s Supplier of the Year on four occasions, as well as Microsoft’s preferred Analytics partner. Thus, it is one of the top companies for Data Science around the globe.

For further information on MuSigma, visit the official website here.

2) Fractal Analytics

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Fractal is one of the most well-known Artificial Intelligence and Data Science Companies. Fractal’s objective is to employ AI and help the world’s most admired Fortune 500 firms by powering every human decision in the enterprise.

Qure.ai, which helps radiologists make better diagnostic decisions, Cuddle.ai, which helps CEOs and Senior Executives make better tactical and strategic decisions, Theremin.ai, which helps investors make better investment decisions, and Eugenie.ai, which helps find anomalies in high-velocity Data, are all Fractal products.

For further information on Fractal Analytics, visit the official website here.

3) Bridgei2i Analytics

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BRIDGEi2i is a trusted partner for businesses all around the world for driving digital transformation efforts. They solve difficult business problems with contextual AI-powered insights and achieve Digital Transformation results.

For further information on Bridgei2i Analytics, visit the official website here.

4) Tiger Analytics

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Tiger Analytics is breaking new grounds in how AI and Analytics may be used to solve some of the world’s most difficult problems. For several Fortune 500 organizations, they have created custom solutions based on Data and Technology. They have offices in numerous cities across the United States, the United Kingdom, India, and Singapore, as well as a large worldwide virtual workforce.

They’ve won numerous honours, including being named a Forrester Research Leader and Financial Times’ fastest-growing IT businesses. They routinely appear on lists of the “Best Analytics Firms.” Thus, they can be considered one of the best Data Science Organizations to work for. 

For further information on Tiger Analytics, visit the official website here.

5) LatentView

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LatentView Analytics is one of the prominent worldwide Analytics and Data Science Companies that help businesses achieve Digital Transformation and build a competitive advantage through Data. With Analytics Solutions they provide a 360-degree perspective of the digital consumer, power Machine Learning capabilities, and aid AI ambitions.

Predictive Modeling, Marketing Analytics, Big Data Analytics, Advanced Analytics, Web Analytics, Data Science, Data Engineering, Artificial Intelligence, and Machine Learning Applications are just a few of the areas in which they excel.

For further information on LatentView, visit the official website here.

6) Absolutdata

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Absolutdata, an Infogain company, combines cutting-edge AI and Machine Learning with its legacy in analytical frameworks, business expertise, and technology to create scalable business impact throughout the enterprise. The enterprise-focused NAVIK AI Platform combines AI, Data, and Analytics to serve as the intelligence layer for forward-thinking businesses. 

The NAVIK AI Platform provides a range of pre-built AI solutions, or it can be licensed and modified using a strong bench of Data Scientists and Engineers.

For further information on Absolutdata, visit the official website here.

7) Innovaccer

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Innovaccer Inc is a leading Healthcare Data activation platform firm that uses cutting-edge Analytics and transparent, clean, and accurate Data to deliver more efficient and effective Healthcare. Innvoaccer’s goal is to help medical-related organizations make powerful decisions and achieve strategic goals based on key insights and predictions from their Data by simplifying complex Data from all points of care and streamlining the information. It is the only company amongst the top Data Science Companies which only focuses on Healthcare Solutions.

For further information on Innovaccer, visit the official website here.

8) TEG Analytics

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TEG Analytics is a Data Science as a Service Company that helps businesses make better decisions by combining Business, Technology, and Applied Mathematics. Their goal is to provide Insights at Business Speed, hence, being an excellent Data Science Organization.

TEG aids market leaders in defining Analytics-driven strategy across industry verticals (such as Retail and Consumer Packaged Goods, and Healthcare). TEG has carved out a position for itself in the field of Sales and Marketing Analytics.

For further information on TEG Analytics, visit the official website here.

9) Teradata

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Teradata is a Multi-Cloud Data platform that helps businesses solve Data challenges from start to finish. Only Teradata gives you the flexibility to handle today’s enormous and mixed Data workloads, making your Data more accessible to everyone without putting your Data in danger.

For further information on Teradata, visit the official website here.

10) Impact Analytics

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Impact Analytics creates AI-powered retail automation technologies with a 360-degree view to help businesses automate complex procedures and turn Data into insights. To create solutions that lure clients, they combine business expertise from top-tier strategy consultants, advanced Machine Learning techniques from skilled Data Scientists, and cutting-edge product development from expert Application Designers and Developers. Impact Analytics comes amongst the top companies for Data Science.

For further information on Impact Analytics, visit the official website here.

Understanding the Applications of Data Science

All Data Science Companies provide their expertise in every industry, but the main areas where Data Science techniques are being used are: 

  • Marketing: Marketing has a lot of potential; for example, a better pricing approach. Data Science-driven pricing can help companies like Uber and E-Commerce enterprises increase their profitability.
  • Healthcare: Wearable Data is being used to monitor and prevent health issues. The information gathered from the body can be utilized in healthcare to avoid future disasters.
  • Banking and Finance: Data Science can be used in the Banking sector. Fraud Detection is one of the well-known applications in this sector, which can help banks lower their non-performing assets.
  • Government Policies: Government can use Data Science to better design policies that cater to the interests and desires of the people, based on Data obtained through surveys and other official sources.

Conclusion

The list above contains the top 10 successful Data Science Companies in 2021. The demand for professionals in Data Science is increasing as Data becomes a core decision-making influencer in today’s business world. The first step in implementing any Data Science algorithm is integrating the Data from all sources. However, most businesses today have an extremely high volume of Data with a dynamic structure that is stored across numerous applications. Creating a Data Pipeline from scratch for such Data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased Data Volume and Schema Variations. Businesses can instead use automated platforms like Hevo.

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