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Top 10 Recession-Proof Data Science Companies To Follow In 2023

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Top 10 Recession-Proof Data Science Companies To Follow In 2023

Here is a curated list of the top recession-proof data science companies to keep an eye on in 2023

Data science is changing many different businesses. It’s hardly surprising that demand for data scientists is rising rapidly given how useful insights gained from data are enabling data science companies to make more informed decisions. It is estimated that job opportunities for data science roles will grow 22% by 2030.

The top data science companies to work for are listed below. Data science is the study of data with the objective of getting significant business insights. It combines concepts and methods from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze enormous amounts of data. No matter their size, recession-proof data science companies must have a robust data science strategy if they want to grow and keep a competitive edge against other data science companies. In this article, we discuss some of the recession-proof data science companies to follow in 2023 that’ll help you in the future.

 

1.EY

EY is a multinational accounting and professional services corporation with expertise in corporate finance, strategy, consulting, and transactions. EY leverages data and artificial intelligence to re-engineer processes, improve risk controls, and provide its customers with a competitive edge. To create new revenue possibilities, manage performance, and drive capital allocation plans, analytics and AI are integrated into their business operations.

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2.VMware

VMware is a corporation that creates virtualization and cloud computing technology solutions for multi-cloud enterprises. The development of anomaly detection, cryptographic agility, cache-adaptive algorithms, and CloudCast at VMware is driven by data science.

 

3.Walmart

In addition to developing fresh and creative ways to use various types of data, Walmart is dedicated to analyzing a tonne of already-existing data. Walmart uses extensive data mining to identify point-of-sale data trends that are subsequently used to supply chain optimization, product assortment optimization, and retail checkout improvements.

 

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4.PwC

Data is used by PwC, a global network of professional services businesses, to measure market size, determine risk premiums, and enhance its Total Impact Measurement and Management (TIMM) framework. Additionally, it provides extensive, adaptable, and competitive pay and benefits.

 

5.Splunk

Splunk is the first data-to-everything central platform in the world, enabling users to search, monitor, and analyze their data. Advanced built-ins including outlier identification, predictive analytics, event grouping, and forecasting are used by users as they engage with a web-style interface. These products support organizations in improving their cloud transition, resilience, and digital customer experience.

 

6.Numerator

For e-commerce, the numerator emphasizes consumer insights. Numerous Fortune 100 organizations use Numerator, and its data scientists assess buying environments, feelings, behaviors, and outcomes using client and customer data.

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7.Databricks

A relatively new business, Databricks has its roots in academia and the open-source community. The Lakehouse Platform, an open and integrated platform for data and AI that combines the finest features of data warehouses and data lakes, is the company’s main offering. Shell, HSBC, T-Mobile, and the H&M Group are just a few of the biggest companies in the world that depend on Databricks to allow massive-scale data engineering, business analytics, full-lifecycle machine learning, and collaborative data science.

 

8.Cloudera

A hybrid data cloud business called Cloudera makes data and analytics simple and available to everyone. Private and public clouds can be combined by clients to uncover more potent business insights. A wide range of sectors, including financial services, retail, technology, healthcare, education, and manufacturing, are served by Cloudera.

 

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9.Algorithmia

Machine learning model deployment and administration solutions are offered by Algorithmia. It makes use of top-tier enterprise-grade infrastructure, which DataRobot just purchased, to manage the production machine learning lifecycle inside already-in-place operational procedures. To operationalize their machine learning use cases, thousands of non-governmental organizations, government intelligence agencies, and Fortune 500 firms use Algorithmia.

 

10.Cropin

Cropin is an AgTech company that enables data-driven farming using Earth observation and AI. Using a multidisciplinary approach toward AI, earth observation, agriculture, meteorology, and computer sciences, it seeks to “re-imagine agriculture with data.” Cropin uses pixel-level information from satellite photography to provide useful knowledge and enhance the agricultural environment. In more than 50 countries, Cropin currently offers SaaS solutions to 225 agribusinesses as well as numerous governmental and non-governmental organizations.

The post Top 10 Recession-Proof Data Science Companies to Follow in 2023 appeared first on Analytics Insight.

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Business Analytics

The Growing Demand For Business Analytics And Data Science

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The Growing Demand For Business Analytics And Data Science

Business analytics and data science courses are expected to grow drastically in the next few years

Business analytics and data science have become powerful weapons that are helping the world to recover from pandemic losses. Data is the most precious resource in today’s technologically driven world. Data science combines different algorithms, tools, and machine learning principles. The demand for data science and business analytics will continue to grow at an exponential rate. There are many data science and analytics courses today, so there needs to be a substantial filter now to determine which online data science courses are the best. This article features the top 5 online business analytics and data science courses to take in 2023.

 

Data Analytics for Business Course – Tableau by Future Learn

Duration: 12 weeks

Fee: US$ 572.28

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Enrollment Type: Online

Through this course, You will build core quantitative data analysis skills in managing and analyzing complex data sets with Excel and SQL. It will make you learn how to make business recommendations using analytical modeling, progressing from diagnostic analysis to predictive models.

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Business Analytics Foundations by EdX

Duration: 8 months

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Fee: US$ 996.84

Enrollment Type: Online

This program provides the foundational skills necessary for a career in business and data analysis, including knowledge of scripting languages and data analysis to solve real-world problems. Learners will finish this program with the ability to automate tasks and apply statistical methods to solve problems with Python. Here you will learn to translate business requirements into problems that can be solved computationally.

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Business Analytics For Strategic Decision Making by Simplilearn

Duration: 6 months

Fee: NA

Enrollment Type: Online

This Business Analytics program will help you gain the knowledge you require to turn your organization’s data into a tactical asset to generate business value. Make data-driven strategic decisions for your organization with this Business Analytics for Strategic Decision-Making program by IIT Roorkee.

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Business Analytics Foundations: Descriptive, Exploratory, and Explanatory Analytics by Linkedin Learning

Duration: NA

Fee: US$ 10.84

Enrollment Type: Online

In this course, you are going to be using two sets of data as examples and use cases. The first data set is about heart diseases, specifically heart disease measurements for a set of individuals. It contains popular measures like blood pressure, cholesterol, sugar, et cetera. This is used to code examples during the lecture videos. The second data set is about email campaigns and conversions. It is about a business that sends out emails periodically to potential customers with offers for their products.

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Get Ahead in Business Analytics and Analysis by Linkedin Learning

Duration: 21 hours

Fee: NA

Enrollment Type: Online

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In this course, you will get an overview of the business analytics framework that applies to topics like problem framing, stakeholder analysis, data wrangling, visualizations, descriptive statistics, exploratory analysis, and more. Here you will learn how business analysis really works—and how you can use it to improve your organization.

Enroll Here

The post The Growing Demand for Business Analytics and Data Science appeared first on Analytics Insight.

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Top 10 Unimagined Uses Of Data Science For Businesses In 2023

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Top 10 Unimagined Uses Of Data Science For Businesses In 2023

The top uses of data science for businesses that can have a great impact in the future

A growing field that combines computer science and statistics is known as data science. By combining multiple fields, this multidisciplinary branch grows and enables businesses to use granular data for decision-making. There are several uses of data science in various sectors and industries and growth of data science is imminent.

Data science for businesses in 2023 has not suddenly become more important. We can now forecast outcomes in minutes rather than the many hours it could take a human to process them thanks to quicker processing and less expensive storage. Data science in businesses lets you earn a staggering US$124,000 per year, and they credit it to the lack of qualified workers. This is the cause of the record-breaking interest in the Data Science with Python Language Course! Through this article, we present to you the top uses of data science that explore a variety of fields, including the following:

 

1.Data Science in Manufacturing

Nearly all medium and big manufacturing facilities use data to pinpoint the specific needs and requirements of different manufacturing goods. The lines separating the physical and digital worlds have become hazier as a result of the use of data science in industrial industries. Manufacturing industries can determine which aspect of manufacturing needs more attention and labor through data science.

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2.Data Science for Genomics

The field of medical science known as genomics studies and analyses various genomes that have been sequenced. Genes are found in all living things, including humans. These genes contain DNA as well as additional existential qualities that characterize each organism. Following the completion of the Human Genome Project, scientists and researchers are working harder to advance genetic engineering. To gain insightful knowledge from data, they combined the concepts of big data, data science, and statistical analysis at that point.

 

3.Data Science in Transportation

This sector uses data science to provide in-depth analyses of driver behavior, fuel use, vehicle monitoring, etc. By enhancing vehicle performance, providing drivers with more security, paving the way for the development of autonomous vehicles, and enhancing the documentation of routes, traffic, weather, and other factors that affect the journey, data science and machine learning are significantly improving the safety of the transportation system.

 

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4.Customer Behavioral Analytics

Data analysis uses user data to understand how customers behave toward a specific product as well as their aspirations, interests, and other relevant data that can be useful to the company. This information aids in forecasting consumer behavior and how they will use a certain product.

 

5.Data Science in Recommender Systems

Recommender systems are a common use of data science for a variety of applications involving the provision of recommendations. They work with vast and complex data margins and give higher priority to the parts of the application or system that the user would find more engaging. The analysis of these user choices and data by data science is crucial to the operation of the recommendation algorithm.

 

6.Data Science in Transportation and Logistics

While using public transportation and biking can both reduce emissions from driving, data science can do the same by maximizing road routes. Even among businesses that aren’t specifically environmentally conscious, data-driven route changes can help save thousands of gallons of gas when applied to hundreds of trips and vehicles.

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7.Data Science in Sports

The Oakland Athletics’ recruitment budget in the early 2000s was so low that the team was unable to bring in top talent. They were at least unable to acquire players that other teams thought were of high caliber. To foresee player potential and create a potent team despite their limited resources, the general manager redefined quality by employing in-game statistics other teams ignored. His approach became more successful after it helped the A’s get to the postseason.

 

8.Data Science in Government

The U.S. government’s agencies have access to more data than Google and Meta put together, even though few consider it to be “very online.” Government agents can seek warrants to access data from any American data warehouse in addition to the agencies’ databases of ID photographs, fingerprints, and phone activities.

 

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9.Data Science in E-Commerce

Everybody in a particular town used to shop at the same mall, which was a real location with some indoor fountains, a jewelry store, and most likely a Body Shop. Based on the data profiles of their customers, online retailers frequently automatically customize their web shops.

 

10.Data Science in Social Platforms

The emergence of social networks has fundamentally changed how individuals interact. On Venmo, romantic connections develop in full view. Meta engineers have access to users’ birthday invitation lists. There are huge online data trails left by friendship, acquaintanceship, and workplace relationships.

The post Top 10 Unimagined Uses of Data Science for Businesses in 2023 appeared first on Analytics Insight.

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Top 10 New Billionaires That Data Science Has Made Over The Decade

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Top 10 New Billionaires That Data Science Has Made Over The Decade

Big data and big money go hand in hand. An increasing number of tech industry titans who have made it on the list are mainly through their investments in big data technology and applications. Have a look at the top 10 New Billionaires that Data Science has made over the decade.

 

Travis Kalanick

Travis Kalanick used to work at Uber and stepped back from it in the wake of numerous scandals. Uber has up-ended the process of hailing and hiring short trips. The reason why Uber tasted success in a short span of time is because of the data wonks who build systems and algorithms to analyze and predict demand and dynamically put a price on trips. Travis made a good chunk of money (so much so that he turns out to be a billionaire) while he worked in the field of data with Uber.

 

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Vincent Viola

The story of Vincent Viola is quite interesting. He is the son of a truck driver from Brooklyn. Who would have thought that he would be one of those big data billionaires that people would look up to? This data billionaire founded Virtu Financial which ultimately resulted in Viola making billions. Virtu Financial is into the high-speed analysis of stock trade data and algorithmic prediction.

 

Alexander Karp

Alexander Karp undoubtedly makes it to the list of the top 10 New Billionaires that Data Science has made over the decade for the sole reason that he brought Palantir into existence. This big data company has made headlines for all the right reasons – making billionaires, being one among them.

 

Lucy Peng

Amazon is not prominent in China. Thanks to Alibaba, China’s straightforward answer to Amazon in the e-commerce world. Lucy Peng is one such billionaire that the company has minted. She is extremely passionate about data and has currently taken up the role of executive chair at AliPay, the world’s largest mobile, and online payment platform.

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Reed Hastings

Who doesn’t know Netflix? Well, Netflix is everyone’s go-to place for movies and series as it eliminated the necessity of you relying on trusted friends or media sources for the same.  Netflix was founded by Reed Hastings and made the latter a billionaire in the least possible time. The careful tracking and analysis of your individual viewing habits by Reed’s company did sheer magic!

 

Lee Sang-Hyuk

Lee Sang-Hyuk is the brain behind Yello Mobile boasting of being a massive mobile media company with diverse big data plays such as mobile dating, online payments, and even an automated wake-up call service. This Korea-based tech billionaire came to the big data world quite naturally and there has been no looking back since then. 

 

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Patrick Collison

Patrick Collison had a clear-cut idea – leveraging big data to make online payment processing safe and affordable. This gave birth to Stripe, a company that makes use of automation and machine learning-based fraud detection to sniff out sketchy transactions before they can do damage. In no time, Patrick Collison became a billionaire who deserves appreciation.

 

Forrest Li

Forrest Li is the founder and CEO of Asia’s most valuable tech startup. This data-oriented mind has a brilliant idea put forth – AirPay mobile payment system that is expected to take Asia by storm. Forrest Li’s Garena is a gaming company that forms the foundation of Sea Limited.

 

John Collison

Co-founder and president of Stripe, John comes from a hospitality background. He was born and raised in Ireland. His parents ran a lakeside hotel. John along with his brother came up with the idea of Stripe, a company that lets businesses and individuals accept payments over the internet. A fun fact to note is that it was during the brothers’ vacation in South America that the company made its first payment.

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Kevin Systrom

Systrom has made it to the list because Instagram, his invention has made headlines for the popularity it gained. This photo-sharing service was anointed as the chosen one in the social media space when Facebook purchased the company in 2012. Kevin’s growing bank balance is good enough an indication as to why he makes it to the list of top billionaires in the field of data.

The post Top 10 New Billionaires that Data Science has Made Over the Decade appeared first on Analytics Insight.

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