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The hype for highest paying companies for Data Scientists attracts more Aspirants

The 

Top companies paying high salaries to data scientists

Data Scientist’s salary: US$124,333 Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).  

Data Scientist’s salary: US$162,931 Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and automatable.  

Data Scientist’s salary: US$157,798 Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and expertise.  

Data Scientist’s salary: US$146,032 Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and philosophy.  

Data Scientist’s salary: US$137,668 Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the entity.  

Data Scientist’s salary: US$197,500 Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing cost.  

Data Scientist’s salary: US$197,800 Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing initiatives.  

Data Scientist’s salary: US$173,503 Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation service.  

Data Scientist’s salary: US$145,172 Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several devices.  

Data Scientist’s salary: US$129,833

The data science landscape is filled with opportunities spanning diverse industries. As new technologies are being added to the digital sphere year-on-year, the transformation is likely to continue into the coming decade. Owing to the increasing influence of technology in our daily lives, the demand for data science jobs has drastically spiked. The openings for data scientists are expected to go beyond 2023, adding more than 150,000 jobs in the coming years. This trend is a natural response of the digital age for adding more data into its ecosystem. Besides paying high salaries, data science jobs are demanding when it comes to talent requirements and innovation. Data science requires the expertise of professionals, who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. On a positive note, the nature of data science jobs allows an individual to take on flexible remote works and also to be self-employed. Despite the leniency, the hype for highest paying companies for data scientists remains at the top. In this article, Analytics Insight has listed the top 10 companies that are paying a fortune for data scientists in chúng tôi Scientist’s salary: US$124,333 Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).Data Scientist’s salary: US$162,931 Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and chúng tôi Scientist’s salary: US$157,798 Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and chúng tôi Scientist’s salary: US$146,032 Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and chúng tôi Scientist’s salary: US$137,668 Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the chúng tôi Scientist’s salary: US$197,500 Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing chúng tôi Scientist’s salary: US$197,800 Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing chúng tôi Scientist’s salary: US$173,503 Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation chúng tôi Scientist’s salary: US$145,172 Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several chúng tôi Scientist’s salary: US$129,833 Genentech is a biotechnology company that discovers, develops, manufactures, and commercializes medicines to treat patients. The company offers medicine for the prevention of oncology, immunology, metabolism, monoclonal antibodies, small molecules, tissue repair, and virology, as well as conducts scientific research to produce biologic medicines. The company uses its data science capabilities to enhance its performance in the market by unraveling effective medicines.

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Gradient Boosting Machine For Data Scientists

Objective

Boosting is an ensemble learning technique where each model attempts to correct the errors of the previous model.

Learn about the Gradient boosting algorithm and the math behind it.

Introduction

In this article, we are going to discuss an algorithm that works on boosting technique, The Gradient Boosting algorithm. It is more popularly known as Gradient boosting Machine or GBM.

Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.

The models in Gradient Boosting Machine are building sequentially and each of these subsequent models tries to reduce the error of the previous model. But the question is how does each model reduce the error of the previous model? It is done by building the new model over errors or residuals of the previous predictions.

This is done to determine if there are any patterns in the error that is missed by the previous model. Let’s understand this through an example.

Here we have the data with two features age and city and the target variable is income. So, based on the city and age of the person we have to predict the income. Note that throughout the process of gradient boosting we will be updating the following the Target of the model, The Residual of the model, and the Prediction.

Steps to build Gradient Boosting Machine Model

To simplify the understanding of the Gradient Boosting Machine, we have broken down the process into five simple steps.

Step 1

The first step is to build a model and make predictions on the given data. Let’s go back to our data, for the first model the target will be the Income value given in the data. So, I have set the target as original values of Income.

Now we will build the model using the features age and city with the target income. This trained model will be able to generate a set of predictions. Which are suppose as follows.

Now I will store these predictions with my data. This is where I complete the first step.

Step 2

The next step is to use these predictions to get the error, which will be used further as a target. At the moment we have the Actual Income values and the predictions from the model1. Using these columns, we will calculate the error by simply subtracting the actual income and the predictions of income. A shown below.

As we mentioned previously the successive models focus on the error. So the errors here will be our new target. That covers up step two.

Step 3

In the next step, we will build a model on these errors and make the predictions. Here the idea is to determine, Is any hidden pattern in the error.

So using the error as target and the original features Age and City, we will generate new predictions. Note that the predictions, in this case, will be the error values, not the predicted income values, since our target is the error. Let’s say the model gives the following predictions

Step 4

Now we have to update the predictions of model1. We will add the prediction from the above step and add that to the prediction from model1 and name it Model2 Income.

As you can see my new predictions are closer to my actual income values.

Finally, we will repeat steps 2 to 4, which means we will be calculating new errors and setting this new error as a target. We will repeat this process till the error becomes zero or we have reached the stopping criteria, which says the number of models we want to build. That’s the step-by-step process of building a gradient boosting model.

In a nutshell, We build our first model that has features x and target y, let’s called this model H0 that is a function of x and y. Then we build the next model on the errors of the last model and a third model on the errors of the previous model and so on. Till we build n models.

Each successive model works on the errors of all previous models to try and identify any pattern in the error. Effectively, I can say that each of these models is individual functions having independent variable x as the feature and the target is the error of the previous combined model.

So to determine the final equation of our model, we build our first model H0, which gave me some predictions and generated some errors. Let’s call this combined result F0(X).

Now we created our second model and added new predicted errors to F0(X), this new function will be F1(X). Similarly, we will build the next model and so on, till we had n models as shown below.

So, at every step, we are trying to model the errors, which helps us to reduce the overall error. Ideally, we want this ‘en’ to be zero. As you can see each model here is trying to boost the performance of the model hence we use the term boost.

But why we use the term gradient, here is the catch. Instead of adding directly these models, we add them with weight or coefficient, and the right value of this coefficient is decided using the gradient boosting technique.

Hence, a more generalized form of our equation will be as follows.

The math behind Gradient Boosting Machine

I hope, now you have a broad idea of how gradient boosting works. Here onward, we will be focusing on how the value of Yn is calculated.

We will use the gradient descent technique to get the values of these coefficients gamma(Y), such that we minimize the loss function. Now let’s dive deeper into this equation and understand the role of the loss function and gamma.

Here, the loss function we are using is (y-y’)2. y is the actual value and y’ is the final predicted value by the last model. So, we can replace y’ with Fn(X) which represents the actual target minus the updated predictions from all the models we have built so far.

Partial Differentiation

I believe you would be familiar with the gradient descent process as we are going to use the same concept. We will differentiate the equation of L with respect to Fn(X), you will get the following equation, which is also known as pseudo residual. Which is the negative gradient of the loss function.

To simplify this, we will multiply both sides with -1. The result will be something like this.

Now, we know the error in our equation of Fn+1(X) is the actual value minus updated predictions from all the models. Hence, we can replace the en in our final equation with these pseudo residuals as shown in the image below.

So this is our final equation. The best part about this algorithm is that it gives you the freedom to decide the loss function. The only condition is that the loss function should be differentiable. For ease of understanding, we used a very simple loss function (y-y’)2 but you can change it to a hinge loss or logit loss or anything.

The aim is to minimize the overall loss. Let’s see what would be the overall loss here, it will be the loss up to model n plus the loss from the current model we are building. Here is the equation.

In this equation, the first part is fixed but the second part is the loss from the model we are currently working on. The loss of this model still can not be changed but we can change the gamma value. Now we need to select the value of gamma such that the overall loss is minimized and this value is selected using the gradient descent process.

So the idea is to reduce the overall loss by deciding the optimum value of gamma for each model that we build.

Gradient Boosting Decision Tree

I talk about a special case of gradient boosting i.e Gradient boosting decision tree (GBDT). Here, each model would be a tree and the value of gamma will be decided at each leaf-level, not at the overall model level. So as sown in the following image each leaf would have a gamma value.

That’s how Gradient Boosting Decision Tree work.

End Notes

Boosting is a type of ensemble learning. It is a sequential process where each model attempts to correct the errors of the previous model. This means every successive model is dependent on its predecessors. In this article, we saw the gradient boosting algorithm and the math behind it.

As we have a clear idea of the algorithm, try to build the models and get some hands-on experience with it.

If you are looking to kick start your Data Science Journey and want every topic under one roof, your search stops here. Check out Analytics Vidhya’s Certified AI & ML BlackBelt Plus Program

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Top 10 Data Scientists Of 2023: Blowing Minds You Should Follow

Ever since the importance of artificial intelligence (AI) and machine learning skyrocketed, data science has seen constant hype in the technology sector. Data science has introduced attention that perhaps no other tech revolution has managed to. The technology is popular among computer science students who have a knack for mathematics and analytics. More and more people are also showing a big interest in data science roles, especially, for data scientist. Data scientists are responsible for tasks right from interpreting large datasets to putting them to use for making data-driven decisions. Analytics Insight has listed top 10 data scientists who made their way as maestros to 2023 and from whom the aspiring people can take cues.

Top 10 data scientist of 2023 Dr DJ Patil

Dhanurjay Patil is a former LinkedIn executive (from 2008-2011) who later served as the White House’s Chief Data Scientist under President Barack Obama. He is well-known as the first-ever data scientist in the US. He played a big role in digitizing the administrative works in Obama’s period. Remarkably, Patil coined the word ‘data scientist’ and was in limelight for over a decade. In 2012, he co-authored a paper titled ‘Data Scientist, the sexiest job of the 21st century.’ Patil earlier served as the Vice President of Products at RelateIQ, which was later acquired by chúng tôi Chief Product Officer of Color Labs, and Head of Data Products and Chief Scientist of LinkedIn. Twitter- 

Dean Abbott

Dean Abbott is the Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics in San Diego, California. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience in applying data mining algorithms, data preparation techniques, and data visualization methods to real-world data-intense problems including fraud detection, risk modelling, text mining, personality assessment, response modelling, survey analysis, planned giving, and predictive toxicology. He is the author of ‘Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst’ and co-author of the ‘IBM SPSS Modeler Cookbook.’ Twitter- 

Randy Leo

Randy Leo is an innovative data scientist who has his own blog site ‘ClaoudML’ to publish useful data science and machine learning resources free of cost. Besides, he is also a data science mentor at an E-Learning platform called Data Science Dream Job where he helps aspiring data scientists in the learning process and getting jobs. LinkedIn- 

Eric Webner

Eric Webner is a Senior Data Scientist at LinkedIn, an online educator and an epitome of lifelong learning. Webner holds a Bachelor degree in Mathematics from the University of Wisconsin, two Masters degree in Math and Business Analytics at Arizona State University and the University of Minnesota, and a PhD from ASU. Previously, he held roles such as the Director of Data Science and Analytics for HealthEast, Principal of Data Management and Data Science at CoreLogic. LinkedIn- 

Yoshua Bengio

Yoshua Bengio is a full professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (LISA), CIFAR Fellow in the Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms. He also holds the NSERC-Ubisoft industry chair. Bengio’s main research ambition is to understand the principles of learning that yield intelligence. LinkedIn- 

Corinna Cortes

Corinna Cortes is the Head of Google Research, New York, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Cortes spent more than a decade at AT&T Labs- Research, formerly AT&T Bells Labs, where she held a distinguished research position. Cortes’s research work is well-known for its contributions to the theoretical foundations of support vector machines (SVMs).  

Sarita Digumarti

Sarita Digumarti is the Co-founder and COO at Jigsaw Academy. She has over ten years of extensive analytics and consulting experience across diverse domains including retail, healthcare, and financial services. Digumarti has worked in both India and the US, helping clients tackle complex business problems applying analytical techniques. LinkedIn- 

Yann LeCun

Yann Lecun is the Director of AI Research at Facebook, and a Silver Professor of Dara Science, Computer science, Neural Science, and Electronical Engineering at New York University, affiliated with the NYC Center for Data Science. As a professor, LeCun has been working on machine learning, computer vision, robotics, artificial intelligence, computational neuroscience and related topics. LinkedIn- 

Nando de Freitas

Nando de Freitas is an Associate Professor in the Department of Computer Science at the University of British Columbia. Freitas is a specialist in machine learning with an emphasis on neural networks, Bayesian optimisation and inference, and deep learning. As the principle scientist at Google DeepMind, he helped the organisation in its mission to use technologies form widespread public benefit and scientific discovery, while ensuring safety and ethics. LinkedIn- 

Alex Sandy Pentland

Alex Paul ‘Sandy’ Pentland is an American computer scientist, the Toshiba Professor at MIT, and serial entrepreneur. He directs MIT Connection Science, an MIT-wide initiative, and previously helped create and direct the MIT Media Lab and the MediaLab Aisa in India. He is one of the most-cited computational scientists in the world.

Dhanurjay Patil is a former LinkedIn executive (from 2008-2011) who later served as the White House’s Chief Data Scientist under President Barack Obama. He is well-known as the first-ever data scientist in the US. He played a big role in digitizing the administrative works in Obama’s period. Remarkably, Patil coined the word ‘data scientist’ and was in limelight for over a decade. In 2012, he co-authored a paper titled ‘Data Scientist, the sexiest job of the 21st century.’ Patil earlier served as the Vice President of Products at RelateIQ, which was later acquired by chúng tôi Chief Product Officer of Color Labs, and Head of Data Products and Chief Scientist of LinkedIn. Twitter- @dpatil LinkedIn- DJ Patil Dean Abbott is the Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics in San Diego, California. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience in applying data mining algorithms, data preparation techniques, and data visualization methods to real-world data-intense problems including fraud detection, risk modelling, text mining, personality assessment, response modelling, survey analysis, planned giving, and predictive toxicology. He is the author of ‘Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst’ and co-author of the ‘IBM SPSS Modeler Cookbook.’ Twitter- @deanabb LinkedIn- Dean Abbott Randy Leo is an innovative data scientist who has his own blog site ‘ClaoudML’ to publish useful data science and machine learning resources free of cost. Besides, he is also a data science mentor at an E-Learning platform called Data Science Dream Job where he helps aspiring data scientists in the learning process and getting jobs. LinkedIn- Randy Lao Eric Webner is a Senior Data Scientist at LinkedIn, an online educator and an epitome of lifelong learning. Webner holds a Bachelor degree in Mathematics from the University of Wisconsin, two Masters degree in Math and Business Analytics at Arizona State University and the University of Minnesota, and a PhD from ASU. Previously, he held roles such as the Director of Data Science and Analytics for HealthEast, Principal of Data Management and Data Science at CoreLogic. LinkedIn- Eric Weber Yoshua Bengio is a full professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (LISA), CIFAR Fellow in the Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms. He also holds the NSERC-Ubisoft industry chair. Bengio’s main research ambition is to understand the principles of learning that yield intelligence. LinkedIn- Yoshua Bengio Corinna Cortes is the Head of Google Research, New York, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Cortes spent more than a decade at AT&T Labs- Research, formerly AT&T Bells Labs, where she held a distinguished research position. Cortes’s research work is well-known for its contributions to the theoretical foundations of support vector machines (SVMs).Sarita Digumarti is the Co-founder and COO at Jigsaw Academy. She has over ten years of extensive analytics and consulting experience across diverse domains including retail, healthcare, and financial services. Digumarti has worked in both India and the US, helping clients tackle complex business problems applying analytical techniques. LinkedIn- Sarita Digumarti Yann Lecun is the Director of AI Research at Facebook, and a Silver Professor of Dara Science, Computer science, Neural Science, and Electronical Engineering at New York University, affiliated with the NYC Center for Data Science. As a professor, LeCun has been working on machine learning, computer vision, robotics, artificial intelligence, computational neuroscience and related topics. LinkedIn- Yann LeCun Nando de Freitas is an Associate Professor in the Department of Computer Science at the University of British Columbia. Freitas is a specialist in machine learning with an emphasis on neural networks, Bayesian optimisation and inference, and deep learning. As the principle scientist at Google DeepMind, he helped the organisation in its mission to use technologies form widespread public benefit and scientific discovery, while ensuring safety and ethics. LinkedIn- Nando de Freitas Alex Paul ‘Sandy’ Pentland is an American computer scientist, the Toshiba Professor at MIT, and serial entrepreneur. He directs MIT Connection Science, an MIT-wide initiative, and previously helped create and direct the MIT Media Lab and the MediaLab Aisa in India. He is one of the most-cited computational scientists in the world. LinkedIn- Alex ‘Sandy’ Pentland

Citizen Data Scientists: 4 Ways To Democratize Data Science

Analytics vendors and non-technical employees are democratizing data science. Organizations are looking at converting non-technical employees into data scientists so that they can combine their domain expertise with data science technology to solve business problems.

What does citizen data scientist mean?

In short, they are non-technical employees who can use data science tools to solve business problems.

Citizen data scientists can provide business and industry domain expertise that many data science experts lack. Their business experience and awareness of business priorities enable them to effectively integrate data science and machine learning output into business processes.

Why are citizen data scientists important now?

Interest in citizen data science is almost tripled between 2012-2024, as seen below.

Reasons for this growing interest are:

Though there is an increasing need for analytics due to increased popularity of data-driven decision making, data science talent is in short supply. As of 2023, there are three times more data science job postings than job searches.

As with any short supply product in the market, data science talent is expensive. According to the U.S. Bureau of Labor Statistics, the average data science salary is $101k.

Analytics tools are easier-to-use now, which reduces the reliance on data scientists.

Most industry analysts are also highlighting the increased role of citizen data scientists in organizations:

IDC big data analytics and AI research director Chwee Kan Chua mentions in an interview: “Lowering the barriers to allow even non-technical business users to be ‘data scientists’ is a great approach.”

Gartner defined the term and is heavily promoting it

Various solutions help businesses to democratize AI and analytics:

Citizen data scientists first need to understand business data and access it from various systems. Metadata management solutions like data catalogs or self-service data reporting tools can help citizen data scientists with this.

Automated Machine Learning (AutoML): AutoML solutions can automate manual and repetitive machine learning tasks to empower citizen data scientists. ML tasks AutoML tools can automate are

Data pre-processing

Feature engineering

Feature extraction

Feature selection

Algorithm selection & hyperparameter optimization

Augmented analytics /AI-driven analytics: ML-led analytics, where tools extract insights from data in two forms:

Search-driven: Software returns with results in various formats (reports, dashboards, etc.) to answer citizen data scientists’ queries.

Auto-generated: ML algorithms identify patterns to automate insight generation.

No/low-code and RPA solutions minimize coding with drag-and-drop interfaces which helps citizen developers place the models they prepare in production.

Sponsored

BotX’s no-code AI platform can empower citizen data scientists to build solutions faster while reducing development costs. BotX solutions allow developers and data scientists to launch apps and set infrastructure and IT systems through: 

What are best practices for citizen data science projects? Create a workspace where citizen data scientists and data science experts can work collaboratively

Most citizen data scientists are not trained in the foundations of data science. They rely on tools to generate reports, analyze data, create dashboards or models. To maximize citizen data scientists’ value, you should have teams that can support them which also includes data engineers and expert data scientists.

Train citizen data scientists

use of BI/autoML tools for maximum efficiency

data security training to maintain data compliance

detecting AI biases and creating standards for model trust and transparency so that citizen data scientists can establish explainable AI (XAI) systems.

Classify datasets based on accessibility

Due to data compliance issues, all data types should not be accessible to all employees. Classifying data sets that require limited access can help overcome this issue.

Create a sandbox for testing

Sandboxes, software testing environment, which include synthetic data and which are not connected to production environments help citizen data scientists quickly test their models before rolling them to production.

If you still have questions on citizen data science, don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Top 10 Universities Teaching Data Science For Free In 2023

Data science has created a plethora of job opportunities for aspiring data scientists and many more professions in recent years. Students and working professionals are highly interested to have a strong understanding of different aspects and elements of data science. They constantly look for online courses with 100% placement in reputed companies from different institutions offering courses on data science. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates. Let’s explore some of the top universities providing data science courses.

MIT, OpenCourseWare

MIT is one of the leading institutes for both teaching and research in the field of modern computing. In 2001, the university launched its OpenCourseWare platform. The aim of which is to make lecture notes, problem sets, exams, and video lectures, for the vast majority of its courses, available for free online.

Columbia, Applied Machine Learning

Andreas C. Muller, one of the core developers for the popular Python machine learning library Scikit-learn, is also a Research Scientist and lecturer at Columbia University. Each year he publishes all material for his ‘Applied Machine Learning Course online. All the slides, lecture notes, and homework assignments for the course are available in this Github repo.

Stanford, Seminars Free Online Courses, Harvard

Harvard University publishes a selection of completely free online courses on its website. The courses are mostly hosted by edX so you also have the option of pursuing certification for each course for a small payment.

Purdue University: Krannert School of Management

Offered through the Krannert School of Management, Purdue University’s Master of Science in Business Analytics and Information Management is a full-time program that starts every year in June and runs for three semesters. The graduate program offers three specializations in supply chain analytics, investment analytics, and corporate finance analytics.

DePaul University

DePaul University offers an MS in data science that promises to equip students with the right skills for a career in data science. The program includes a graduate capstone requirement, but you can choose between completing a real-world data analytics project, taking a predictive analytics capstone course, participating in an analytics internship, or completing a master’s thesis.

University of Rochester

The University of Rochester offers an MS in Data Science through the Goergen Institute for Data Science. The program can be completed in two or three semesters of full-time study, but the two-semester path includes a rigorous course load, so it’s recommended for students who already have a strong background in computer science and mathematics. For those without a strong background in computer science, you can take an optional summer course that will help get you up to speed before the program starts.

New York University

New York University offers an MS in Data Science (MSDS) and offers several concentrations to select from, including data science, big data, mathematics and data, natural language processing, and physics. You’ll need to earn 36 credits to graduate, which takes full-time students an average of two years to complete.

Carnegie Mellon University

Carnegie Mellon University offers a Master’s in Computation Data Science (MCDS) through the Tepper School of Business. During your first semester, you will be required to take four core courses: cloud computing, machine learning, interactive data science, and a data science seminar. By the end of the first semester, you will need to select from three concentrations, including systems, analytics, or human-centered data science. Your concentration will help inform the courses you take during the rest of the program.

North Carolina State University – Raleigh, North Carolina

North Carolina State University offers an MS in Analytics (MSA) program that’s designed as a 10-month cohort-based learning experience that focuses on teamwork and one-on-one coaching. The graduating MSA class of 2023 had a 95 percent employment rate by graduation, with an average base salary of US$98,200 per year, according to the university.

Top 10 Robotic Surgical Companies In 2023 All Over The World

Robotic Surgical Companies that are transforming the field of surgical procedures

Medical robots form a rapidly growing sector of the medical devices industry. Regardless of whether utilized for home help, crisis response, negligibly invasive medical surgery, targeted therapy, or prosthetics, they are turning out to be increasingly more widely utilized these days, transforming medical care across the globe. The most popular robotics applications in medical services and healthcare are surgical. Everything from organ transplants and gastrointestinal surgeries to spine medical procedures and urological operations are performed utilizing robots of some sort. In the United States, around 9,00,000 robot-assisted surgeries were performed in 2023. The worldwide surgical robotics market created $4.71 billion in 2023 and is assessed to reach $15.43 billion by 2029. The market is classified into three fragments, in particular, surgical systems, surgical services and instruments & accessories. Let’s look at the top 10 robotic Surgical Companies in 2023 all over the world  

Intuitive Surgical stood firm on the leading position in the United States robotic surgery market in 2023. It got FDA approval for its first-historically robotic system da Vinci® in 2000, and from that point forward, they have developed the da Vinci® framework to yield a robotic surgery empire. Other da Vinci® systems, for example, the da Vinci S® were marketed in 2006 and the da Vinci Si® in 2009, yet Intuitive’s leader product, the da Vinci Xi®, was presented in 2014. Intuitive Surgical will still be the leader by a huge edge for a considerable number of years, powered by revenue from its surgeries, services and maintenance fees.  

The Mountain View, Calif.-based organization’s robotic catheter is intended to explore the peripheral vasculature and give a conduit for the arrangement of therapeutic devices. The catheter works as a component of Hansen’s Magellan robotic system. The Sensei X Robotic system utilizes 3D catheter controls and 3D visualization to permit a surgeon to control its robotically steerable catheters for gathering electrophysiological information inside the heart atria.  

Diligent’s AI-empowered robots work with people in day-to-day scenarios. The organization’s autonomous Moxi robot can be left alone to perform tedious logistical errands in hospitals like setting up patient rooms and restocking supply rooms. Fit for navigating hospital hallways and other restricted spaces, Moxi is even permeated with social intelligence that is passed on through its head movements and LED eyes.  

Medrobotics is a Massachusetts-based organization that recently got US$20 million in financing to venture into general medical surgery and create cutting-edge robotic systems. Its Flex Robotic System got FDA approval in 2024 and permits doctors to get to anatomical areas like ear, nose, or throat with its snakelike plan and 180° path.  

CMR Surgical is a British organization, building the cutting-edge careful surgical robotic system, Versius, for minimal access medical surgery. Their vision is to make minimal invasive medical surgery universal that is easily accessible and affordable. CMR Surgical, established in 2014, has its headquarters in Cambridge and got the European CE Mark in March 2023 for the Versius Surgical Robotic System.  

Corindus’ CorPath 200 device is intended for percutaneous coronary mediations. The CorPath accompanies a radiation-protected “cockpit” for the surgeon. The CorPath 200 is the first and only robotic-assisted procedure to take into account controlled placement of coronary guidewires and stent/swell catheters from an optimized interventional cockpit. In utilizing the framework, the surgeon works CorPath 200 from behind a radiation-protected “cockpit.” Rather than being with a patient while clad in a lead apron, the surgeon is situated behind an operating station, controlling surgical devices with a range of touch-screen and joystick controls.  

Verb surgical is perhaps the most innovative robotic surgery organization on the planet and was framed as a strategic partnership between Google’s Alphabet and Johnson & Johnson’s medical devices subsidiary, Ethicon. They are zeroing in on creating a digital surgery platform that integrates robotics technology, progressed visualization, progressed instrumentation and data analytics. Such robotic surgery companies embrace big data and machine learning expertise from Google to create a digital surgical platform, which will not cost exactly the current robots in medical.  

Zimmer Biomet Robotics, once known as Medtech SA, is a robotic surgery company organization, established in 2002 in Montpellier, France. It creates robotic help for surgical procedures of the central nervous system and different applications like the knee. Its main product is ROSA, a robotic surgical assistant intended for negligibly invasive medical procedures.  

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