Trending March 2024 # Citizen Data Scientists: 4 Ways To Democratize Data Science # Suggested April 2024 # Top 10 Popular

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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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Data Science: The 10 Commandments For Performing A Data Science Project

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly. You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

It is crucial to understand the goals of the users or participants in a data science project. However, this does not guarantee success. Data science teams must adhere to best practices when executing a project in order to deliver on a clearly defined brief. These ten points can be used to help you understand what it means.

1. Understanding the Problem

Knowing the problem you are trying to solve is the most important part of solving it. You must understand the problem you are trying to predict, all constraints, and the end goal of this project.

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2. Know Your Data

Knowing what your data means will help you understand which models are most effective and which features to use. The data problem will determine which model is most successful. Also, the computational time will impact the project’s cost.

You can improve or mimic human decision-making by using and creating meaningful features. It is crucial to understand the meaning of each field, especially when it comes to regulated industries where data may be anonymized and not clear. If you’re unsure what something means, consult a domain expert.

3. Split your data

What will your model do with unseen data? If your model can’t adapt to new data, it doesn’t matter how good it does with the data it is given.

You can validate its performance on unknown data by not letting the model see any of it while training. This is essential in order to choose the right model architecture and tuning parameters for the best performance.

Splitting your data into multiple parts is necessary for supervised learning. The training data is the data the model uses to learn. It typically consists of 75-80% of the original data.

This data was chosen randomly. The remaining data is called the testing data. This data is used to evaluate your model. You may need another set of data, called the validation set.

This is used to compare different supervised learning models that were tuned using the test data, depending on what type of model you are creating.

You will need to separate the non-training data into the validation and testing data sets. It is possible to compare different iterations of the same model with the test data, and the final versions using the validation data.

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4. Don’t Leak Test Data

It is important to not feed any test data into your model. This could be as simple as training on the entire data set, or as subtle as performing transformations (such as scaling) before splitting.

If you normalize your data before splitting, the model will gain information about the test set, since the global minimum and maximum might be in the held-out data.

5. Use the Right Evaluation Metrics

Every problem is unique so the evaluation method must be based on that context. Accuracy is the most dangerous and naive classification method. Take the example of cancer detection.

We should always say “not cancer” if we want to build a reliable model. This will ensure that we are correct 99 percent of the time.

Also read: Top 10 Business Intelligence Tools of 2023

6. Keep it simple

It is important to select the best solution for your problem and not the most complex. Management, customers, and even you might want to use the “latest-and-greatest.” You need to use the simplest model that meets your needs, a principle called Occam’s Razor.

This will not only make it easier to see and reduce training time but can also improve performance. You shouldn’t try to kill Godzilla or shoot a fly with your bazooka.

7. Do not overfit or underfit your model

Overfitting, also called variance, can lead to poor performance when the model doesn’t see certain data. The model simply remembers the training data.

Bias, also known as underfitting, is when the model has too few details to be able to accurately represent the problem. These two are often referred to as “bias-variance trading-off”, and each problem requires a different balance.

Let’s use a simple image classification tool as an example. It is responsible for identifying whether a dog is present in an image.

8. Try Different Model Architectures

It is often beneficial to look at different models for a particular problem. One model architecture may not work well for another.

You can mix simple and complex algorithms. If you are creating a classification model, for example, try as simple as random forests and as complex as neural networks.

Interestingly, extreme gradient boosting is often superior to a neural network classifier. Simple problems are often easier to solve with simple models.

9. Tune Your Hyperparameters

These are the values that are used in the model’s calculation. One example of a hyperparameter in a decision tree would be depth.

This is how many questions the tree will ask before it decides on an answer. The default parameters for a model’s hyperparameters are those that give the highest performance on average.

Also read: Top 9 WordPress Lead Generation Plugins in 2023

10. Comparing Models Correctly

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly.

You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

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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Data Science Interview Series: Part

This article was published as a part of the Data Science Blogathon.

Introduction

Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. Apart from these, an interviewer asks you about your projects and their objective. In short, interviewers focus on basic concepts and projects.

This article is part 1 of the data science interview series and will cover some basic data science interview questions. We will discuss the interview questions with their answers:

What is OLS? Why, and Where do we use it?

OLS (or Ordinary Least Square) is a linear regression technique that helps estimate the unknown parameters that can influence the output. This method relies on minimizing the loss function. The loss function is the sum of squares of residuals between the actual and predicted values. The residual is the difference between the target values and forecasted values. The error or residual is:

Minimize ∑(yi – ŷi)^2

Where ŷi is the predicted value, and yi is the actual value.

We use OLS when we have more than one input. This approach treats the data as a matrix and estimates the optimal coefficients using linear algebra operations.

What is Regularization? Where do we use it?

Regularization is a technique that reduces the overfitting of the trained model. This technique gets used where the model is overfitting the data.

Overfitting occurs when the model performs well with the training set but not with the test set. The model gives minimal error with the training set, but the error is high with the test set.

Hence, the regularization technique penalizes the loss function to acquire the perfect fit model.

What is the Difference between L1 AND L2 Regularization?

L1 Regularization is also known as Lasso(Least Absolute Shrinkage and Selection Operator) Regression. This method penalizes the loss function by adding the absolute value of coefficient magnitude as a penalty term.

Lasso works well when we have a lot of features. This technique works well for model selection since it reduces the features by shrinking the coefficients to zero for less significant variables.

Thus it removes some features that have less importance and selects some significant features.

L2 Regularization( or Ridge Regression) penalizes the model as the complexity of the model increases. The regularization parameter (lambda) penalizes all the parameters except intercept so that the model generalizes the data and does not overfit.

Ridge regression adds the squared magnitude of the coefficient as a penalty term to the loss function. When the lambda value is zero, it becomes analogous to OLS. While lambda is very large, the penalty will be too much and lead to under-fitting.

Moreover, Ridge regression pushes the coefficients towards smaller values while maintaining non-zero weights and a non-sparse solution. Since the square term in the loss function blows up the outliers residues that make the L2 sensitive to outliers, the penalty term endeavors to rectify it by penalizing the weights.

Ridge regression performs better when all the input features influence the output with weights roughly equal in size. Besides, Ridge regression can also learn complex data patterns.

What is R Square?

R Square is a statistical measure that shows the closeness of the data points to the fitted regression line. It calculates the percentage of the predicted variable variation calculated by a linear model.

The value of R-Square lies between 0% and 100%, where 0 means the model can not explain the variation of the predicted values around its mean. Besides, 100% indicates that the model can explain the whole variability of the output data around its mean.

In short, the higher the R-Square value, the better the model fits the data.

Adjusted R-Squared

The R-square measure has some drawbacks that we will address here too.

The problem is if we add junk independent variables or significant independent variables, or impactful independent variables to our model, the R-Squared value will always increase. It will never decrease with a newly independent variable addition, whether it could be an impactful, non-impactful, or insignificant variable. Hence we need another way to measure equivalent RSquare, which penalizes our model with any junk independent variable.

So, we calculate the Adjusted R-Square with a better adjustment in the formula of generic R-square.

What is Mean Square Error?

Mean square error tells us the closeness of the regression line to a set of data points. It calculates the distances from data points to the regression line and squares those distances. These distances are the errors of the model for predicted and actual values.

The line equation is given as y = MX+C

M is the slope, and C is the intercept coefficient. The objective is to find the values for M and C to best fit the data and minimize the error.

Why Support Vector Regression? Difference between SVR and a simple regression model?

The objective of the simple regression model is to minimize the error rate while SVR tries to fit the error into a certain threshold.

Main Concepts:

Boundary

Kernel

Support Vector

Hyper-plane

The best fit line is the line that has a maximum number of points on it. The SVR attempts to calculate a decision boundary at the distance of ‘e’ from the base hyper-plane such that data points are nearest to that hyper-plane and support vectors are within that boundary line.

Conclusion

The ordinary least squares technique estimates the unknown coefficients and relies on minimizing the residue.

L1 and L2 Regularization penalizes the loss function with absolute value and square of the value of the coefficient, respectively.

The R-square value indicates the variation of response around its mean.

R-square has some drawbacks, and to overcome these drawbacks, we use adjusted R-Square.

Mean square error calculates the distance between points on the regression line to the data points.

SVR fits the error within a certain threshold instead of minimizing it.

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Mastering Exploratory Data Analysis(Eda) For Data Science Enthusiasts

This article was published as a part of the Data Science Blogathon

Overview

Step by Step approach to Perform EDA

Resources Like Blogs, MOOCS for getting familiar with EDA

Getting familiar with various Data Visualization techniques, charts, plots

Demonstration of some steps with Python Code Snippet

What is that one thing that differentiates one data science professional, from the other?

Not Machine Learning, Not Deep Learning, Not SQL, It’s Exploratory Data Analysis (EDA). How good one is with the identification of hidden patterns/trends of the data and how valuable the extracted insights are, is what differentiates Data Professionals.

1. What Is Exploratory Data Analysis

EDA assists Data science professionals in various ways:-

3 Getting a better understanding of the problem statement

[ Note: the dataset in this blog is being opted as iris dataset]

2. Checking Introductory Details About Data

The first and foremost step of any data analysis, after loading the data file, should be about checking few introductory details like, no. Of columns, no. of rows, types of features( categorical or Numerical), data types of column entries.

Python Code Snippet

Python Code:



data.head()              For displaying first five rows

data.tail()   For Displaying last Five Rows

3. Statistical Insight

This step should be performed for getting details about various statistical data like Mean, Standard Deviation, Median, Max Value, Min Value

Python Code Snippet

data.describe() 

  4. Data cleaning

This is the most important step in EDA involving removing duplicate rows/columns, filling the void entries with values like mean/median of the data, dropping various values, removing null entries

Checking Null entries

data.IsNull().sum   gives the number of missing values for each variable

Removing Null Entries

data.dropna(axis=0,inplace=True)     If null entries are there

Filling values in place of Null Entries(If Numerical feature)

Values can either be mean, median or any integer

Python Code Snippet

Checking Duplicates

data.duplicated().sum()  returning total number of duplicates entries

Removing Duplicates

data.drop_duplicates(inplace=True)

5. Data Visualization

Data visualization is the method of converting raw data into a visual form, such as a map or graph, to make data easier for us to understand and extract useful insights.

The main goal of data visualization is to put large datasets into a visual representation. It is one of the important steps and simple steps when it comes to data science

You Can refer to the blog below for getting more details about Data Visualization

Various Types of Visualization analysis is: a. Uni Variate analysis:

This shows every observation/distribution in data on a single data variable. It can be shown with the help of various plots like Scatter Plot, Line plot,  Histogram(summary)plot, box plots, violin plot, etc.

b. Bi-Variate analysis: c. Multi-Variate analysis:

Scatterplots, Histograms, box plots, violin plots can be used for Multivariate Analysis

Various Plots

Below are some of the plots that can be deployed for Univariate, Bivariate, Multivariate analysis

a. Scatter Plot Python Code Snippet

sns.scatterplot(data[‘sepal_length’],data[‘sepal_width’],hue =data[‘species’],s=50)

For multivariate analysis Python Code Snippet

sns.pairplot(data,hue=”species”,height=4)

b. Box Plot Python Code Snippet

plt.show()

c. Violin Plot

More informative, than box plot, and shows full distribution of data

Python Code Snippet

plt.show()

d. Histograms

It can be used for visualizing the Probability density function(PDF)

Python Code Snippet

.add_legend();

Email: [email protected]

You can refer to the blog being, mentioned below for getting familiar with Exploratory Data Analysis

Exploratory Data Analysis: Iris Dataset

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

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Ten Highest Paying Companies For Data Scientists In 2023

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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