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

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 2021, 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 2021.

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Top companies paying high salaries to data scientists

Oracle

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).

Pinterest

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.

Lyft

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.

Uber

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.

Walmart

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.

Nvidia

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.

Airbnb

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.

Netflix

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.

Dropbox

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.

Genentech

Data 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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Data scientists and RPA developers should collaborate to make a perfect team.

If  RPA developers work with data scientists, it will facilitate more creative solutions to complex business problems; than working separately.

Robotic process automation (RPA) is a cost-effective way of automating basic tasks as humans do, with the help of various hardware and software systems that can perform on different applications. RPA also focuses on the manual processing of data to gather more information for the company. Applying data analysis to this RPA-generated data can help the businesses gain a deeper understanding of the improvement opportunities, different business structures, and models, and help meet customer demands faster.

RPA and data science have always shared a mutually beneficial relationship. RPA tools integrated on the insights drawn from the data analysis, and the predictive models of data science were programmed to enhance the capability of these tools.

The further advancement of robotic process automation into the realm of data science will prove a remarkable transformation for business enterprises since they will gather more data in a cost-effective and non-invasive manner.

The skills RPA developers and data scientists possess are different but they complement each other. To understand why they should collaborate, let us look at the roles and responsibilities of data scientists and RPA developers.

Role of an RPA developer

The primary responsibility of an RPA developer is designing, innovating, and implementing new RPA systems. Other responsibilities include:

• Enabling high-quality automation using quality assurance (QA) processes and preventing potential complexities.
• Design business processes for automation.
• Develop process documentation to refine business processes by highlighting mistakes and successes simultaneously.
• Provide instructions and guidance for process designing.

Role of a Data Scientist

A data scientist analyzes and handles vast amounts of information to find patterns, customer behavior, trends, and potential risks in the market. Other responsibilities are:

• To implement data science techniques like machine learning, artificial intelligence, and statistical models to gain data for the company.
• Understand and select correct potential models and algorithms for different business tasks.
• Cooperate with engineering and product development teams to produce solutions and strategies for complex business problems.
• Develop predictive models and machine learning algorithms.

How the two teams complement each other?

The skill-set that a data scientist possesses differs from an RPA developer. They have different temperaments since their workflow and timelines are very different. When the workflow divulges, so do the mindsets and it affects the communication between the two teams.

But RPA developers can generate more complex processes working with the data science team than working alone. Business organization leaders should understand the potential outcomes and encourage RPA developers to communicate with data scientists.

A forward-thinking business organization will not compromise between two valuable teams, instead align them. RPA’s automation of data science allows the generation of models and selecting the most suitable model for unique business tasks. On the other side, these features enable data scientists to invest more time in other important tasks and develop creative models to provide analytical solutions for critical business problems. Bottom line, combining these two teams will not only enhance productivity but also amplify business growth.

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These top 100 companies have opened their door for talented data scientists

Data is the new oil for modern businesses,’ a familiar statement that is making many changes in the technology radar. Organizations of all sizes and types from diverse industries produce large volumes of data. The increasing amount of data often comes in unstructured and raw forms. But fortunately, they hold the key to make enormous growth in companies. Getting the most out of data requires expertise in advanced data analytics tools and techniques. Henceforth, data science comes as a handy solution for companies dealing with data management crises. Data science plays a hectic role in the business as it deals with the exploration, evaluation, modelling and generation of meaningful insights from large datasets.

Data science is becoming mission-critical to many organizations. Therefore, the demand for data scientists is significantly spiking. Companies across domains need data science professionals at various means. Besides data scientists, there are various roles associated with the interdisciplinary field namely Data Engineers, Machine Learning Engineers, Data Architects, Big Data Engineers, etc.

Data science has become a sought-after career opportunity in India. The opening for data science professionals in the country is getting more void as technological adoption increases. Remarkably, companies are also willing to pay high-salaries for talented individuals. Big companies across diverse sectors, including technology, financial services, manufacturing, automotive, etc always welcome data scientists with open arms to drive innovation. The hunt for data scientists in India remains an endless tale. Henceforth, to make job seekers and employers work easy, Analytics Insight has listed the top 100 companies that are hiring data scientists in India.

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What Is Data Science?

The terminology came into the picture when the amount of data had started expanding in the starting years of the 21st century. As the data increased, there was a newly emerged need to select only the data that is required for a specific task. The primary function of data science is to extract knowledge and insights from all kinds of data. While data mining is a task that involves finding patterns and relations in large data sets, data science is a broader concept of finding, analyzing, and providing insights as an outcome.

In short, data science is the parent category of computational studies, dealing with machine learning, and big data.

Data science is closely related to Statistics. But as opposed to statistics, it goes way beyond the concepts of mathematics. Statistics is the collection, interpretation of quantitative data where there is accountability for assumptions ( like any other pure science field). Data science is an applied branch of statistics dealing with huge databases which require a background in computer science. And, because they are dealing with such an incomprehensible amount of data, there is no need to consider assumptions. In-depth knowledge of mathematics, programming languages, ML, graphic designing, and the domain of the business is essential to become a successful data scientist.

How Does It Work?

Several practical applications provide personalized solutions for business problems. The goals and working of data science depend on the requirements of a business. The companies expect prediction from the extracted data; to predict or estimate a value based on the inputs. Via prediction graphs and forecasting, companies can retrieve actionable insights. There’s also a need for classifying the data, especially to recognize whether or not the given data is spam. Classification helps in work reduction in further cases. A similar algorithm is to detect patterns and group them so that the searching process becomes more convenient.

Commonly Used Techniques In The Market

Data Science is a vast field; it is very difficult to name uses of all the types and algorithms used by data scientists today. Those techniques are generally categorized according to their functions as follows:

Classification – The act of putting data into classes on both structured and unstructured data (unstructured data is not easy to process, at times distorted, and requires more storage).

Further in this category, there are 7 commonly followed algorithms arranged in ascending order of efficiency. Each one has its pros and cons, so you have to use it according to your need.

Logistic Regression is based on binary probability, most suitable for a larger sample. The bigger the size of the data, the better it functions. Even though it is a type of regression, it is used as a classifier.

The Naïve Bayes algorithm works best on a small amount of data and relatively easy work such as document classification and spam filtering. Many don’t use it for bigger data because the algorithm turns out to be a bad estimator.

Stochastic Gradient Descent is the algorithm that keeps updating itself after every change or addition for minimal error, in simple words. But a huge problem is that the gradient changes drastically even with a small input.

K-Nearest Neighbours is typically common to deal with large data and acts as the first step before further acting on the unstructured data. It does not generate a separate model for classification, just shows the data nearest to the K. The main work here lies in determining the K so that you get the best graph of the data.

The Decision Tree provides simple visualized data but can be very unstable as the whole tree can change with a small variation. After giving attributes and classes, it provides a sequence of rules for classifying the data.

Random forest is the most used technique for classification. It is a step ahead of the decision tree, by applying the concept of the latter to various subsets within the data. Owing to its complicated algorithm, the real-time analysis gets slower and is difficult to implement.

Support Vector Machine(SVM) is the representation of training data in space, separated with as much space as possible. It’s very effective in high dimensional spaces, and very memory efficient. But for the direct probability estimations, companies have to use an expensive five-fold cross-validation.

Feature Selection – Finding the best set of features to build a model

Filtering defines the properties of a feature via univariate statistics, which proves to be cheaper in high-dimensional data. Chi-square test, fisher score, and correlation coefficient are some of the algorithms of this technique.

Wrapper methods search all the space for all possible subsets of features against the criterion you introduce. It is more effective than filtering but costs a lot more

Embedding maintains a cost-effective computation by using a mix of filtering and wrapping. It identifies the features that contribute the most to a dataset.

The hybrid method uses any of the above alternatively in an algorithm. This assures minimum cost and the least number of errors possible.

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Emerging technologies like AI, data science and machine learning are all about working with intelligent models that need good algorithms to run. For instance, logistic regression or support vector machines.To understand these algorithms and how they work, one must be adept at programming languages.

Here, we discuss 11 crucial programming languages for data scientists.

1| C/C++

C/C++ are usually the first languages one learns when entering the world of programming. These languages give learners insights into the basics of programming and how it works. In machine learning and data science, libraries and frameworks are essential to tackle complex computational tasks. Popular languages like C and C++ have a number of interesting libraries that makes it easy and accessible for data scientists to work on complex computational projects. 

2| Julia

Introduced in 2015, Julia is one of the popular languages ideal for data science, scientific computing, parallel computing, data mining, machine learning, among others. This language uses multiple dispatches as a paradigm and can be used as a general-purpose programming language where you can easily code and write software in the application domains. 

3| Java

Java is one of the oldest languages used for various enterprise development purposes. As one of the oldest languages, it comes with a great number of libraries and tools for ML and data science. Java has a number of libraries and tools — the popular ones being Weka, Java-ML, Deeplearning4j — which are leveraged to solve most of the cutting edge machine learning problems. Also, Java is 25 times faster than Python.

4| JavaScript

JavaScript is a lightweight and interpreted programming language used to create web sites and applications. Using JavaScript in data science and machine learning has several advantages. For instance, the language provides maximum security compared to popular languages like Python. It is a cross-platform programming language to quickly develop and deploy applications in any operating system.

5| Lisp

Programming language like Common Lisp helps in creating flexible computational models. Programs that analyse the sequence data, graph knowledge, and tabular data can be written easily, and can be made to work together naturally in Lisp. The language allows the computer program to examine, introspect, as well as modify its own structure and behavior at runtime, making it ideal for artificial intelligence and machine learning applications. This language is suitable for bioinformatics and computational biology research.

6| MATLAB

Developed by MathWorks, MATLAB is a multi-paradigm programming language and numeric computing environment for complex computations. As data science is all about large swathes of data and numbers, MATLAB is ideal to gain insights from the data and visualise them. MATLAB code can be integrated with other languages, enabling developers to deploy algorithms as well as applications within web, enterprise, and production systems.

7| Python

Python is a general-purpose coding language. The main reason why this language is so popular among the developers is its plethora of libraries and frameworks, which help in performing complex computational tasks. The language has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++.

8| R

R is a popular statistical language that has gained much traction over the last few years when it comes to processes of data analytics and visualisations. With better data visualisation techniques, this programming language offers an essential role in statistical methods. The language can be used for effective data analysis and gain meaningful insights from it.

9| SQL

SQL or Structured Query Language has become one of the go-to languages for the aspiring data scientists. Most organisations use this language for analysing the tons of raw and unstructured data. The language is used for both data management and data analysis. Due to the speed advantages of SQL, recruiters expect developers to be proficient in it.

10| Scala

Scala or SCAlable LAnguage is a Java-like programming language designed to express common programming patterns in a concise, elegant, and type-safe way. The language is excellent for large-scale projects. It provides a lightweight syntax for defining anonymous functions and supports higher-order functions as well as allows functions to be nested apart from supporting multiple parameter lists. 

11| SAS

SAS, previously known as the Statistical Analysis System is a popular programming language that provides users with a host of product components, including asset performance analytics, analytics for IoT, decision making, and more. One of the best features of this language is allowing data in any format, ranging from SAS tables to Excel worksheets. SAS can also manage and manipulate data to obtain important information. 

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How to add value to businesses with the help of an AI Factory?

Like a physical factory creates physical products reliably at scale and speed, an artificial intelligence (AI) factory delivers AI solutions for businesses at scale and speed. An AI factory combines data, people, process, product, and platform to move beyond science experiments and deliver AI that drives business value. The AI factory builds on the principles of the AI Ladder, which describes the importance of creating solid information architecture for sustained AI success. It combines DataOps, ModelOps, and MLOps to stimulate AI innovations to market.

How an AI Factory Works

Quality data obtained from internal and external sources train ML algorithms to make predictions on specific tasks. In cases like diagnosis and treatment of diseases, these predictions can help human experts with their decisions. In content recommendation cases, ML algorithms can automate tasks with little or no human intervention.

The algorithm and data-driven model of the AI factory allows companies to test new hypotheses and make a change that improves their system. It could be new features added to an existing product or new products built on top of what the company already owns. In turn, these changes enable the company to obtain new data, improve AI algorithms, and again find new ways to increase performance, create new services and products, grow, and move across markets.

How AI Factories add Value to Businesses

In many ways, building a successful AI company is as much a product management challenge as an engineering one. Many successful companies have figured out building the right culture and processes on long-existing AI technology instead of fitting the latest developments in deep learning into an infrastructure that doesn’t work. Let’s see how an AI factory helps businesses to grow at scale.

The AI Factory begins with Centralised Governance

The idea is to pool and coordinate investment and steering efforts. Only a small number of companies’ highest-value projects will be examined by those sponsors most engaged in their success. The selection of these use cases must be extremely rigorous. No project specifically should see the light if it doesn’t respect the simple law of 10X (offer a 10:1 return on investment). The success and impact of each use case should be measurable as per a simple and understandable KPI. And the systematic improvement of this KPI the most crucial reason for the teams.

Lean AI

Lean AI is a methodology that reduces the uncertainty of efficiency and applicability of AI solutions. Models are never perfect and must be examined in real-world situations. The method contains a continuous improvement loop of short cycles which include the formulation of hypotheses, the identification of pertinent data, the construction and testing of one or more models, followed by deployment on a test perimeter, and collection of user feedback.

The cycle is repeated with the formulation of new hypotheses, new data, etc. This technique enables testing in real situations, then the improvement of cases not explored, until reaching a level of satisfaction considered acceptable by the organization to begin production.

Important Ethical Challenge

The recent example of Alexa and the unpleasant surprise of her listening have been noticed. Regulations will always lag behind technology. It is important that those enterprises that employ AI understand the ethical challenges of these solutions. Seven guiding ethical principles that were published by the Committee of Independent Experts mandated by the European Commission, includes AI at the service of humanity, trustworthiness, which respects private data, transparent, non-discriminatory, dedicated to the improvement of the common good, and finally, with a clearly defined human responsibility.

People lead to the Success of an AI Factory

An AI factory requires a team of people with a variety of skills, roles, and responsibilities to be successful, just like a physical factory. AI development traditionally involves cross-functional or full-stack technical teams. It is essential to consider the AI factory not just as a technical shop but as a market-driven business. In designing an AI factory, all jobs of AI and IT stakeholders, data scientists, data journalists, IT support, business analysts, marketing, and sales need to be done. Assigning people with clear ownership, roles, and responsibilities will add value to a business.

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Data scientists need to assemble predictive analytics workflow benefits to review on processes and algorithms. Data science techniques and tools are constantly evolving and only online resources can aid data scientists to keep up the pace. Even though when traditional models like reading books and journals give the understanding of fundamental concepts that benefit data scientists on a large-scale. The need for a community of experts to support the work of a data scientist has ignited a number of forums and groups where people seek help online. Henceforth, Analytics Insight is bringing a list of top 10 data science communities that professionals can take part in.

Kaggle

Kaggle is one of the world’s largest data science communities with powerful tools and resources. The community has around 3 million active members. Data scientists can find all the code and data they need for their data science work. They can use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time. Kaggle, a subsidiary of Google LLC, allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle started its community in 2010 by offering machine learning competitions. It later extended its services to a public data platform, a cloud-based workbench for data science, and artificial intelligence education.

IBM Data Community

IBM Data Science Community offers a constant stream of freshly updated content including featured blogs and forums for discussion and collaboration. The community provides access to the latest white papers, webcasts, presentations, and research uniquely for members, by members.

Reddit

Reddit is a one-stop platform for reading on almost any topics with 40 million searches everyday and over 430 million monthly active users, and 1.2 million communities or subreddits. These subreddits represent the topic-based groups or community on Reddit that comprises users who want to publish and discuss news or stories with that topic. Data science is one of the famous subreddits that data scientists look for. Some of the top subreddits that every data scientists should join are r/datascience, r/dataisbeautiful, r/MachineLearning, etc.

Open Data Science

Open Data Science is a global community that unites all researchers, engineers and developers around data science and related areas. It creates data science projects, and conducts events and educational courses. Data scientists in the community share their experience, which helps other sprouting data scientists to develop their skills.

Data Science Central

Data Science Central is the industry’s online resource for data practitioners. It provides services starting from Statistics to Analytics, Machine Learning, Artificial Intelligence (AI) and Data Science Central. The platform gives a community experience that includes a rich editorial platform, social interaction, forum-based support, plus the latest information on technology, tools, trends, and careers.

Data Community DC

Data Community DC is a non-profit organization committed to connecting and promoting the work of data professionals by fostering education, opportunity, and professional development through high-quality, community-driven events, resources, products and services. Data Community DC or DC2 has six meetup groups with over 5000 unique members, a board of 12 people, a blog, occasional workshops, and plans for bigger events in the future.

Stack Overflow

Stack Overflow is an open community for anyone that codes. This mainly helps data scientists who code, to get the answer for their toughest data science questions and share knowledge with co-workers. Slack Overflow serves 100 million people every month making it one of the 50 most popular websites in the world.

Dataquest

Dataquest has a data science community that allows access for all its students. The trained moderators and other learners help the data science students with their queries. The community is a go-to resource if data science students are stuck on a mission, encounter a platform issue, need advice or want feedback on the project.

Data Science Society

Data Science Society is an international digital community. The platform has been building a strong core of members digitally around a body of data science knowledge while having fun. So far, data science Society has organized multiple data science meetups and international datathons with attendees from more than 20 countries.

DrivenData

DrivenData works on projects at the intersection of data science and social impact, in areas like international development, health, education, research and conservation, and public services. The platform works on giving organizations more access to the capabilities of data science, engage more data scientists with social challenges where their skills can make a difference. The DrivenData community has leveraged the luxury for the team to work with 35 organizations across 50 projects.

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What makes Python a top choice in the Data Science community?

Python has become the most used programming language for data science practices. Developed by Guido van Rossum and launched in 1991, it is an interactive and object-oriented programming language similar to PERL or Ruby. Its inherent readability, simplicity, clean visual layout, less syntactic exceptions, greater string manipulation, ideal scripting, and rapid application, an apt fit for many platforms, make it so popular among data scientists. This programming language has a plethora of libraries (e.g., TensorFlow, Scipy, and Numpy); hence Python becomes easier to perform multiple additional tasks.

Python is an object-oriented, open-source, flexible, and easy to learn programming language.  According to a 2013 survey by industry analyst O’Reilly, 40% of data scientist respondents admitted using Python in their daily work. They join the many other programmers in all fields who have made Python one of the world’s top ten most popular programming languages ever since 2003. In fact, many surveys show it as the number one preferred language.

Why is it so Popular?

One of the main reasons why Python is widely used in the scientific and research communities is its ease of use and simple syntax that makes it easy to adapt for people without much programming or engineering background. It is also suitable for quick prototyping. Further, it allows the developer to run the code anywhere, like Windows, Mac OS X, UNIX, and Linux. And since it is a flexible programming language, it offers data scientists the facility to solve any given problem or carry projects concerning about developing machine learning models, web services, data mining, classification, etc., in less time frame than most of the programming languages. Python libraries Python Scrapy and BeautifulSoup can help to extract data from the internet, whereas Python Seaborn and Matplotlib help in data visualization or graphical representation. In data analytics helps with better insight, understanding patterns and correlates data from big datasets. Its libraries like Tensorflow, Keras, and Theano allow data scientists to develop deep learning models and Scikit-Learn helps to develop machine learning algorithms. It can also be leveraged in non-technical fields like business and advertising.

Besides, Python has a huge community base of engineers and data scientists like Python.org, Fullstackpython.com, realpython.com, etc., where Python developers can impart their issues and thoughts to the community at no cost. Also, Python has great compatibility with Hadoop, which is a renowned open-source big data platform.

Microsoft’s New Update

Microsoft has been a constant advocate of Python. It supports open-source Python in developer tools, including the Visual Studio integrated development environment (IDE), and hosts it in Azure Notebooks and uses it to build end-user experiences like the Azure command-line interface (CLI). Recently, Microsoft released a new update of its Visual Studio Code (VS Code) code editor for Windows, Windows on Arm, macOS, and Linux. In this, it launched a new version of the Python language extension for VS code editor that breaks out the Jupyter Notebooks functionality into a distinct VS Code extension. Jupyter is a free, open-source, interactive web tool, which researchers use to combine software code, computational output, explanatory text, and multimedia resources in a single document.  It draws its name from the programming languages Julia (Ju), Python (Py), and R. This means, it not only supports Python but also other popular data science languages like Julia and R.

Although Microsoft’s Python extension for VS Code has supported Jupyter Notebooks for a year now, the tech giant decided to break out Jupyter notebooks functionality to improve support for other data-science languages.

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Data scientists come in numerous flavors with various qualities that may suit various kinds of companies relying upon the sorts of issues or projects

Data Scientists have consistently been around – it is only that nobody realized that the work that these individuals are doing is called data science. Data Science as a field has emerged distinctly over the recent few years yet individuals have been working in the data science field as analysts, mathematicians,learning and actuarial scientists, business analytic practitioners, digital analytic consultants, quality analysts and spatial data scientists. Individuals working under these jobs are well furnished with data scientist skills and they are most demanded in the business.

Data science has quickly developed as a challenging, lucrative and highly rewarding career. While developed nations got comfortable with it part of the way through the last decade, data science has received consideration on a worldwide scale after the exponential development of e-commerce in developing economies, particularly India and China. In the previous decade, there has been a significant change in perspective in the way the world shops, books holidays, makes transactions and basically everything else.

Not all data scientists are made equal, particularly now that few “generations” of data scientists have entered and left organizations. Today, data scientists come in numerous flavors with various qualities that may suit various kinds of companies relying upon the sorts of issues or projects they are taking a shot at. Not to state that one sort is better or worse over another kind of data scientist — everything relies upon what a business is looking for.

Management Consultant

This classification traverses the junior business analyst and the ex McKinsey consultant. They share a common enthusiasm for Excel and their capacity to flaunt v-lookups and fancy formulas even to plan their house move. They are additionally the ones who have more passion for the business issue. For them, business comes first, data after.

They needed to learn Python or R by need, not on the grounds that they enjoyed programming. They actually try to abstain from coding as much as possible and their code is by and large as re-usable as a single-use napkin.

They have great instincts for the nuts and bolts of statistics however, they needed to learn concepts like p-worth or t-test the most difficult way possible. They are good at data science projects that bolster decision making, business-oriented processes, one-off projects.

Statisticians

This is data analysis in the conventional sense. The field of statistics has consistently been about number crunching. A solid statistical base qualifies you to extrapolate your enthusiasm for various data scientist areas. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the important skills possessed by statisticians which can be extrapolated to pick up expertise in explicit data scientist fields.

Statistics knowledge, when clubbed with domain knowledge, (for example, marketing, risk, actuarial science) is the ideal blend to land a statistician’s work profile. They can create statistical models from big data analysis, complete experimental design and apply theories of sampling, clustering and predictive modelling to information to decide future corporate activities.

Data Science for People

The consumers of the yield are leaders like chiefs, product managers, designers, or clinicians. They need to reach inferences from data so as to settle on decisions, for example, which content to license, which sales lead to follow, which medication is less inclined to cause a hypersensitive response, which site page design will prompt greater engagement or more buys, which email will yield higher income, or which explicit aspect of a product user experience is suboptimal and needs attention. These data scientists design, define, and implement metrics, run and interpret experiments, create dashboards, draw causal inferences, and generate recommendations from modeling and measurement.

Academia Data Scientist

They often have a PhD and originated from a research background. They examined hardcore math and statistics and they could talk hours about the philosophical contrasts between the Bayesian and frequentist methods.

They are typically alright at coding, as long as they don’t need to propel themselves a lot into the boundaries of data engineers. A test-driven programming approach may be a stretch for them. However, they are presumably acceptable at lower-level projects, for example, C++, which could come helpful for applications at large scale or deep learning.

What they in general need is business thinking. Building up a product is most likely the ultimate objective for them since they perceive that as the equivalent of publishing a paper in academia.

They are good at complex ML ventures at the front edge of development. They can push boundaries, go through a lot of research papers to pick and implement the best thoughts. A deep-tech organization would presumably require a small bunch of those profiles.

Actuarial Science

Actuarial Science has been around for quite a while. Banks and financial establishments depend a lot on actuarial science to anticipate the economic situations and decide the future salary, income, profits/losses from these mathematical algorithms.

It is possible to be an actuarial scientist without taking up any data science training. However, a data scientist will have an awesome handle over the mathematical and statistical algorithms that are required for actuarial science. A ton of organizations are currently speeding up the cycle by employing CFAs to accomplish the work of an actuarial researcher.

This is a specific position which requires data science experts to apply mathematical and statistical models to BFSI (Banking, Financial Services and Insurance) and other related professions. One must have a globally defined range of abilities and exhibit it by passing a progression of expert assessments before going after this position. Preliminary necessity is to know various interrelated mathematical subjects, for example, probability, statistics, finance, economics, financial engineering and computer programming.

Data science for Machines

Here the customers of the yield are computers which devour information through training data, models, and algorithms. Instances of the work these data scientists are: recommendation systems which suggest what shirt a client may like or what medication a doctor ought to consider prescribing depending on a designed optimization function. For example, enhancing for customer clicks or for minimizing readmission rates to the medical clinic. Contingent upon the engineering foundation of these data scientists, these work items are either conveyed legitimately to the production system, or if they are prototypes they are given off to software engineers to help implement, optimize and scale them.

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The UK government has announced hundreds of public sector staff will be trained in data science this year, as it launched a new “data strategy” for Britain.

Ministers and officials hope to transform the government’s use of data to improve public services and drive efficiency, as well as opening up more data to the public.

The government, which is now consulting on the strategy, wants to be known as the “world’s most digitally advanced.” A new chief data officer will be appointed to lead the initiative, with recruitment ongoing.

500 analysts will be trained up in data and data science by 2021. Up to ten people will also be hired as part of new “innovation fellowships” across departments, echoing a similar initiative in the US.

Other initiatives include exploring how to increase teaching of data skills among undergraduate students, and building T-levels — new courses which follow GCSEs and are equivalent to 3 A levels — that include digital skill qualifications.

A “Smart Data” initiative could create new ways for consumers and small- and medium-sized firms to use their own data to find better tariffs in areas like telecoms, energy and pensions. The move, which will require new legislation, may “open the doors to disruptors in every part of the marketplace,” according to the government.

Tech chiefs and experts welcomed the plans for a new data strategy.

Sue Daley, associate director of technology and innovation at tech firm lobby group TechUK, said unlocking data’s power had “never been more important.”

“A national data vision and strategy for realising the full economic and social value of data is vital to driving social good, innovation, competition, economic growth, productivity and job creation,” she said.

UK government IT projects have a chequered history, from an aborted £11bn ($14.2bn) NHS project to the delayed universal credit rollout and coronavirus test-and-trace app.

But digital secretary Oliver Dowden said the coronavirus had shown “just how much we can achieve when we can share high-quality data quickly, efficiently, and ethically.”

“Our new National Data Strategy will maintain the high watermark of data use set during the pandemic — freeing up businesses, government and organisations to innovate, experiment and drive a new era of growth.

“I am absolutely clear that data and data use are opportunities to be embraced, rather than a threat to be guarded against.”

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