Top 10 tools a data scientist should use in 2021

The work of a data scientist centers around the process of extraction of meaningful data from unstructured information and analyzing that data for necessary interpretation. This requires a lot of useful tools. The following are the top 10 most necessary tools that a data scientist needs to know about in 2021.

Python

Python is the most widely used programming language for data science and machine learning and one of the most popular languages overall. The Python open-source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” as well as built-in data structures and dynamic typing and binding capabilities. The site also touts Python’s simple syntax, saying it’s easy to learn and its emphasis on readability reduces the cost of program maintenance. The multipurpose language can be used for a wide range of tasks, including data analysis, data visualization, AI, natural language processing, and robotic process automation. Developers can create web, mobile, and desktop applications in Python, too. In addition to object-oriented programming, it supports procedural, functional, and other types, plus extensions written in C or C++.

Jupyter Notebook

Jupyter Notebook is an open-source web application that enables interactive collaboration among data scientists, data engineers, mathematicians, researchers, and other users. It’s a computational notebook tool that can be used to create, edit and share code, as well as explanatory text, images, and other information. Jupyter users can add software code, computations, comments, data visualizations, and rich media representations of computation results to a single document, known as a notebook, which can then be shared with and revised by colleagues. As a result, notebooks “can serve as a complete computational record” of interactive sessions among the members of data science teams, according to Jupyter Notebook’s documentation. The notebook documents are JSON files that have version control capabilities. In addition, a Notebook Viewer service enables them to be rendered as static web pages for viewing by users who don’t have Jupyter installed on their systems.

Apache Spark

Apache Spark is an open-source data processing and analytics engine that can handle large amounts of data, upward of several petabytes, according to proponents. Spark’s ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open-source communities among big data technologies. Due to its speed, Spark is well suited for continuous intelligence applications powered by near-real-time processing of streaming data. However, as a general-purpose distributed processing engine, Spark is equally suited for extract, transform and load uses and other SQL batch jobs. Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

D3.js

Another open-source tool, D3.js is a JavaScript library for creating custom data visualizations in a web browser. Commonly known as D3, which stands for Data-Driven Documents, it uses web standards, such as HTML, Scalable Vector Graphics, and CSS, instead of its graphical vocabulary. D3’s developers describe it as a dynamic and flexible tool that requires a minimum amount of effort to generate visual representations of data. D3.js lets visualization designers bind data to documents via the Document Object Model and then use DOM manipulation methods to make data-driven transformations to the documents. First released in 2011, it can be used to design various types of data visualizations and supports features such as interaction, animation, annotation, and quantitative analysis. D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, many data scientists don’t have JavaScript skills. As a result, they may be more comfortable with a commercial visualization tool, like Tableau, leaving D3 to be used more by data visualization developers and specialists who are also members of data science teams.

TensorFlow

TensorFlow is an open-source machine learning platform developed by Google that’s particularly popular for implementing deep learning neural networks. The platform takes inputs in the form of tensors that are akin to NumPy multidimensional arrays and then uses a graph structure to flow the data through a list of computational operations specified by developers. It also offers an eager execution programming environment that runs operations individually without graphs, which provides more flexibility for research and debugging machine learning models. Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and now incorporates the Keras high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations can be built in C++.

Keras

Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It’s an open-source deep-learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with its 2.4.0 release in June 2020. As a high-level API, Keras was designed to drive easy and fast experimentation that requires less coding than other deep learning options. The goal is to accelerate the implementation of machine learning models, in particular, deep learning neural networks through a development process with “high iteration velocity,” as the Keras documentation puts it. The Keras framework includes a sequential interface for creating relatively simple linear stacks of layers with inputs and outputs, as well as a functional API for building more complex graphs of layers or writing deep learning models from scratch.

Xplenty

Xplenty, is data integration, ETL, and an ELT platform that can bring all the data sources together. It is a complete toolkit for building data pipelines. This elastic and scalable cloud platform can integrate, process, and prepare data for analytics on the cloud. It provides solutions for marketing, sales, customer support, and developers. Sales solution has the features to understand your customers, for data enrichment, centralizing metrics & sales tools, and for keeping your CRM organized. Its customer support solution will provide comprehensive insights, help you with better business decisions, customized support solutions, and features of automatic Upsell & Cross-Sell. Xplenty’s marketing solution will help you to build effective, comprehensive campaigns and strategies. Xplenty contains the features of data transparency, easy migrations, and connections to legacy systems.

IBM SPSS

IBM SPSS is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a statistical analysis, data visualization, and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities. SPSS Statistics covers every step of the analytics process, from planning to model deployment, and enables users to clarify relationships between variables, create clusters of data points, identify trends and make predictions, among other capabilities. It can access common structured data types and offers a combination of a menu-driven UI, its command syntax, and the ability to integrate R and Python extensions, plus features for automating procedures and import-export ties to SPSS Modeler. Created by SPSS Inc. in 1968, initially with the name Statistical Package for the Social Sciences, the statistical analysis software was acquired by IBM in 2009, along with the predictive modeling platform, which SPSS had previously bought. While the product family is officially called IBM SPSS, the software is still usually known simply as SPSS.

PyTorch

An open-source framework used to build and train deep learning models based on neural networks, PyTorch, is touted by its proponents for supporting fast and flexible experimentation and a seamless transition to production deployment. The Python library was designed to be easier to use than Torch, a precursor machine learning framework that’s based on the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators. First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs, and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice versa.

Knime

KNIME, for data scientists, will help them in blending tools and data types. It is an open-source platform. It will allow them to use the tools of their choice and expand them with additional capabilities. It is very useful for the repetitive and time-consuming aspects. Experiments and expands to Apache Spark and Big data. It can work with many data sources and different types of platforms.

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With the Telangana government declaring last year as the year of AI and numerous job opportunities in the IT sector, the scope for AI is growing.

Hyderabad: Digithon, the digital entity of Telangana Information Technology Association (TITA), is starting an internship programme in Artificial Intelligence (AI) from July 12. Those clearing the course will receive a certificate from University of Texas at Dallas (UTD), US.

With the Telangana government declaring last year as the year of AI and numerous job opportunities in the IT sector, the scope for AI is growing. TITA had earlier imparted training in AI to make the State’s youth job-ready. Over 50,000 applications were received for the course of which more than 2,000 individuals were imparted training in AI as part of the Telangana government’s ‘Year of the AI’ initiative.

About 96.7 per cent of individuals cleared the AI exam conducted by UTD, and of this 80 per cent of individuals landed jobs in the AI area. The TS government, in its official report on AI, recognised and appreciated the role of Digithon, the digital entity of Telangana Information Technology Association (TITA) in the application and imparting skills in AI. To continue the training programmes in 2021, TITA has come up with the internship programme.

TITA will roll out the ‘Artificial Intelligence & Machine Learning In-plant Training cum Internship Program’ from July 12 and the last date for enrolling in the course is July 10. Participants will be trained in AI projects such as facial recognition, chatbot, etc. The ensuing training session will take up projects based on machine learning (ML) and deep learning.

Participants will visit industry as part of the industrial tour and those clearing an exam post the eight-week AI programme will receive a certificate in AI & ML from the UTD.

TITA said, institutes offering programmes in AI usually charge a hefty fee ranging into lakhs of rupees. However, TITA is offering the same programme at a nominal charge of Rs 10,000 for the benefit of youth in the State. Individuals interested in taking up the AI programme can register themselves at bit.ly/digithon_academy.

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Data science courses are high in demand. A data scientist needs to interpret the data accurately because he/she is responsible to communicate the complexity of the topic to the target audience in easiest ways.

Data science courses are very high in demand in the recent times. At least 88% of the Data Scientists hold Master’s degree and 46% are PhDs. While there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.

To become a data scientist, you can opt for a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data.

According to a research by LinkedIn, the supply of workers with the correct skills isn’t sufficient, thus making data science skills among the most in-demand. Thankfully, there are plenty of online learning opportunities available today to help become a highly-skilled data scientist.

Below are a few of the most popular data science options:

IntelliPaat’s Data scientist Master’s program in collaboration with IBM: This training program offered in collaboration with IBM lets learners gain proficiency in Data Science with Python, Machine Learning, AI, Deep Learning, Big Data Hadoop, Spark, Tableau Desktop, etc with 24*7 support. This program comprises 10+ courses, 53+ industry-specific projects, and 232 Hours of online instructor-led training with a CAPSTONE project. Learners will get access to the IBM course and earn a certificate from IBM.

The best part of this course is that learners have lifetime access to attend multiple batches and keep themselves abreast with technology updates. This course also provides career assistance which includes resume preparation, mock interviews.

GreatLearning Data Scientist program in collaboration with Great Lakes: Designed for freshers and young professionals exploring rewarding careers in Data Science. With Python, SQL, Tableau, Data Science and Machine Learning tools & techniques and its applications. They teach through immersive lectures delivered by our expert faculty in a classroom. Great Lakes faculty have been ranked among India’s Top Data Science academicians.

UpGrad Data Scientist Masters program in collaboration with Liverpool John Moores University: Any specialization can be chosen regardless of background. The program caters to: Engineers, Marketing & Sales Professionals, Freshers, Data Professionals, Domain Experts, Software & IT Professionals. They teach Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, etc

Simplilearn’s Data Scientist Master’s Program in Collaboration with IBM: This course offered in joint collaboration introduces students to integrated blended learning, equipping them with expertise in both Artificial Intelligence and Data Science. This advanced course in Data Science will make students industry-ready for skills in Artificial Intelligence and Data Science and consequently render them job-ready. This Data Science certification training provides hands-on exposure to key technologies including R, Python, Machine Learning, Tableau, Hadoop, and Spark via live interaction with practitioners, practical labs, and industry projects.

 
Coursera Data Scientist Program in collaboration with Johns Hopkins University: In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

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No second thoughts to the fact that today, the world runs on data. Irrespective of whether you want to create something or come up with better decisions, the dependence on data is insanely high. Well, data doesn’t stop there. It continues to rise, way beyond one’s imagination. Simply put, this data is humungous – thanks to the technology rich world that we are living in. gone are the days when data in petabytes was addressed. Now is the era of zettabytes. Just about a decade ago, the data across the world was limited to a few zettabytes. But, today we’ve crossed a century already!

Data forms the backbone of every possible field that you can think of. Companies and industries rely on data to formulate their business strategies. Following this, they carry on with their day-to-day operations. Where to invest, how much to invest and why to invest – all of this revolves around data. It is only by virtue of data that companies are able to apply the right marketing strategies that makes it possible for them to align with what the customers expect. Doing so, not only increases their customer base but also helps them in creating value and making profits at the same time. Healthcare, finance, education, defence, you name it and you learn how each and every sector relies on data to draw meaningful insights. Since, it is impossible to think of a life without data and how critical it is to lead our lives, the scope that this field has is immense, without a doubt. The rising demand for data-related jobs is hence not surprising.

This is where ‘data scientist’ as a job comes into picture. When Harvard Business Review itself has termed the role of a data scientist as “the sexiest job of the 21st century”, is there any other validation required?

Data scientists are concerned with transforming the raw, unstructured data into a form that it is possible to draw meaningful insights from the same. The data obtained should be such that the company is in a position to take better decisions and take the business to new heights. But, what is observed across the globe is that there is a huge gap between what the organizations demand and what the candidates are capable of delivering. It is because of this that the number of vacancies in these jobs is huge.

All this throws light on how can one get skilled to be able to fit in the role of a data scientist. Yes, it does require some effort to be put in. But, all this would be worth it, for sure.

Here’s everything that could help you grab the sexiest job of the century

• Usually, graduation in the field of engineering, computer science, statistics are given importance to. But, on the bitter side, a degree alone might not be enough.

• You need to understand what sets a data scientist apart from the rest, what are those unique skills that are required.

• Companies are more interested in the skill-set that the candidates possess. A clarity on the skills desired and working on the same is the key here.

• Yes, coding skills are critical but what you need to understand here is that data scientists typically need to be adept at programming languages such as Java, Python, SQL, R, and SAS.

• Techniques such as decision trees, neural networks, clustering, etc. play a pivotal role in the day-to-day activities of a data scientist. Hence, a sound knowledge about them helps.

• How can one not talk about Big Data frameworks while talking about data scientists? A good knowledge about Big Data frameworks such as Hadoop, Spark, and Pig, to name a few would help you land a job faster.

• Data scientists should also be familiar with technologies like deep learning, machine learning, etc.

• Since, technology keeps improving daily, it is expected that data scientists are in line with what’s happening on the technological front.

Since, it is evidently seen that data is growing exponentially, the dependence of the companies on data will continue till eternity. Data science is meant to stay and this is why the requirement of the right people who’d manage this humungous data in the best possible manner is the need of the hour. All that needs to be done now is to stay updated and skilled in the relevant areas that’d pave the way for the sexiest job of the 21^st century.

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Yes, having a technical degree helps, but you can also pursue a rewarding career in data science with non-technical background

The global market revenues from data science activities are set to grow in leaps and bounds in the future. And hence, it is no wonder that the demand for data scientists in various industrial roles will rise in proportion to market growth. But the main question is how to get started for a career in data science?

While there are specialized technical courses that can be pursued if one has a technical background, things may not be the same for someone with a non-technical (non-engineering) background. At the same time, given the gap between existing skills and required skills, it will be sometime before a non-techie finds a perfect fit in the data science market. Nevertheless, interested individuals can still succeed professionally with or without a technical background.

But why work as a Data Scientist?

As data becomes an essential asset to the digital transformation pipeline, companies are seeking talent with data skills that can help derive actionable insight from the data for business growth.

Meanwhile, there is an acute shortage of people with data science skills. According to the Co-founder and CEO of Fractal Analytics, Srikanth Velamakanni, there are two types of talent deficits: Data Scientists – who can perform analytics and analytics consultants – who can understand and use data. He reiterates that the talent supply for these job titles, especially Data Scientist is extremely scarce, and the demand is enormous.

Besides, data science includes several sub-job roles each with lucrative salaries and highly rewarding careers.

A Slice from Daily Work of Data Scientists

A data scientist’s work involves predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information. By using a combination of programming, statistical skills, and machine learning algorithms, data scientists can excavate through large amounts of structured and unstructured data to identify patterns. A data scientist can explore and examine data from multiple disconnected sources, and carry data mining by using APIs or building ETL pipelines.

Data scientists also perform the cleaning process of the datasets for separating the data which is relevant to a particular problem statement for better accuracy of the results. They are also assigned to determine the most optimal models and algorithms for the problem according to the requirements of the data.

Basically, a career in data science can seem intimidating at first but it is not inaccessible. Thanks to resources like video courses, eBooks, Stack Overflow, GitHub, hackathons, meetups, etc., most of which are free and open, it is not difficult to get started at least. It all depends upon passion, dedicated hard work, and interest.

So What to do if you are from Non-Technical Background?

Start From Scratch!

Though one may not have had the exposure to working with data, one can begin with understanding how data is being leveraged by organizations and its industrial applications. Then one can curate a curriculum to prep oneself with required technical skills.

For instance to learn about programming languages, and other key concepts, one can register for courses. Online platforms like Udacity, KDnuggets, Dataquest, and more, already offer online courses in data science. One must also be acquainted with basic mathematical concepts like linear algebra, calculus, probability and statistics. This is important because while data science tools and tech will continue to change rapidly, the underlying math will not.

One can also enroll in certification programs for data science. Earning a certification can improve one’s skills and boost the chances of being a better data scientist candidate. Potential certifications include certified applications professional, Cloudera certified professional: data scientist, EMC: data science associate and SAS certified predictive modeler using SAS Enterprise Miner 7.

Real-Life Projects Help!

Gaining practice training and experience is the linchpin of securing a data science job in the top reputed companies. For this, one must focus on building a portfolio of projects that focus on solving real-world bottlenecks and inefficiencies.

Obviously, there will be many candidates eying for the same data scientist job position. So, going for more focused project learning is a sure way to stand out in a crowd than the academic route. These projects also highlight one’s ability to transfer theoretical skills into the creation of data models that have an impact on society and industry.

The internet already has vast troves of free datasets that can be used for various kinds of projects like criminal records, census reports, cause of death count, etc. These projects can either be online courses based, individual undertaking or mentor-led. One can also host the project work on GitHub to receive feedback from experts or write content around it on Medium or a personal blog.  Either can help in increasing visibility and boost one’s chances of being noticed by a recruiter.

Apart from projects one must participate in various hackathons and other coding competitions conducted by online sites like Kaggle. Additionally, one must also invest some time in attending data science events like  Strata Conference, KDD and join data science communities like Datatau.

Find a Mentor

Charting a course in data science can seem daunting and overwhelming, especially when starting a new career. However, finding a good mentor can mean a huge difference between trying to find a job, preparing for an interview and working one’s first day as a data scientist. A mentor not only guides on what set of actions one must take in one’s career but also helps in securing the candidate’s future via networking. They also offer insider industry tips after one has fetched a well-paying job in data science. They can act as a bridge where one can channelize ideas to top executives and industry leads and also receive feedback in return.

Therefore, finding good mentors can ensure long-term career advice when needed. And when the time comes, one can return the favor by being a good mentor to other new-comers!

To surmise, if you are planning to start a career in data science, do not be afraid to explore your abilities. Figure out which persona you fit in and which are your gaps and take necessary action to propel oneself ahead in a competitive talent pool. Develop requisite skills, deploy learning in real-life use cases, receive healthy feedback, never hesitate to ask for help and lastly, never stop learning!

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Data is the basis of innovation. It’s the main reason why businesses collect pertinent information from consumers, as they could translate to powerful business predictions and decisions. But at the rate companies across the globe is accumulating mounds of raw data, there’s never enough qualified professionals to manage the torrential downpour of information. It’s no surprise data scientists are incredibly in demand, as they have the capacity to transform data into quantifiable pieces of information that can then turn into fact-based decisions.

If you want to capitalize on this demand, there’s no better time than now to harness and sharpen your data analytics and visualization skills. The Dynamic Data Scientist Bundle Ft. Power BI, Python & MATLAB was specifically put together to help you on that front, and for a limited time, you can get it on sale for only $29.99.

Regardless of your experience with organizing, processing, and analyzing data, this 7-part e-learning bundle will impart with you the skills to help make accurate and powerful data-driven predictions for any organization. With instructional content led by experts, you’ll learn straight from top data science specialists things like coding a multitude of data structures, mathematical concepts used in machine learning, creating visualizations and altering them to accommodate various situations, and effective data gathering and visualization techniques to make sense of raw information.

In addition to all these, the bundle also offers courses on using the different tools that data scientists have in their arsenal. You’ll be guided through what Microsoft Power Business Intelligence (BI) is and how it can be leveraged to view and share insights into businesses. You’ll discover MATLAB to figure out how machine learning works and get familiarized with Python to make line and scatter plots, as well as create various types of visualizations.

Future-proof your career and make yourself indispensable by learning data science. The bundle usually retails for $1,393, but for a limited time, you can get it on sale for only $29.99.

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AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

How it Functions

Using AutoML, businesses can automate tedious and time-consuming manual work required by today’s data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Automating ML Workflow

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

• Data science is like any other business function that must be performed with due diligence and needs creative thinking and human skills to get the best results.
• Data science is like babysitting, and one has to take care of the ML models, data and other assets regularly.
• AutoML is still in infancy. Once it’s ready, living without data scientists could be possible, at least for the most part.
• When one gets the results and business insights, the individual would still need the data workers to interpret them and communicate them to business.

Future of AutoML

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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What Are The Top Skills You Need To Amass To Become A Data Scientist?

The interdisciplinary field of data science is growing with extraordinary relevance and so do data scientists. At the same time, the world is generating more data than ever before, supported by inexpensive and endless cloud computing resources available to process that data. Since data is the new currency, companies focus on extracting value from the data pool that will help them boost business and adapt to the changing technologies in the market. For this, they need to hire the right people with reliable data science skills. Data scientists are generally believed to have profound knowledge and expertise in fields like machine learning, statistics, mathematics, computing science, data visualization, and communication. Besides, it is justified to possess such technical skills as a data scientist is one of the highest paying jobs in the Tech community. Along with these, a data scientist must have the ability to solve business problems, be agile, carry effective business communication, be a good data storyteller, and a team player.

Male Dominance

A study was recently carried to observe how an individual becomes a qualified applicant for a data scientist position. The study, “1,001 data scientist LinkedIn profiles,” was held for the third consecutive year. This time, it was able to delineate the typical traits of data science professionals in 2020 and compared this data with the 2018 and 2019 figures. The study found that a data scientist’s collective image is viewed as a male (71%) who is bilingual and has been in the workforce for 8.5 years (3.5 years of being a data scientist). A data scientist works with Python and/or R and has a Master’s degree. This is because a majority of data scientists in the research are male. The proportion remained very stable — 70%-30% in 2018, 69%-31% in 2019, and 71%-29% in 2020 — and is likely a true representation of the workplace’s actual situation. It was also discovered that the median work experience of people who work as data scientists jumped from 4.5 years in 2018 to 8.5 years in 2020.

Python Preference

A data scientist’s job includes data mining using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. The study noted that the most popular coding language in the field is Python. The top 5 mainly used programming languages by data scientists for their projects, i.e., Python (73%), R (56%), SQL (51%), MATLAB (20%), and Java (16%). This is a major shift from the previous year’s observations. In 2018, Python and R had the same level of adoption, which was 53%. The following year, in 2019, Python came in the lead with 54% compared to 45% for R. Now Python has established itself as the industry’s coding language of choice, with a significant lead over R.

Educational Background

In terms of Academia, the large majority (95%) of current data scientists have a Bachelor’s degree or higher. Out of those, 53% hold a Master’s degree, and 26% – a Ph.D. The study concludes that a person must aim for a second-cycle academic degree; although, having a Bachelor’s can still serve as a pre-requisite as long as the person has the technical skills and preparation required.

Next insight in the educational background was, while 19 out of 20 data scientists have a university degree, 55% of the data scientists in the cohort come from one of three university backgrounds: Data Science and Analysis (21%), Computer Science (18%), and Statistics and Mathematics (16%). There are fewer representatives of Economics and Social Sciences (12%), Engineering (11%), and Natural Sciences (11%). All of these are technical courses that prepare graduates for the quantitative and analytical aspects of the job. But a Data Science, Computer Science, or Statistics and Mathematics degree offer the best chance for a data scientist career.

Previous Job Experiences

The study also examined data scientists’ previous job occupation 1 and 2 jobs ago. Two positions prior to their current role, the average data scientist in the data pool were either already a Data Scientist (29%), an Analyst (17%), or in Academia (12%). And before entering their current role, the figures are 52% for Data Scientists, 11% for Analysts, and 8% for Academia. Apart from that, having an internship has helped people find jobs in data science. As per the study, 11% of data scientists were interns two jobs ago, and 7% of them were interns immediately before becoming data scientists.

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Whenever we use our smartphone or any other similar gadget, we do something over data. Data is generated enormously with every passing second, just because the number of people using the internet is increasing, as well as the time they spend over the internet is also increasing exponentially.

The data that is generated is unstructured. It has to be analyzed and organized according to the company’s requirements. The data is generated from various resources and simple BI tools are unable to process that data. Here, Data Science comes into play. The need for more complex and advanced analytics algorithms and tools for processing, analyzing, and making meaningful conclusions and insights out of it, has made Data Science a crucial and inevitable requirement of every business.

The experts predicted that by 2020, there will be 40 zettabytes of data in existence, and this will make career opportunities in Data Science shoot up drastically. And the shortage of skilled professionals makes Data Science a hot career opportunity for the related candidates.

When we are living in the era of Big Data, you would wish to launch a career in the field of Data Science. This article will let you know how promising the future becomes if you take up a Data Science online course and become one of the hottest certification holders.

Data Science as a Career

Data Science abbreviates statistical preparation, programming skills, visualization techniques, and a good deal of business sense which refers to the ability to turn business matters into answers with current or upcoming data.

So, you need to have a blend of skills in mathematics, spotting or finding out trends, and computer engineering. The main role of a data scientist is to decipher large volumes of data and prepare a plan for further analysis to find out trends and other meaningful insights.

Since the field of data science is so vast that there is no perfect definition for it, so is the career in Data Science. You may have many options to choose from as a data scientist. Some roles that come under data science are:

●Data Analyst

●Data Architect

●Statistician

●Data Mining Engineer

●Business Intelligence Analyst

●Data Scientist

●Senior Data Scientist

●Analytics Manager

●Research Analyst

●Data Science Architect

The above list shows you that there are many career options in Data Science and every position requires you to have some specified skill-set. You can choose any of the job roles according to your area of interest and the level of knowledge.

To start a career in Data Science, you must possess hard skills like machine learning, analysis, statistics, Hadoop, etc. apart from these, depending on the job role you may need to excel in persuasive communications, critical thinking, and problem-solving.

In this article, we will discuss the job role of “Data Science Architect”.

Data Science Architect

Data Science Architect is considered as a new role in data that businesses should take into account. A Data Science architect is a job role that is a mix between a data scientist and a data engineer.

We have already read about data scientists. Now, a data engineer is a candidate who is involved in preparing data for operational or analytical uses. The task of data engineer may typically include building data pipelines to bring together information from various sources, integrating, cleansing, and structuring data for usage in individual analytics applications.

The Data Science Architect or DSA comes in between the two. The task of DSA is to deal with the design of data, analysis, and storage processes while taking into consideration cost and time trade-offs and requirements of the business.

A DSA starts with an analysis of a company’s requirements with the end goal of using data to generate values. With the goal in mind, DSA needs to design the architecture and the analytics pipelines along with considering appropriate time frames, and costs.

For the best structuring data strategy and the roles in an organization, Data Science Architect can provide great assistance.

Let us now see how to become a Data Science Architect.

How to Become a Data Science Architect?

The main task of a data architect is to create a blueprint of the data management system. That strong background in computer science. Let us look at the steps required to become one.

1.Pursue a degree in computer science, computer engineering, or related field.

A strong background of computers includes coverage of data management, big data developments, system analysis, and technology architecture.

2.Technical and business skills.

Technical skills required for a data architect include data modeling tools, application server software, Database Management system software, agile methodologies, user interface, and query software, UML, ETL Tools, Hadoop, and NoSQL databases, machine learning, data visualization, development environment software, etc.

Business skills for data architects include analytical problem-solving, expert management, effective written and verbal communication skills, and understanding the way your industry functions.

3.Go for Certification and training.

A certification always keeps you ahead of other similar candidates when it comes to job opportunities. There are many certifications to choose from to become a data architect.

Taking up an online training course for getting certified is the best choice you can go for. This is because becoming a data architect involves studying a lot of material and expertise in many technologies.

The training providers make it easy for you to sort the things and prepare the study material that is specifically designed according to your knowledge level.

Conclusion

Becoming a Data Architect in the world of Big Data can really be rewarding. The average annual salary of a Data Architect is $123,680 according to Glassdoor, and that of senior data architect is $132,312.

With such a high salary and shortage of skilled professionals, this position has become one of the favorite career options among IT professionals.

Taking up an online training course for getting certified has several benefits. It gives you the flexibility of learning at your own pace and learning hours according to your convenience. It gives you options to choose from modes of learning as well. You can go for online training, blended learning, or instructor-led training.

So, go ahead and get your registration done so that you can get certified.

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Before implementing a new model, a data scientist must be sure they understand their data set. One method data scientists use to better understand their data and understand what model would work best for it is exploratory data analysis.

Though there are many tools to employ in this process, the Python programming language offers some of the most used tools in exploratory data analysis. Since Python is one of the most popular programming languages used in data analysis and data mining, most data scientists already find it accessible. Using Python exploratory data analysis tools allows you to easily summarize and visualize your data to choose the best algorithms and modeling to derive the most useful information from your data.

What is exploratory data analysis? 

Exploratory data analysis focuses on what insights you can derive from the data. The process starts with defining the problem you need the data to solve. One of the first steps is to figure out what the business objective you need to address is. What is the specific end goal for your analysis?

Once you know the business objective, it’s possible to prepare your data and develop different models to achieve that goal. It also allows you to summarize the available data, find correlations and create visualizations to present your findings to stakeholders.

Exploratory data analysis is an important part of defending your model to business stakeholders and proving that it can produce the desired results.

Commonly used tools

There are plenty of tools that can facilitate the exploratory data analysis process, from tools built into programming languages such as Python and R to tools supported by vendors such as SAS and KNIME.

Python exploratory data analysis is common among data scientists as Python is one of the most-used programming languages in the field. Available programming libraries such as pandas and Matplotlib can easily summarize and create visualizations for your data. Additionally, there are libraries in the language that can facilitate hypothesis testing and identify correlations between factors.

Learning to use Python for exploratory data analysis

By understanding your data set with exploratory data analysis, it is possible to determine the effectiveness of your model and better determine the correct machine learning model to reach your defined business goals. Learning Python exploratory data analysis can help you easily pull the most meaningful insights from your data using a tool you’re already familiar with.

In their new book, Hands-On Exploratory Data Analysis with Python, published by Packt Publishing, authors Suresh Kumar Mukhiya and Usman Ahmed talked about useful techniques and Python tools for exploratory data analysis. The book includes links to sample code with step-by-step instruction on how to perform each process.

Here’s a preview of the book. Click here to read Chapter 1: Exploratory Data Analysis Fundamentals.

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