Ultimate Guide To Data Science Careers
By Kamal Jacob
Before understanding the nitty-gritty of a Data Science Career in India, let us start from where the things began to emerge in order to have in-depth fundamental knowledge about the evolution of Data Science. Let us also see the developments which led to shape the Data Science as we see today, and how it will impact the future scenario, to get a fair idea about the career scope in Data Science today and beyond.
Before the emergence
Ever since time immemorial, data have been an essential part of the operations. Be it enterprises, institutions, organizations or households, data had been an inevident part that was omnipresent. In the historian times, data was captured and stored in the physical formats of scripts, and later as the time evolved, record files took its place.
For a really long time until the last few decades, every kind of data was stored in the physical forms. It was a challenge in itself to manage, maintain and access, especially when the data volumes were humongous. As a result, the chances of missing out on any data file was on a higher side as it grew older with time.
Data was stored in floppies and disks, which was transferred from one local system to another. Although, the storage and maintenance problem was resolved to some extent, yet the data was confined to a fixed location, making simultaneous access nearly impossible.
Then came the beginning of the internet revolution, which has brought in a new horizon of soft copies and digital storage of the data. This was a revolution in itself, a historic step into a new era of digitization and the first foundation stone of the data science landscape.
With time this issue was resolved with the help of database servers, which were used as backend storage devices, while the operations could be performed without any location constraints due to cloud practices.
This has been the point where modern IT practices have changed the way data was stored and accessed over a period of time. But, this only one aspect (Storage & Management) of the universe of Data Science. What still remained a point of curiosity is the methodologies that have been used to extract the information that provides knowledge and insights from the unstructured data that has been collected and stored at various scattered origins.
Throughout the whole journey of how data has been extracted, stored and managed, what remained at the center stage has been the Data itself. And this evolution defines the true power of data and the potential future it holds underneath its wings that is yet to be unleashed.
Now that we have gone through the journey of how the landscape of Data has evolved, let us now focus on:
1. What Data Science is,
2. Its areas of specialization,
3. Skills required for the areas of specialization,
4. Current scenario in India in terms of Career and growth prospects,
5. Location and Industry-wise analysis,
6. Remuneration aspect,
7. List of Companies and Startups using Data Science and absorbing Data Science Professionals,
8. Roadmap at each stage of experience,
9. Resources that navigate the journey of Data Science Career,
10. Taking the first step for a Career in Data Science
What is Data Science?
Data Science has made a lot of buzz in the IT industry in the past few years off late. With a lot of people aspiring to build a career in Data Science, while many still do not have a clear idea about it. Let us discover the basics of Data Science to understand the career prospects in it.
Data Science as a whole is a vast multidisciplinary field that deals with extracting and handling the knowledge and insights from the unstructured data. These insights could be used to study for research and analysis purposes. It is done by using various scientific methods involving different kinds of calculations and statistics.
Earlier these practices of extracting knowledgeable insights were done by conducting manual statistical calculations. It is due to the manual performing of the calculations, often the results were delayed and some of them even carried miscalculated inferences due to human errors.
The emerging advances in the IT field led to the advent of data computing. This later evolved as advanced computing with data involving Machine Learning and other computation algorithms. With a lot dependent on data, be it market research, product analytics or offering your customers an exclusive personalized experience, data science is behind everything.
Business Analytics has gone to a much different and advanced stage where everything from the customers’ behavior to finding out solutions for some of the most complex business problems that have remained unsolved over the past few decades can be done easily. All of this has made Data Science Analytics seem like a magic wand which every business house wants to own.
But the question which arises is, what does Data Science actually do that makes it one of the most sought after technology in today’s business landscape. Data Science is not less than a miracle which has opened new horizons of prospects for business with its accurate prediction of customers’ behavior pattern like some old Nostradamus prophecies.
Data Science collects all the data from the users, such as location, age group, preferences and what not, which it analysis in different aspects and complex dimensions in order to draw very accurate personalized conclusions.
The data is extracted by various means in the form of cookies by tracking down your activities and based on that data computing algorithms make various trends and patterns at different levels which are further used as insights.
This has led to a massive revolution in the industry with a lot of well-paying job prospects given the emerging massive demand of Data Scientist, on the contrary, the actual number of Data Science professionals is quite limited.
Let us take a step further to explore the specialization areas in Data Science so as to get a closer idea about the career prospects and how to build a successful Data Science Career in India.
Exploring Areas of Specialization in Data Science
While being Jack of all trades sounds cool, but at the same time, Master of None makes no sense. Being a domain expert with a specialization gives extra mileage to your career no matter whichever trade you choose.
Data Science being a wide spectrum of various practices one has to Specialize in one or the other field to become a trusted resource that can go miles ahead in a Data Science Career. While making your choice for specialization it is very essential to explore all the options that are available and understand which suits your interest and capabilities more, before you plan to take the plunge.
Here are the areas of specialization in Data Science as your options if you are looking forward to taking up specialization in Data Science:
1. Data Engineering and Data Warehousing
Data Engineering requires you to transform the data into formats that could be used for analysis. It is one of the core components of Business Intelligence, where Data Warehouses are maintained as central repositories of integrated data meant for analysis and reporting.
Data Warehousing is a multi-stage process that requires specialization where you are enabled to source, structure, segregate, store, perform a quality check on data and ensure better accessibility.
It leads to the designation of Data Engineer, Database Developer, or Database Analyst depending upon the nature of work and organizational hierarchy.
2. Data Mining and Statistical Analysis
Data Mining requires you to apply statistics in the form of Exploratory Data Analysis and Predictive Models in order to draw patterns, figure out trends and build up insights from the existing data sources.
It enables you to transform a business problem statement into a Data question and then create different predictive models to answer them and find the inference to draw conclusions.
It leads to the designation of Data Scientist, Business Analyst, or Statistician depending upon the nature of work and organizational hierarchy.
3. Business Intelligence and Strategy
BI requires you to improve the back-end data sources for enhanced accuracy and simplicity. You need to build tailored analytics solutions, manage dashboards, report stakeholders and identify opportunities.
That is how the BI maintains the best practices in reporting and analysis with data integrity, a test design, due analysis of it, proper validation and documentation.
BI leads to the designation of BI Engineer, BI Analyst, Data Strategist, or BI Developer depending upon the nature of work and organizational hierarchy.
4. Database Management and Architecture
Database Management requires you to design, deploy and maintain databases to support high volumes of complex data transactions for specific individual or group of services.
Database Management and Architecture lead to the designation of Database Analyst, Database Administrator, or Data Specialist depending upon the nature of work and organizational hierarchy.
5. Data Visualization and Presentation
Data Science is not just limited to the fact of extracting and analyzing data, but it also consists of an area where you are required to present the data insights in a visual form. Data Visualization and Presentation requires you to create BI solutions for the stakeholder teams and even the clients.
These BI solutions must be based on some specified Business requirements and use cases.
Data Visualization and Presentation leads to the designation of Data Viz Engineer, or Data Viz Developer depending upon the nature of work and organizational hierarchy.
6. Cloud and Distributed Computing
Cloud and Distributed computing require you to design and implement an infrastructure for enterprises that is essential for performing it. At the same time, you shall also be responsible for secure integration and analysis of system requirements.
Cloud and Distributed Computing could lead you to the designation of Cloud Architect, Cloud Engineer or Platform Engineer depending upon the nature of work and the hierarchy of the organization.
7. Machine Learning and Cognitive Computing Development
Machine Learning has been widely seen as synonymous with Data Science by most of the people. But in the actual case, Machine Learning forms up only a specific area of Data Science as a whole.
Seeing this from a layman’s perspective, Machine Learning is a way to develop algorithms that could help in Computing Data to form insights even in the most complex multiple dimensions.
Simply said, in Machine Learning you are required to ensure a model that gives the desired output which targets to solve a business problem by putting data extracts as the input.
So working on Machine Learning you need to build up the pipelines and the infrastructure which would support the actual algorithm or model. These algorithms or models need to be developed from scratch based on the business requirement specifications.
What is more interesting to observe here is you do not have any specified predetermined way of doing this. All you need is an alert, aware and diligent approach to build these models and their supporting infrastructure using Machine Learning in order to meet the business requirements.
But Machine Learning and Cognitive Computing Development in itself is a vast field which is further classified based on the area of infrastructure you handle. It is, therefore, very much required that you pay attention to these areas where Machine Learning is used extensively, to find the most suitable specialized area that your show your expertise in.
Machine Learning and Cognitive Computing Development could lead you to the designation of ML Engineer, AI Specialist or Cognitive Developer which largely depends upon the nature of work and the hierarchy of the organization.
8. Industry-specific roles for Marketing and Operation Research
While it might be a surprise to see that the requirement of the Data Science professionals is not just limited to the IT sector, but even other industries such as healthcare, finance, insurance, education, etc. too require them.
These are highly industry and department specific roles which require not only a sound technical background but also an apt domain knowledge to proceed with these industry-specific roles.
These seem to be tailor-made to suit the industry and further gets classified based on the department conducting research. Not that the methodology of Data Science gets changed, rather the approach and the business requirements change dramatically for each of these roles. It is because they are largely dependent on the business perspective than the technology, which itself builds upon the business requirements.
With these many options available to build up a successful career in Data Science, it largely depends on how you would choose a specific area to proceed with. Having said this, although all of these work under the umbrella of Data Science, the basic virtue of each of them varies significantly and thus makes it essential for you to make the right choice to land, where you would want to in your career.
With time these areas of specialization part on diverse ways that might look very close initially but in long-term sets you apart. So, making a careful choice is always important at the beginning stage itself before you take the plunge.
Prerequisite Skills for the Areas of Specialization in Data Science
In order to proceed with the area of specialization you choose to pursue in Data Science, it is important that you obtain certain skills to be able to master the chosen area. Here are the skills that you are required based on the areas of specialization:
1. Mathematics and Statistical Analysis
To understand what goes under the data processing, it is important that you have a strong hold on the key concepts of Mathematics, Statistical Analysis, and Probability. These would help you to understand the basics of algorithm and would help you develop and direct your approach towards building algorithms for the specific business requirements.
2. Database Warehousing Concepts
If you are looking for a specialization in Data Engineering and Warehousing, it is important that you understand the fundamentals of Data warehousing. This also means at the advanced level you should understand how the ETL (Extract, Transfer, and Load) works and how the data is segregated to obtain high-quality data. This also involves testing to ensure the quality and integration of data flow is maintained.
Big Data Analytics using Hadoop is an essential skill which you should master to have a successful career in Data Warehouse.
3. Business Intelligence and Reporting
If you are looking ahead to build a career in business intelligence and reporting, then you have to build a strong foundation of analytical aspects of the business strategies. This would require you to master the skills of reporting, business analytics, deriving inference using various online analytical processing.
4. Data Visualization Techniques
For building a potential career in Data Visualization and Presentation, it is quite essential for you to learn and master various techniques that could be used to present data in visual formats.
It requires you to build a strong approach towards data visualization and understand thoroughly the methodologies to read and present data in the form of graphs, plots and information graphics where inference can be easily made.
5. Data Mining Techniques
To have a strong hold over data mining and analysis, it is important that you attain the skills involving computer science and statistics as it is an interdisciplinary field. From the basic computing algorithms and learning programming languages such as Python for data analytics, you have a long way to go.
Machine Learning and Computing Methodologies make an important and foremost step in the field of data mining. It is very much essential to maintain a good hold and understanding of Machine Learning to enlighten the path of having a career in Data Mining.
6. Advanced Steps in Data Mining
With Deep Learning and Natural Language Processing, you can go an extra mile, once you have already attained a strong hold over the Machine Learning. Understanding the Deep learning concepts and the basics of NLP can do wonders and take you to the next level of Data Mining and Computing.
Deep Learning unfolds the layers of artificial neural networks which could let you to the horizon of Artificial Neural Networks (ANNs) and unravel the technologies behind speech recognition and other such phenomena that connects AI with the human senses, leading you to an advanced stage in the Data Science landscape.
7. Industry-specific skills
Apart from learning the high-end technological advances, you should also pay attention to the industry based domain learning if you are looking for industry-specific roles in Data Science.
These would help you analyze the Data Science approach through the business perspectives, letting you make custom strategies that are specified for the given industry.
Be it healthcare, HRanalytics, finance, insurance or education, getting hold of industry-specific application based skills can give you an edge over others when it comes to industry-specific roles.
With these skills, you can take a leap ahead in the way to build a career in Data Science by mastering them. This helps you to stand ahead tall and strong among the competition opening the doors to a successful career.
By far we have thoroughly discussed the areas of specialization and the skills one must master to make a potentially strong career in Data Science. Let us take a step further to look at the other side of the image i.e. the career prospects in terms of the current scenario and the future scope for growth in the field of Data Science.
Current Scenario and Future Growth Prospects
As Economic Times study says “India has seen more than 400% rise in demand for data science professionals across varied industry sectors at a time when the supply of such talent witnesses a slow growth, said Talent Supply Index (TSI) by Belong.”
Belong, which is a predictive outbound hiring startup, said in its 2018 edition of the Talent Supply Index that “Demand for data scientists has shot up by 417% in the last one year.” This demand is in stark contrast with the supply of such professionals as supply grew 19%.
Going by another interesting report which states that the demand and supply factors in the analytics job market have been inversely proportional, which means there have been more demands in the market than available talent currently (close to 2x). Despite being a 12% contributor to data science jobs in the world, India still fails in filling up the vacancies for data scientists in companies around the country.
As BusinessWorld study suggests there are over a million registered companies in the country, out of which over 75% of them will invest or already have invested in data science and machine learning. Besides, the Banking and Financial Services industry (BFSI) create the most job opportunities for aspirants, numbering close to 44%.
A study by Quartz India suggests “India has the most number of Data Analytics jobs second to the US which tops the list.” The number of jobs in Data Analytics that have been advertised has been increasing with the time clearly depicting the increase in the demand of professionals in Data Analytics.
Source: Edvancer Eduventures
Going by these multiple studies, there is a massive demand for Data Science professionals in India, which is yet to be fulfilled. This clearly indicates the fact, that in the coming years as the technology in the Data Science sector is still emerging, the career opportunities would likely increase.
One has to keep this thing in mind and start up-skilling themselves to seize this golden opportunity to build a successful career in India, without wasting much time. This would help you avail the benefits of first come first serve and give you an edge over the competition with the advantage of time on your side.
To get a further in-depth insight into the Data Science career prospects, let us analyze this picture more closely on a micro level by determining the scenario based on the various industries and location.
Location and Industry wise break-up of the Data Science career prospects
Going by the Location wise Data Science career prospects in India, a 2018 study by Analytics India Magazine suggests that Bengaluru tops the list by offering a whopping 73% of the total job opportunities in Data Science that are offered in India. It is followed by Mumbai and Pune with 14% and 5% respectively. While 3% of the total Data Science job opportunities are offered in Delhi NCR, and the rest makes up to 6%.
Source: Analytics India Magazine
The maximum Data Science career opportunities that you might find are based in Bengaluru which is also named as the Silicon Valley of India. While in Bengaluru, the chances of landing a job in Data Science becomes higher.
Let us look at the industry wise breakup of the Data Science job opportunities in India. Although, it is a common notion that IT sector hires most of the Data Science professionals, what comes as a surprise here is the fact that IT sector is closely followed by the E-Commerce and Banking sector that comes a close second.
As the 2018 study by Analytics India Magazine suggests, IT & Services sector hires 26% of the Data Scientists in India, followed by Banking and E-commerce industry both hiring 24% each. While the healthcare and the retail industry hires 12% and 8% of the total Data Scientists in India respectively, 6% are hired by other industries.
Source: Analytics India Magazine
This clearly states Banking Finance, E-commerce, Healthcare, and Retail industries are the key players in the Data Science landscape apart from the traditional IT sector. With this study, it is very much clear that industry based job roles in Data Science have a massive demand and it is an added advantage to have a strong grip over the domain learning apart from the technical knowledge to stand ahead.
Remuneration Aspect of Data Science Career
Well a very important aspect that drives the direction of market: Remuneration factor is significant criteria for a bright career. As the demand for Data Science has remained on a higher side over the past few years, so has been the case of remuneration.
India Today’s report suggests “INR 11.5 Lakhs is the median annual salary in Data Science and Analytics with an increase in the number of jobs offering over INR 15 Lakhs per annum.”
23% Data Science and analytics jobs are offering over INR 15 lakhs per annum. This has seen a surge of 3% in 2018 since last year is 20%.
This clearly states Data Science as a promising career sector not just in terms of the job opportunities that are available but also in terms of the remuneration offered, that is higher than other and yet continues to increase.
Not that we have got a clear picture about the future scope and current scenario of the Data Science sector, let us go the extra mile to explore the key players who make it to the list of top recruiters for Data Science in India.
Top Data Science Recruiters in India
Jotting down the list of top data science recruiters in India, let us explore the scope of getting recruited by the top Data Science Recruiters in India.
1. Fractal Analytics
Commenced in 2000, Fractal Analytics has come a long way to become the top analytics service providers in India. Having several Fortune 500 companies across various industries among their clientele, Fractal Analytics is a great place to work for a Data Science aspirant.
The global E-commerce giant Amazon is one among the top analytics recruiters in the world. No wonder how they always come up with personalized choices of products every time you open it. Working on a variety of operations such as supply chain, inventory and sales forecasting, advertising optimization, Amazon is the place to work if you are looking for some challenges at work.
One of the oldest Financial advisory, Deloitte which has been working on tax and consulting globally is also a top Data Science recruiter. Deloitte works on some of the largest analytics projects that simplify complex data for their clients. If Finance is a sector which amazes you and you love to solve complex data analytics jargons, then Deloitte is the right place for you.
The social platform which has successfully brought the professionals on a common- place is also a top Data Science recruiter in India. Have you ever wondered how every time you get relevant connection suggestions? Data Science has backed this social media platform for professionals and has been doing it very successfully, connecting professionals across the globe. Working with LinkedIn would undoubtedly give you some of the major learning experiences.
Being one of the world’s largest solutions provider, MuSigma is an Analytics giant based in Chicago has its delivery hub in Bengaluru. If you dream to work on complex data analytics finding out solutions for businesses, then MuSigma is the place you should head to.
The Indian E-commerce giant also follows the suit of its biggest competitors in being among the top 10 Data Science recruiters in India. With its growing scale of business and subsequently the amount of data that follows, Flipkart looks for diligent Data Scientists who can use it to the optimum benefits for the business.
IBM is one of the oldest and biggest names in IT consulting services, that has been helping businesses grow since 1911. Offering its clients with the data analytics services and solutions, IBM has made up to the list of top Data Science recruiters in India.
Accenture, which is one of the key players in the IT consulting and services industry, understands and leverages the power of Data Science for the growth of its clients. That is why it makes up to the top Data Science recruiters in India.
The US-based IT services company, Citrix offers SaaS, networking and cloud computing services in India also works on various Data Science projects, which makes them hire Data Science professionals on a large scale.
Myntra alike its parent organization Flipkart is a firm believer of giving personalized experience using Data Science and Advanced Analytics. Having this approach, they land among the top Data Science recruiters in India.
With these many companies offering job opportunities in Data Science at scale, complies with the fact that Data Science appears to be the future of IT holding a promising future. Having explored all the sides of the Data Science career, let us take a step further to route the way to a successful career in Data Science.
Roadmap for a Successful career in Data Science
Building a successful career in Data Science is not an impossibly humongous task as it is often perceived. Given the fact, if you are determined to explore and learn at every step, you can surely excel in Data Science. But yes, it is a road less travelled, which makes it even more unique and challenging.
With this roadmap, leaping successfully on the career path of Data Science could become easier leading to success that might seem a bit distant initially.
a) Start with the basics of Mathematics, Statistics, and Probability to lay a strong foundation.
b) Understand the basics of Database, Data Warehousing, its architecture and integration to get a clear idea of the data flow and segregation.
c) Learn the basics of BI, Data Visualization and Presentation to get a fair understanding of how business strategies work around Data Science.
d) Take the next step by learning Big Data Hadoop for a strong grip on how the data works.
e) Learn Python development to understand the basics of Data Computing.
f) Machine Learning is the next step to master the Cognitive Computing Development of Data.
g) If you wish to go the extra mile to learn deep learning, R language, and the basics of NLP to take a further leap in Data Science.
h) For expertise in the industry-based specialization learn how Business Analytics go hand in hand with the domain learning. This would help you leverage the power of industry level specialization in business strategies.
Having said that learning and leap in career go hand in hand, it is essential to make sure that you include learning at every stage, no matter if you are a beginner or experienced working professional taking certifications.
Resources that navigate the journey of a Successful career in Data Science
Finding the right resources to walk ahead in the journey of a successful career can be a bit tricky but once you find them, the learning process becomes a lot easier. To locate the right resources, reliability factor remains the foremost concern and it becomes essential to ensure that the study material you are following should be accurate.
Apart from the learning factor even the certification matters, as it adds weight to the resume. Given the fact that the Analytics India Magazine 2018 survey says, a formal degree or certification is important for landing a Data Science job, one must not ignore the certification part during the learning.
Source: Analytics India Magazine
With Data Science courses from Manipal Prolearn, you can get the best of learning along with the certification which can be a dual benefit for you from the career perspective.
Taking the First Step ahead for a Successful Data Science Career
Data Science sector being on the boom has brought in massive job opportunities which are yet to be taken. With these well paying job and bright career growth prospects in future, Data Science is one of the most sought after career in India among IT professionals.
All it takes is learning and determination to open the door of professional success. With a plunge to build a potential career in Data Science, you can go a long way ahead given the evolution in technology that takes place.
On a closing note, hope this blog post has been helpful for you to get a detailed in-depth idea about Data Science Career in India from different perspectives. Share your thoughts in the comment section and we would be glad to read them. Looking up to building a career in Data Science? Explore our Data Science courses here to upskill in Data Science.