Explore the Best Industries for Data Science Jobs

Posted on April 11, 2025April 11, 2025 by Imarticus Learning

Most of the companies within every sector, nowadays, focus on moving into a data-driven approach in coming up with strategies to improve efficiency in operations and deliver better service to customers besides coming up with innovation.

And thus, knowing which is the biggest area of opportunities will make all the difference between being successful in traversing the road down the data scientist roadmap or failing with the planning of one’s career down data science.

According to research, In India, the data science market is expected to grow at a CAGR of over 33% from 2020 to 2026, driven by a growing emphasis on data-driven policies across sectors like finance, healthcare, and e-commerce. That’s an enormous push in the opportunities that professionals would want to establish their foothold in this domain.

If you are looking to penetrate this space, the appropriate data science course does turn out to be pretty crucial. That is, a good program will provide you with the right amount of technical knowledge, along with the right amount of practical experience and industry exposure to get you into data science jobs with a high growth rate.

Why Data Science Jobs Are in High Demand?

The Advent of Data-Driven Decision Making

A business activity today can leverage on big data, machine learning, as well as AI to drive efficiencies that better the user experience, enhance revenue, or both. All the same, organizations realize a need to process big datasets every day, and data science is turning out to be one of the most sought after data science jobs around.

With the emergence of smart technologies, automation, and predictive analytics, data-driven insights have now made the firm dependent on these to function ahead of the curve. Besides the advantages, a requirement is recognized in the analysis of big data, trend prediction, and decision-making through data.

Competitive Salaries

Firm-level data science career path growth with ample scope in this domain. The average salary for an Indian data scientist lies between INR 10-15 LPA and for experienced persons, it reaches the amount of INR 30+ LPA. For talented professionals, the market in the US or UK presents checks into the amount of $100,000-$150,000 a year.

Thus, one of the lucrative professions is due to the fact that data scientists willing to spend large amounts of money on companies from various domains fetch meaningful insights out of raw data.

Besides a good remuneration package, one of the reasons that people want data science career path. Experience and skill set perfectly aligned for a data analyst will eventually take that individual to become senior data scientists, machine learning engineers, and even chief data officers.

Top Industries Hiring Data Science Professionals

Finance and Banking

The finance and banking industry ranks at the very top among those employing data science dominantly. For everything-ranging from fraud detection to algorithmic trading, not to mention countless others- it has employed analytics in this industry. Here are the most often found use cases that are mostly for financial institutions.

Fraud Detection: It employs AI-enabled systems to check ongoing transactions constantly to spot activity considered suspicious real time.
Customer Risk Assessment: predictive analytics can be used by banks to check the credit history of a potential customer.
Investment Strategies: Algorithmic stock trading is applied data science in hedge funds and investment banks

Hire most in this industry are the following:

Banks: HDFC, ICICI, SBI, Citibank
Investment Firms: Goldman Sachs, J.P. Morgan, Morgan Stanley
Fintech Companies: Paytm, Razorpay, PhonePe

Healthcare and Pharmaceuticals

Healthcare Industry uses data science for:

Predictive Analytics: The AI-based models assist in identifying the patterns that can lead to patient deterioration and cause diseases.
Drug Discovery: Machine learning has expedited drug discovery and development within Pharma companies.
Medical Imaging Analysis: AI enables doctors to provide proper diagnosis for diseases.

Key Hired in this sector are:

Hospitals: Apollo, Fortis, AIIMS
Pharma Majors: Pfizer, Novartis, Johnson & Johnson
HealthTech Startups: Practo, 1mg, PharmEasy

E-commerce and Retail

Data science is extensively used in the following sectors of e-commerce and retail:

Customer Personalisation: Algorithms suggest products to the customer based on his activity.
Supply Chain Management: Organizations use AI algorithms for demand forecasting and according to that, design out their inventory.
Pricing Strategies: Price dynamic model, which changes the price due to competition and requirements of the customers.

Key players in this industry are:

E-commerce Companies: Amazon, Flipkart, Myntra
Retail Stores: Reliance Retail, Big Bazaar, D-Mart
Market places: Nykaa, Meesho

IT and Technology

Tech companies apply data science for
Cloud Computing: Handle massive data to load quicker
Cybersecurity: AI-based security systems against threats and cyber attacks
Software Development: Show the most appealing design with ease of usage to make it even more user-friendly.

Major recruiters in the field:

Tech Giants: Google, Microsoft, Apple, Facebook
IT Services Firms: Infosys, TCS, Wipro, Accenture
AI & Cloud Companies: NVIDIA, IBM, Oracle

Want to get started with Data Science?

Step 1 – Choose the Right Data Science Course

The right data science course will equip you with the best skills for high-paying jobs in data science. The Postgraduate Program in Data Science and Analytics by Imarticus Learning offers:

100% Job Guarantee with 10 guaranteed interviews
Live interactive sessions by expert faculty
Practice with 25+ projects and get hands-on experience
Career guidance which helps you acquire your dream job.

Step 2: Technical Skills

In-depth skill building in the field of data science
Programming: Python, R, SQL
Data Visualization- Tableau, Power BI
Machine Learning & AI- TensorFlow, Scikit-learn

Step 3: Practical Experience

Practical experience at job level on the projects based on industries will add more employability. Organisations require that candidate, who can make skills happy through practical use

Step 4 – Participation in Hackathons and Internship

A candidate gets to face actual experience in solving the problem through exposures to hacking and an internship. Definitely, such events as competitions on Kaggle enhance portfolios.

FAQs

Most sought after jobs of a data scientist?

Finances, Health, E-Commerce and in IT would see data scientists are being hired most.

Is a Fresher eligible to become a data scientist?

Fresher can most surely get data scientist job when he joins data science course, and few projects on hand for real times

What is the duration to become a data scientist?

With the right training, one can be ready for the job in 6 months to 1 year.

How much does a data scientist earn?

Average INR 10-15 LPA in India and up to INR 30+ LPA for experienced professionals.

Which firms hire data science professionals?

Data science professionals are being actively hired by finance, healthcare, retail, IT, telecom, and manufacturing companies.

What are the skills required for a data science job?

Base for Python, R, SQL, machine learning, data visualization, and AI frameworks

How can I build my portfolio in data science?

Do Kaggle competitions, hackathons, open-source projects, and even real-world case studies that best present the skills that you bring to the table.

Conclusions

There is always a high demand for data science jobs, which also means the possibility of a good, stable-paying career. The finance, healthcare, e-commerce, and IT industries are currently hiring full-time specialists. So, you have all the potential to succeed as a data scientist if you sign up for the best data science course and gain hands-on knowledge of how to keep abreast of the trends in the industry.

All set to start off your career in data science? Enrol with the postgraduate program in Data Science and Analytics by Imarticus Learning today and begin to tread along this high-paying data science career path.

Optimization In Data Science Using Multiprocessing and Multithreading!

Posted on May 31, 2021March 26, 2024 by Imarticus Learning

Every day there is a large chunk of data produced, transferred, stored, and processed. Data science programmers have to work on a huge amount of data sets.

This comes as a challenge for professionals in the data science career. To deal with this, these programmers need algorithm speed-enhancing techniques. There are various ways to increase the speed of the algorithm. Parallelization is one such technique that distributes the data across different CPUs to ease the burden and boost the speed.

Python optimizes this whole process through its two built-in libraries. These are known as Multiprocessing and Multithreading.

Multiprocessing – Multiprocessing, as the name suggests, is a system that has more than two processors. These CPUs help increase computational speed. Each of these CPUs is separate and works in parallel, meaning they do not share resources and memories.

Multithreading – The multithreading technique is made up of threads. These threads are multiple code segments of a single process. These threads run in sequence with context to the process. In multithreading, the memory is shared between the different CPU cores.

Key differences between Multiprocessing and Multithreading

Multiprocessing is about using multiple processors while multithreading is about using multiple code segments to solve the problem.
Multiprocessing increases the computational speed of the system while multithreading produces computing threads.
Multiprocessing is slow and specific to available resources while multithreading makes the uses the resources and time economically.
Multiprocessing makes the system reliable while multithreading runs thread parallelly.
Multiprocessing depends on the pickling objects to send to other processes, while multithreading does not use the pickling technique.

Advantages of Multiprocessing

It gets a large amount of work done in less time.
It uses the power of multiple CPU cores.
It helps remove GIL limitations.
Its code is pretty direct and clear.
It saves money compared to a single processor system.
It produces high-speed results while processing a huge volume of data.
It avoids synchronization when memory is not shared.

Advantages of Multithreading

It provides easy access to the memory state of a different context.
Its threads share the same address.
It has a low cost of communication.
It helps make responsive UIs.
It is faster than multiprocessing for task initiating and switching.
It takes less time to create another thread in the same process.
Its threads have low memory footprints and are lightweight.

Optimization in Data Science

Using the Python program with a traditional approach can consume a lot of time to solve a problem. Multiprocessing and multithreading techniques optimize the process by reducing the training time of big data sets. In a data science course, you can do a practical experiment with the normal approach as well as with the multiprocessing and multithreading approach.

The difference between these techniques can be calculated by running a simple task on Python. For instance, if a task takes 18.01 secs using the traditional approach in Python, the computational time reduces to 10.04 secs using the pool technique. The multithreading process can reduce the time taken to mere 0.013 secs. Both multiprocessing and multithreading have great computational speed.

The parallelism techniques have a lot of benefits as they address the problems efficiently within very little time. This makes them way more important than the usual traditional solutions. The trend of multiprocessing and multithreading is rising. And keeping in mind the advantages they come up with, it looks like they will continue to remain popular in the data science field for a long time.

Related Article:

https://imarticus.org/what-is-the-difference-between-data-science-and-data-analytics-blog/

How Data Science Training Will Ensure Business Continuity In The Post-Covid World?

Posted on April 28, 2021March 26, 2024 by Imarticus Learning

Lockdown restrictions have pushed us into recession along with a health crisis we have never seen before. Businesses are struggling to make ends meet, and many have had to take tough decisions like layoffs, frozen hiring, salary cuts, and more. The year has also shown us how vital digitalization is and why data science is the driving force in the post-COVID era.

One of the most prominent examples is remote work and how people have adapted to such a form of work. Businesses are accepting digital tools to optimize their functionality, and this is where data science comes in.

Digital tools not only help businesses measure ROI but also determines every small or big aspect like regulating spends, analyzing the long-term impact, and more. The change is making more companies include AI, VR, AR, and cybersecurity to transform their businesses.

Why is data science essential for business continuity?

Here are some of the reasons why adopting a data science course is crucial for current and aspiring businesses leaders:

Analyzing and forecasting

The pandemic gave us clarity of how uncertain times can be. Businesses can go through drastic changes due to such conditions unexpectedly.

With the help of data analytics, we can predict our future better. It helps us analyze risks and develop strategies to mitigate them.

Companies can use historical data from the current situation to estimate t

he trends of the future. Data science training can also give us cues of future obstacles and how to handle them efficiently.

Moreover, historical data can help plan different outcomes of businesses during an unfortunate phase.

Assessing resources for maximum utilization

While analyzing risks and mitigating them fast can prevent disrupting business processes, knowing the way to put resources to use during such times is also crucial.

Every company needs to imply analytic practices to get through critical times and understand the state of their business.

One example of this would be setting up an analytics team for finance. The process will help a business find how changes in the economy can or are affecting their business. Data assists in allocating resources and promotes effective decision-making.

Identify Opportunities

When you adopt data science, you can identify new opportunities for business continuity. The process of analyzing available data resources helps catch loopholes early and find new possibilities to overcome them.

Currently, most companies are aggregating COVID data and combining the same with employee data. It helps one understand better ways to support employees in this crisis. For example, updating a dashboard to check geographical data helps offices take preventive measures or recommend closedown. HR’s are using such data to determine if offices are safe to open or not.

Necessity of cybersecurity

As soon as you step into the data science career, you will know how critical cybersecurity is. It is the most vulnerable risk that any form of data can pose.

Much of this has been at stake since people have been working remotely. There is also an increase in cloud services, which need constant technical tracking, maintenance, and recovery.

Even when companies work remotely, they have to imply IT security to safeguard data exposure and threat. Data science and AI give us solutions to cyber threats and monitor network traffics more effectively across VPNs. It quickly detects points of breaches and infringement in real-time.

Helps in quick changes of services

E-commerce and retail industries use data science and AI to attract customers; both online and offline. Companies now leverage AI-powered solutions to get insights into changing consumer demands. Such data helps optimize the supply chain and minimize disruption.

There is a high usage of AI chatbots to get quick solutions to external factors. These chatbots communicate with customers, answer their questions, and record their responses. It is assumed that the usage of chatbots will multiply by 50 times compared to what it was before COVID.

Final thoughts

Business continuity is critical and crucial. More and more companies need to incorporate data science to make businesses effective and reliable. While it has been vital for a long time already, it will become non-negotiable in the post-COVID era. The above-listed reasons give you a brief insight into the bigger picture and why data science is the future of your business goals.

Data Science Job Opportunities Continue to Surge in 2022!

Posted on April 6, 2021April 25, 2024 by Imarticus Learning

Data science has revolutionized the functioning of almost all industries in the world today. The creation of data is the highest at the moment due to the widespread process of digitisation. Therefore data science tools and technological advancements are being deployed in order to push further productivity amidst all organizations.

With this, there is the provision of Big Data, Machine Learning, Data Analytics, Data Mining and Data Analysis thus creating large importance for this technological field.

All businesses and organizations require efficient and quick problem-solving methods. This is offered by data technology, having the ability to analyze and comprehend large sets of data in order to resolve a variety of problems in a fast-paced and accurate manner. This is a much more sought after a method as compared to the completely engineered solution.

The development of proficient machine language algorithms and a change of direction from analytics that were descriptive has resulted in driving progress. Predictive analytics and maintenance have slowly been gaining popularity amongst industries and this popularity only seems to be growing.

Data Science Jobs The demands for various data science services have been seeing a large surge all over the world as researchers for the market predict its magnification in the near future. Due to this increased demand, the path for various other talents and job aspirants is clearing. This would allow them to try their hand and work hard while in this genre of work. The vast number of technologies in relation to data are creating large opportunities for up and coming data professionals to seize.

With an estimated increase of over 1 lakh new job openings in the present year of 2020, which is a little more than a 60% increase from the previous year (2020), aspirants have a large number of openings to prove themselves with a data science career. Almost 70% out of these job opportunities are for budding professionals with experience less than or up to five years.

In a bid to remain in the fast-paced competition of today’s market and maintain relevancy, organizations, businesses and various other companies are taking up newly emerging technology. Due to a large amount of data that is being created, data technology and science is the answer to mining insights that are actionable for businesses.

There is thus a very large scope in this field for data science professionals set in the present year, 2022. This year has been the best year for Data science and furthering its opportunities.

Industries of energy, pharmacology, healthcare, media, retail, e-commerce, etc. are creating a large number of job opportunities in the field with average potential salaries going from 10 lakhs to even 14 lakhs per year.

The industry of data science had been previously (2022) facing a large shortage of skilled professionals which have increased in large numbers this year (2023).

By taking a data science course aspirants will be well equipped with all the necessary information in order to succeed in their future data science career.

What Does It Take To Be A Good Data Scientist?

Posted on September 18, 2020April 2, 2024 by Imarticus Learning

What does a data scientist do?

The importance and applications of data science have grown exponentially over the past decade. Data science is still in its nascent stage and there’s a whole lot to be identified about this discipline. Businesses have started implanting strategic decision-making tools that leverage data science.

Data helps businesses by providing them with hidden insights and helps them predict the future outcome of their decision. This helps organizations to make a better business decision.

Let’s delve deeper into what these data scientists do and how it helps the organizations.

Finding a solution to business problems

One of the most basic and key responsibilities of data scientists in an organization is to identify existing challenges and problems that a business is facing and finding solutions to remedy the situation. This might seem like a generic responsibility of every important professional but the main difference here is that data scientists use tons of relevant data to find the problem.

They try to come up with solutions after properly assessing the situation using various analytical tools that provide them with useful insights. They leverage statistical analysis, data visualization and mining techniques to provide effective solutions.

Find out relevant data using complex research

The 21^st Century businesses are complex than ever, there are various factors that determine the fate of an organization. With the number of complexities that exist, it’s very difficult to figure out what impacts your business and how it does that.

Data scientists simplify this for organizations by studying all variables affecting a business. They use complex research work to identify the variables that have a maximum impact over the business and which are highly relevant.

Identify patterns and trends

Another important work of a data scientist that helps businesses is to identify patterns and trends. Data scientists use sophisticated data analysis techniques to find trends and patterns from the data sets at hand. These data sets are generally historical records of the organization. It helps them to identify the existing patterns and trends which is used to make predictions regarding the future movement of the variables.

How to become a data scientist?

Data science is one of the most in-demand skills in the industry and given the wide range of applications that it has, the demand for a data science professional will continue to rise in the future. One of the most common questions in the minds of data science aspirants is how to become a data scientist? There is no specific answer to this particular question. It depends on what stage of your career you are at and the skillset that you have.

A data science course by reputed institutions such as Imarticus Learning guarantees placement with top-notch firms in the industry in addition to providing relevant knowledge and skills. It also helps you provide guidance from the industry experts who are highly experienced in this domain.

Let’s delve deeper into some of the most prominent skills for data scientists that you should hone if you are planning to opt for a career in this field.

Analytical skills

One of the key skills that are required in this profession and that forms the base of all your work is your analytical skills. One should have an analytical mindset and should be able to identify trends and patterns from a big chunk of data. You should be able to assess a situation from a different perspective to reach a successful conclusion. One should be trained to work with software like Python and R and should be equipped enough to handle large volumes of data.

Problem-solving skills

Another important skill that you need to work on is your problem-solving skills. You need to use data to figure out challenges that exist in the business. After you have figured out the problems you will have to provide a solution using data analytics tools that will help the business to achieve its goals and objectives.

The Increase in Data Science Education in India, Explained!

Posted on June 22, 2020March 26, 2024 by Imarticus Learning

Data science jobs and related roles are increasingly becoming some of the most coveted jobs across industries. This is partly due to how the data science field can cut across industries to be of value, but also thanks to its resilience in tough times and the needs it has responded to.

Over the past few months, colleges and academic institutions have seen a significant rise in enrollment in data science courses in India. The choice is wide– potential students can choose from full-time, part-time or short and snappy online courses to either fill a gap in their skillset or experiment outside their comfort zones.

Although the potential for online learning had been realised by many even a few years ago, certain situations contributed to its exponential rise in recent times.

WFH and Remote Learning During the Coronavirus Pandemic

As lockdowns and shelter-in-place restrictions were imposed on countries all over the world, schools and colleges also had to pull down the shutters. Learning was taken online; in many institutions, exams and lessons were replaced by the opportunity to take online courses that otherwise wouldn’t have been accessible. Whether as a result of this or to fuel this trend, online education providers also reduced or waived off subscription fees and made certain courses available to all regardless of budget or geographies.

As a result, there was a surge in remote and online learning, not just from universities that students were enrolled in but also from coveted universities on the other side of the world. With the demand for data scientists expected to increase, professionals see new opportunities for growth. This, in turn, fueled the desire for upskilling and even pivoting careers as the economy slowed down.

Exposure to Global Universities and Opportunities

Online learning has made courses available in virtually any country from international universities and institutions. By making education accessible globally, online learning significantly increases the scope of the curricula as well as the teaching standards. Another benefit of this exposure is also the ability of graduates and professionals to connect with industry experts in other countries.

Enrolling for data science courses in India that are offered by global universities is also a fantastic learning opportunity.

It exposes students to data science landscapes in other countries as well as lays bare the scope and possibilities they have well within their reach.

Once countries open up and travel restarts, students might also consider physically enrolling in these universities to explore topics further. Having a certificate or two in your portfolio indicates to the interviewer or the recruiter that you are interested and have done preliminary research which has only served to whet your appetite further.

Completely Online Courses

Until very recently, full-fledged online courses weren’t popular or even encouraged by governmental departments in India. Indian universities and colleges have not been permitted to deliver over 20 per cent of a degree online for several years. However, in the first move of its kind, the government gave the green signal for fully online courses in order to democratize education and erase barriers to learning caused by transport, accommodation and overall access.

The approach to fully online degrees is still cautious and restricted to particular subject areas. That said, it is still a welcome shift, especially for those looking to find data science jobs but lacking the access to opportunities that a lot of metropolitan cities and countries enjoy.

Conclusion

Online learning has significantly cut down barriers to entry that involve finance and access. It is a welcome step towards democratizing knowledge and making certain domains of the job market accessible to virtually anyone with a smartphone and a stable internet connection.

Seeing as data science jobs are set to increase in number, now is the ideal time for this surge in data science education, so that students are well-prepared for roles of the future.

Tag: Data Science Jobs