Big Data Analytics Skills To Boost Your Salary

Reading Time: 6 minutes

Are you wondering what the buzz about big data is? Should you make a transition to a career in big data analytics? Well, whether you are already in data analytics or IT, you have come to the right place. As the buzz around big data gathers momentum, more and more professionals are gearing up to improve their employable skills in big data analytics.

Why so, you may wonder.

It is a data-driven world today where more and more businesses are implementing analytics in a big data ecosystem. A Gartner survey shows that more than 75 % of businesses are investing in big data. Whether it is the banking and financial industry, insurance, eCommerce, health sector or travel, all of these are grappling with voluminous data in real-time. Making sense of this data is the domain of the big data analyst.

As the always-connected environment in everyday life gains traction with more and more connected devices, data is generated in huge volumes at great speeds in real-time. Big data analytics has emerged as a leading disruptor in most sectors, powering data-driven insights for transforming customer experiences. Big data is here to stay, and the best way to carve a high-paid salary in analytics is by walking the big data path!

Why is there a need for Big Data Analytics skills?

With the rapid implementation of data analytics in critical business operations and decision making, there has been an increase in the demand for big data experts among other analytics professionals. Individuals with the right set of Big Data analytics skills will have an immense opportunity to pursue a rewarding career in analytics across industries

Big data analytics skillsSource: RHT’s Salary Guide, 2019

For some organizations, big data analytics plays a vital role in decision making. Other areas of application include customer relationship, enabling key strategic initiatives, risk management, and improved financial performance among others.

The soaring demand for big data analytics professionals shows that the market is ready for such technologies. The growth in job opportunities against a lack of supply has created a gap that needs to be filled by skilled professionals and organizations are not shying away from highly rewarding the professionals with the right mix of big data analytics skills.

Read Also: What Your Boss Wants You to Know About Big Data Analytics

According to the latest industry reports, big data analytics salaries are at an all-time high and expected to get better. Entry-level salaries begin at Rs 7.5 lakhs p.a. and go up to Rs 12.10 lakhs p.a. at the senior levels. According to an Edvancer-AIM study on analytics career scope, the median salaries offered are Rs 10.5 lakhs p.a, while 40% of advertised analytics jobs offer salaries of more than Rs 10 lakhs p.a. The percentage by hiring across tools and skills records the highest for R statistical tool at 36 %, followed by Python at 30%; and Hadoop and SAS at 20% each.

At the same time, it is about your data analysis competency and logical reasoning. The more you learn the technical and open source frameworks dedicated to handling big data analytics, the better is your chance to get a salary that you have always dreamed of.

There are multiple job roles that require professionals adept with data analytics skills in demand. If seen from the career point of view, there are a lot of opportunities for big data professionals in different domains and type of work.

Big data analytics skills


Here are the top five big data analytics skills that will likely boost your salary:

The top 5 skills you need to learn to excel in your big data analytics career are:

– Apache Hadoop

– Apache Spark

– Machine Learning and Data Mining


– Statistical Tools

All these skills are explained below in detail.

1. Apache Hadoop

The Apache Hadoop is an open-source project that allows fast processing and insights into huge volumes of structured and unstructured data. The Hadoop has emerged as the driving force behind the growth of big data analytics, with spin-offs in a BI environment. The powerful big data platform offers the Hadoop expert the ability to leverage the central elements of the Hadoop stack for deep and fast analytics. So learning the Hadoop framework, together with the underpinning programming models, such as the Map Reduce, Hive, Pig, and HBase is critical to earning analytical brownies in big data analytics.

2. Apache Spark

In-memory computing enables high-performance algorithms for faster processing. The capability of end-to-end insights in real-time with sophisticated ‘what if’ simulations, has seen increased adoption of Apache Spark by big data practitioners. As mastering the Spark is considered a challenge for many analysts, the professional who is equipped with skills in Apache Spark is sure to command a salary on his terms.

3. Machine Learning and Data Mining

Machine learning is an AI technology that powers big data analytics by learning rules iteratively for improved analytics. Getting trained in machine learning algorithms and rule defining for spotting anomalies and patterns is a popular demand in sectors like banking, finance, and trading. The ability to build predictive analytics in a big data engine for personalized customer experience is a highly sought-after skill.

Read Also: How Big is the Scope of Big Data in India

Data mining is a technique that integrates structured and unstructured data from multiple disparate channels for big data analytics. It can pull any data, including social media, for a 360-degree view of the customer. If you are armed with the skills of data mining, you can bring value to a business. In turn, you can demand the top salary for your data mining skills.

4. NoSQL

Distributed, scale-out NoSQL powered databases continue to be the rage. NoSQL database skills power big data analytics from huge data, with quick iterations and coding. Its many advantages over the traditional RDBMS have pushed NoSQL to the forefront. The distributed architecture of NoSQL framework allows high-performance big data analytics at massive scale, making it almost indispensable in recent times. So the analytics professional armed with working knowledge of one or more NoSQL databases can certainly boost his salary.

5. Statistical Tools

The power of statistical reasoning cannot be underestimated, especially for analytics presentations for businesses. Statistical tools like R, SAS, Matlab, SPSS, or Stata are long-time favoured tools for enterprise big data analytics. Analytical tasks with massive data can be handled with ease, such as coding with social media analytics; data mining, clustering, and regression models. The advantage of R as an open-source model lends its ability to be paired with other technologies and big data products. The statistical tools are a great learning curve for those who want to work as quants, or in the field of retail, insurance or market research.

How to develop big data skills?

After knowing everything about the scope of big data, the available job roles and the most useful tools to learn, the most important question that arises is how to learn all the skills needed to become a successful big data analytics professional. Professionals need to carve out a clear cut roadmap for themselves and follow it ardently.

Along with the technical skills, certain business skills need to be learnt too, which include:

Big data analytics skills


Other skills that would help professionals to perform better in their respective roles are:

– Data Visualization skills

– Analytical Skills

– Programming Knowledge

All these skills are explained below in detail.

Data Visualization skills: The ability to correctly interpret data by visualizing it is crucial. A certain understanding of scientific and mathematical concepts gives an edge to better interpret the data and communicate it to stakeholders. The sense of creativity while visualizing data is an added advantage.

Analytical Skills: Having a strong knowledge of analytics tools is great but being familiar with the business domain is beneficial for analytics professionals to better understand the data and analyse it accurately keeping in mind the end goal and vision.

Read Also: Use of Big Data Analytics in Everyday Life

Programming Knowledge: The ability to code along with statistical and quantitative analysis is an important skill in the realm of big data analytics. A fair knowledge of object-oriented languages, data structures, and algorithms go a long way.

The best way to learn big data analytics skills is to upskill with relevant programs provided by reputed ed-tech firms like Great Learning.

In the race to secure the best salary in big data analytics jobs, single skill development is not enough. Each of the above skills has its own merits. So plan a gradual scale-up of big data analytics learning, with all the five skills, to fetch you a good analytics position. The salary package and career graph in the big data analytics landscape have never been better! So go ahead, and arm yourself with the necessary technical skills. Learn big data with big data certification and online training programmes on-the-go, together with your studies or present job. Get trained on the most relevant big data analytics tools that best fit your career trajectory. Strengthen your analytical skills in a big data environment with suitable weekend programmes, and domain-based management skills for that salary package booster. So if you are a student or analytics newbie, there is no time like the present.

4 Ways Big Data Analytics Is Transforming the Manufacturing Industry

Reading Time: 3 minutes

For a long time, the manufacturing industry was associated with a slew of problems – health risks, worker unions, poor optimization methods, and what not. However, technology and Big Data Analytics (BDA) to be precise, emerged as a game changer and is now taking the factories and production units to the next level.

  1. Making Factories “Smarter”

    Industrie 4.0 is a quintessential example of how modern factories will look like. It’s a German government initiative – a high-tech strategy to promote computerization of manufacturing that has laid the foundation, the roadmap of smart factories covering every process, from product idea to development and from recycling to maintenance.

    Industrie 4.0 comprises of:

  • Interoperability:

    Machines and sensors are connected to a network and work in sync.

  • Automation: 

    Physical devices are capable of making decisions on their own and thus, are automated.
    While experts believe that India is one of the ideal countries to benefit from the Industrie 4.0 model, Cincinnati, Ohio has already declared itself an “Industry 4.0 demonstration city” and is investing a significant amount of money for innovation and development in this area.

Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Optimizing Quality Checks

    Intel has been one of the biggest companies to actively incorporate BDA into its manufacturing processes. Since quality assurance is an important part of its chip-manufacturing process, as is with most manufacturers, it has to run about 19,000 tests on each individual chip. However, harnessing the power of BDA it was able to drastically reduce these steps. For instance, Intel’s analytics system can now go through historical data collected during the manufacturing process at the wafer level and identify only those chips that actually need testing. The chipmaker saved about $3 million in manufacturing costs way back in 2012 using the predictive analytics process implemented on its line of Intel Core processors.

  1. Improving Accuracy and Quantity of Production

    McKinsey gave the perfect example of how BDA can improve manufacturing practices to a great extent. A bio pharmaceuticals manufacturer that produces a certain category of pharma products involving blood components, hormones, and vaccines has to monitor more than 200 variables to ensure purity. However, surprisingly, the yields of two separate batches of the same product produced using the exact same process can vary by as much as 50% to 100%. Given how expensive health care products can be, even a 10% yield difference can cost a lot. Fortunately, there is an easy solution. By dividing the entire production process into smaller segments and applying data analytics on each, the project team can process the inter dependencies and identify the parameters directly responsible for the yield difference. So, modifying these parameters accordingly the team can improve the production quantum by as much as 50% easily, thus saving annual costs by as much as $10 million.

    Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Bettering Collaboration to Promote 3D Printer Factories and MaaS

    3D printers are trending as much BDA. A 3D printer factory can work naturally and most efficiently on a foundation set by BDA. Moreover, we can have a new type of service – Manufacturing-as-a-Service (MaaS) just like Software-as-a-Service we have today.
    3D printer manufacturers such as Materialise and Shapeways are already working on MaaS. With a production of about 200,000 items a month, the latter is doing an astounding level of business with the help of automated software and 3D printers that run 24/7. With BDA, these factories are able to work in a highly collaborative environment where the flow of data and information through engineering, machine operators, quality control, etc. is seamless. The result is remarkable efficiency and quick feedback implementation.

To conclude, Big data analytics (BDA) is the future of manufacturing. It’s providing us the tools and the technology to help create the world where there are automated factories that produce at their highest efficiency and cause minimum wastage of time and resources. Also, the top players are already aware of it and so have taken the lead.

How Big is the Scope of Big Data in India

Reading Time: 2 minutes

With everyone talking about it and recognizing it as one technology trend that can’t be ignored, we are truly in the era of Big Data. As Big Data and analytics continues to become mainstream, the market is growing at a staggering pace. According to a report by IDC, the big data and business analytics market will grow to $203 billion by 2020.

So, how is India placed to cash in on this Big Data wave? Well, if we look at the top industry reports, all of them stress that India currently stands on the cusp of a major opportunity and has the potential to emerge as a Data hub.


Let’s look at the key drivers that will help India to capitalize on the growing Big Data market opportunity:

Talent Pool: India’s biggest lead lies in being favorably placed to address analytics skill gap, which has emerged as a significant Big Data challenge globally. With an abundance of mathematics and statistics talent and one of the largest pools of engineers in the world, India is expected to churn out professionals with deep analytics skills in the coming years. As per a Nasscom-Crisil report, India ranks second, just after the US, in Big Data talent availability and service provider’s initiatives to build talent.

Read Also: 7 Domains Benefiting from Big Data and Machine Learning

Preferred Outsourcing Destination: India has a well-established IT-BPO and knowledge service outsourcing industry and has a reputation for providing outsourcing services at the best possible performance-to-cost ratio. Hence, India already sees a huge demand for outsourced analytics work and is expected to emerge as the key outsourcing destination in the overall Data space.

Domestic Demand: India is also witnessing a huge demand for Big Data analytics domestically, not only from enterprises but also from the government. In fact, government departments and public agencies are looking at Big Data analytics as a wonder drug to address country’s perennial issues, such as air pollution, low crop yield, power shortage, financial inclusion, to name a few.

Thriving Startup Ecosystem: Another major advantage is India’s vibrant start-up ecosystem in the analytics space. As per the estimate of IT industry lobby body NASSCOM, as of last year there were over 600 analytical firms in the country, out of which over 400 were start-ups.

Read Also: Use of Big Data Analytics in Everyday Life

Perfect Testbed for Large-Scale Big Data Projects: Considering India’s vast population (volume), variety (diverse culture) and velocity (the amount of data being added every day), service providers are eyeing India as the perfect place to carry out Big Data projects of large scale and test innovative solutions. A perfect example of a massive Big Data project is Aadhaar. The scope of Aadhaar is to capture 12 billion fingerprints, 1.2 billion photographs, and 2.4 billion iris scans. Given the project’s size and complexity, it has become an example and reference point for Big Data project implementation and execution for countries worldwide.

With domestic and international companies alike looking at India to supply the right skill set, demand for data scientists in the country is on the rise. Already, verticals like e-Commerce, BFSI, telecom have invested heavily in their data science teams. In the coming years, other industries too are expected to join the race to rope in the best talent, while offering fat salaries. Professionals with Big Data certification stand to gain the most from this phenomenal increase in demand.