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How Data Analytics Should Be Managed In Your Company, and Who Will Lead It?

How Data Analytics Should Be Managed In Your Company, and Who Will Lead It?

In the last couple of years, data management strategies have revolutionized a lot. Previously, the data management used to come under the purview of the IT department, while data analytics was performed based on business requirements. Today, a more centralized approach is being taken uniting the roles of data management and analytics – thanks to the growing prowess of predictive analytics!

Predictive analytics has brought in a significant change – it leverages data and extracts insights to enhance revenue and customer retention. However, many companies are yet to realize the power of predictive analytics. Unfortunately, data is still siloed in IT, and several departments still depend on basic calculations done by Excel.

But, of course, on a positive note, companies are shifting focus and trying to recognize the budding, robust technology. They are adopting predictive analytics and trying to leverage big data analytics. For that, they are appointing skilled data scientists, who possess the required know-how of statistical techniques and are strong on numbers.

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Strategizing Analytical Campaigns

An enterprise-wide strategy is the key to accomplish analytical goals and how. Remember, the strategy should be encompassing and incorporate needful laws that need to be followed, like GDPR. This signifies effective data analytics strategies begin from the top.

C-suite is a priority for any company, especially which looks forward to defining data and analytics, but each company also require a designated person, who would act as a link between C-suite and the rest of the company. This is the best way to mitigate the wrong decisions and ineffective strategies that are made in silos within the organization.

Chief Data Officers, Chief Analytics Officers and Chief Technology Officers are some of the most popular new age job designations that have come up. Eminent personalities in these fetching positions play influential roles in strategizing and executing a successful corporate-level data analytics plan. The main objective of them is to provide analytical support to the business units, determine the impact of analytical strategies and ascertain and implement innovative analytical prospects.

Defensive Vs Offensive Data Strategy

To begin, defensive strategy deals with compliance with regulations, prevention of theft and fraud detection, while offensive strategy is about supporting business achievements and strategizing ways to enhance profitability, customer retention and revenue generation.

Generally, companies following a defensive data strategy operate across industries that are heavily regulated (for example, pharmaceuticals, automobile, etc.) – no doubt, they need more control on data. Thus, a well-devised data strategy has to ensure complete data security, optimize the process of data extraction and observe regulatory compliance.

On the other hand, offensive strategy requires more tactical implementation of data. Why? Because they perform in a more customer-oriented industry. Here, the analytics have to be more real-time and their numerical value will depend on how quickly they can arrive at decisions. Hence, it becomes a priority to equip the business units with analytical tools along with data. As a result, self-service BI tools turns out to be a fair deal. They are found useful. Some of the most common self-service BI vendors are Tableau and PowerBI. They are very easy to use and deliver the promises of flexibility, efficacy and user value.

As final remarks, the sole responsibility of managing data analytics within an organization rests on a skilled team of software engineers, data analysts and data scientists. Only together, they would be able to take the charge of building successful analytical campaigns and secure the future of the company.

For R Predictive Modelling Certification, join DexLab Analytics. It’s a premier data science training platform that offers top of the line intensive courses for all data enthusiasts. For more details, visit their homepage.

 

The blog has been sourced from dataconomy.com/2018/09/who-should-own-data-analytics-in-your-company-and-why

 

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Best Data Science Interview Questions to Get Hired Right Away

Best Data Science Interview Questions to Get Hired Right Away

Data scientists are big data ninjas. They tackle colossal amounts of messy data, and utilize their imposing skills in statistics, mathematics and programming to collect, manage and analyze data. Next, they combine all their analytic abilities – including, industry expertise, encompassing knowledge and skepticism to unravel integral business solutions of meaningful challenges.

But how do you think they become such competent data wranglers? Years of experience or substantial pool of knowledge, or both? In this blog, we have penned down the most important interview data questions on data science – it will only aid you crack tough job interviews but also will test your knowledge about this promising field of study.

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What do you mean by data science?

Data is a fine blend of statistics, technical expertise and business acumen. Together they are used to analyze datasets and predict the future trend.

Which is more appropriate for text analytics – R or Python?

Python includes a very versatile library, known as Pandas, which helps analysts use advanced level of data analysis tools and data structures. R doesn’t have such a feature. Therefore, Python is the one that’s highly suitable for text analytics.

Explain a Recommender System.

Today, a recommender system is extensively deployed across multiple fields – be it music recommendations, movie preferences, search queries, social tags, research and analysis – the recommender system works on a person’s past to build a model to predict future buying or movie-viewing or reading pattern in the individual.

What are the advantages of R?

  • A wide assortment of tools available for data analysis
  • Perform robust calculations on matrix and array
  • A well-developed yet simple programming language is R
  • It supports an encompassing set of machine learning applications
  • It poses as a middleman between numerous tools, software and datasets
  • Helps in developing ace reproducible analysis
  • Offers a powerful package ecosystem for versatile needs
  • Ideal for solving complex data-oriented challenges

What are the two big components of Big Data Hadoop framework?

HDFS – It is the abbreviated form of Hadoop Distributed File System. It’s the distributed database that functions over Hadoop. It stores and retrieves vast amounts of data in no time.

YARN – Stands for Yet Another Resource Negotiator. It aims to allocate resources dynamically and manage workloads.

How do you define logistic regression?

Logistic regression is nothing but a statistical technique that analyzes a dataset and forecasts significant binary outcomes. The outcome has to be in either zero or one or a yes or no.

How machine learning is used in real-life?

Following are the real-life scenarios where machine learning is used extensively:

  • Robotics
  • Finance
  • Healthcare
  • Social media
  • Ecommerce
  • Search engine
  • Information sharing
  • Medicine

What do you mean by Power Analysis?

Power analysis is best defined as the process of determining sample size required for determining an impact of a given size from a cause coupled with a certain level of assurance. It helps you understand the sample size estimate and in the process aids you in making good statistical judgments.

To get an in-depth understanding on data science, enroll for our intensive Data Science Certification – the course curriculum is industry-standard, backed by guaranteed placement assistance.

The blog has been sourced fromintellipaat.com/interview-question/data-science-interview-questions

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3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

The job responsibility of data scientists is no mean feat. They stay under a lot of pressure. A wide number of stumbling blocks are laid in front of them, which makes it really difficult for them to secure the long-shot business goals and objectives.

As prevention is better than cure – being aware of the challenges always help data scientists plot the shortest and smartest route to success, and we can’t agree more. Brace yourselves! Below, we’ve enumerated some of the challenges data scientists face while getting started with an IoT project:

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Inferior Data Quality

Messy data is life and soul of data scientists. Irrespective of business scale, the job of every data scientist is to organize data in the correct manner. But, however organizing them may require adequate time as well as hard work.

A fundamental rule – avoid manual data, wherever possible. Intelligent data compilation is the final key to high quality data, which is a prerequisite for favorable company operation. It includes crisp communication, regular anomaly detection, logic determination and well-defined industry standards. Another way to tame your data can be through application integration tools – they are a fabulous way to automate data entry and lessen escalation of typographical errors, individual eccentricities, staggering spellings and more from the data.

Once data is in the right format and quality, data scientists can start slicing off the data they don’t need any more, which takes us to the next step.

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Shedding Out Excessive Data

Though big data is found in abundance, too much of data can also pose a substantial challenge. This is why employing superior data selection techniques and minimizing features are supported, they help eliminate unwanted chaos cutting through what matters the most.

What happens is that when data becomes excessively large, we often end up developing high-end predictive models that fails to deliver productive results. But, on the other hand, if you track the events, giving importance to validation and testing routines, the outcomes will spell perfection. And that’s what we are looking forward to.

Predictive Analytics is the Key

IoT has made predictive analytics a daunting reality. Owing to its critical business significance, predictive analytics is quickly accelerating along the priority ladder of IoT stakeholders. However, take a note, this breed of analytics may not be fruitful in every instance. It’s imperative to begin your analytics endeavor by clearly defining your module’s objective, followed by needed research and valuation.

Next, you need to sync in with subject matter pundits to ascertain which predictions will lead you closer to fulfilling the business objectives. Following to this, you have to be sure that you have all the data required to make prediction. In other cases, you can re-set goals, anytime.

Find the best Data Science Courses in Noida… At DexLab Analytics. Get detailed information on the website.

 

The blog has been sourced from — www.networkworld.com/article/3305329/internet-of-things/3-iot-challenges-that-keep-data-scientists-up-at-night.html

 

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7 Leading Sectors in India That Need an AI & Analytics Makeover

7 Leading Sectors in India That Need an AI & Analytics Makeover

Advancements in the field of data analytics and artificial intelligence are fuelling innovation in every nation around the world. India too is showing keen interest in AI. This year, the government has doubled the amount invested in the innovation program Digital India which drives advances in machine learning, AI and 3-D printing.

The signs of increased activity in AI research and development are showing in different areas. Here are the topmost sectors of India that are in dire need of AI and data science revolution:

FINANCE

According to reports by PricewaterhouseCoopers, financial bodies and payment regulators deal with billions of dollars in transactions through ATMs, credit cards, e-commerce transactions, etc. When human expertise is combined with advanced analytical methods and machine learning algorithms, fraudulent transactions can be flagged the moment they occur. This leaves less room for human errors. Considering the recent discoveries about major frauds in reputed banks in India, this approach seems more like a necessity.

Image source: American Banker

 

AGRICULTURE

Although 40% of the Indian population works in the agricultural sector, revenues from this sector make up only 16% of the total GDP. The agricultural industry needs advanced data analytics techniques for the prediction of annual, quarterly or monthly yields; analyzing weather reports are observing the best time to sow; estimating the market price of different products so that the most profitable crop can be cultivated, etc. AI powered sensors can measure the temperature and moisture level of soil. With the help of such data farmers can identify the best time to plant and harvest crops and make efficient use of fertilizers.

Image source: Inventiva

HEALTHCARE

According to the Indian constitution, each and every citizen is supposed to get free healthcare. And government hospitals do provide that to people below poverty line. Nonetheless, 81% of the doctors work for private hospitals and nearly 60% hospitals in India are private (According to Wikipedia). The root cause for this is that government hospitals are overpopulated. People who can afford healthcare services from a private hospital prefer to be treated there. Data science can play a pivotal role in managing the growing demand for healthcare services by strengthening the current infrastructure. It can help by predicting how many days a patient is likely to be admitted and find out the proper allotment of beds. AI fine tunes medical predictions and helps selecting a proper line of treatment.

Image source: wxpress

CRIME PREDICTION

Considering the number of security threats and extremist attacks India has faced in the past, there’s urgent need to develop efficient methods that can neutralize such threats and maintain proper law and order. AI and ML can step in to ease the burden of security personnel. A welcome development is the collaboration between Israeli company Cortica and Best Group. Massive amounts of data from CCTV cameras across the nation are being analyzed to anticipate crime and take action before it happens. Streaming data is scrutinized for behavioral anomalies, which are considered as warning signs for a person who commits a violent crime. The aim of the Indian authorities is improving safely in roads, stations, bus stops and other public places.

Image source: Digital Trends

From the paragraphs above it is evident that AI and data analytics has immense scope to improve these major sectors in India. While you look forward to these developments also follow DexLab Analytics, which is a leading data analyst training institute in Delhi. For data analyst certification, get in touch with DexLab’s industry experts.

Reference:

www.brookings.edu/blog/techtank/2018/05/17/artificial-intelligence-and-data-analytics-in-india

www.analyticsvidhya.com/blog/2018/08/top-7-sectors-where-data-science-can-transform-india-with-free-datasets

 

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Business Intelligence: How to Enhance User Adoption?

Business Intelligence: How to Enhance User Adoption?

For business modernization, smart business intelligence solution is the key. Getting to the crux and leveraging vast pools of data that companies gain access to triggers encompassing digital transformation. BI tools not only let companies grasp the data but also develop actionable insights to smoothen the impactful decision-making capabilities and take companies towards future progress.

It’s not an out of ordinary kind of concept, for half a decade, companies have been utilizing these kinds of tools for better efficiency and productive outcomes, yet user adoption for BI tool remains relatively low.

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Reasons behind Lower User Adoption of Business Intelligence:

Guys at the helm of company affairs, including Chief Information Officers, Chief Technology Officers and Chief Data Officers may think it’s high time to incorporate Business Intelligence tools for smarter operations, but it may not have the same effect on the employees. Employees may not be much inspired!

It holds truer, especially for those employees, who have been in the workforce for long and haven’t for once used such intricate, new-age tools to decipher what data says. For them, old is gold – they prefer to continue their own kind of data analysis in the same way they have been doing for so many years.

How Companies Can Improve Data-Driven Mindsets?

In order to be ahead of the curve, the data mindset of the workforce needs to be changed. If businesses have to be completely data-driven, they can’t just take Business Intelligence lightly.

Here are a few ways business can drive user adoption of BI:

Introduce BI as a necessity, not luxury

Once understanding company data was considered as an added advantage to normal work procedures. But, in this age of digital transformation, it’s no longer a luxury but a necessity. And sooner the employees realize this, the better it becomes.

Employees across organizations should have thorough access to data. It boosts decision-making. By going completely data-driven, business intelligence user adoption will automatically improve. Along with employees, businesses too will benefit a lot from such adoption.

Promote Favorable Impacts of BI

Putting light on success stories of BI implementation helps! It’s being regarded as a powerful way to encourage budding data scientists and already in-workforce employees: the powerful impression of BI and its significant impacts on key performance indicators will tell a different story to the world.

The best way of doing it would be by developing an internal case study that will elucidate how a team after incorporating Business Intelligence fulfilled their desired organizational goals. For best results, let a manager or C-level employee present the case study to the workforce. Surely, this will enhance levels of user adoption of BI.

Continuous Training is a Must

Business Intelligence calls for no one-track solutions; the concept deals with almost endless opportunities, which means continuous training initiatives should be taken up to explore every facet of this cutting edge tool.

When an employee have deeper knowledge about a particular tool, they are more likely to derive maximum benefits out of it. So, by giving continuous training, through various FAQs, webinars and video tutorials, employees can now become very easily completely data-driven.

Now, following these easy yet effective tips, business leaders can increase their lower rates of BI adoption and stride towards full digital transformation of their companies, triggering impactful future goals.

Want to know more about Data Science Courses in Noida? Drop by DexLab Analytics; for a fulfilling learning experience, opt for their Data Science Courses. They are simply excellent and student-friendly. 

 
The blog has been sourced from — www.sisense.com/blog/make-business-intelligence-necessity-drive-user-adoption
 

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Top 5 Reasons to Feel Excited about Data Analytics This Year

TOP 5 REASONS TO FEEL EXCITED ABOUT DATA ANALYTICS THIS YEAR

‘Tis the year to be super excited about data analytics! Without further ado, let’s find out why:-

Cloud Infrastructure is Expanding and Fostering Fast-paced Innovations

Considering the recent trends in cloud data and related applications, 2018 is a critical time for cloud analytics. Businesses must steadily transition to a cloud environment and for that a robust and flexible analytics strategy is to be adopted. Through cloud analytics platforms businesses can leverage common data logic and unlock new analytic capabilities to plan, predict, discover, visualize, simulate and manage. In short, what businesses need is a hybrid mode that includes data, analytics and applications spread across multi-cloud and on-premise environments. Research suggests that by employing analytics that are built to work together businesses can increase the total cost of ownership (TCO) by 3-5 times and the return on investment (ROI) can be as high as 171%.

Source: ZDNet

The Power of Machine Learning Unleashed

Machine learning and artificial intelligence have made big progress in the last one year. Hence, automated and AI powered tools are becoming central in decision-making. The rapid growth in automation has profound effect on the way analytics is used. It can be said that machine learning is perking up analytics big time. With the help of automated technologies users can develop contextual insights with ease and uncover patterns from massive volumes of data. And data scientists are harnessing these automated technologies to drive scalable insights for smarter business processes.

Source: Tech Carpenter

The Spreadsheet is Nearing Retirement

The spreadsheet has come a long way since its inception. But, for many businesses it is time to move to better alternatives that are free from some of the inefficiencies and inaccuracies of spreadsheets. For these businesses the solution is shifting to cloud-based models that help connect operational plans to financial plans.

Source: GCN.com

Customer Experience is the Current Competitive Battleground

According to the Harris Interactive study, 88% customers prefer purchasing products or services from a company that offers great customer service over a company that provides the latest innovations. Quality customer experience is crucial for business growth. And for that companies must invest in CEM (customer experience management). CEM technology collects data from varied sources and uses advanced analytics to leverage historical experiences and access data fast. This platform ensures that customers are satisfied, their grievances are addressed and there’s an improvement in sales, profits and brand image.

Source: StoryMiners

Big data Industry to Grow 7 times in 7 years!

Studies suggest that the big data industry in India is likely to become a 20 billion dollar industry by 2015. It is expected that analytics and data science market will grow by 7 times in the next 7 years. Currently, the analytics and big data industry is worth an estimated $2.71 billion in annual revenues and is growing rapidly at a rate of 33.5% CAGR.

Source: Analytics India

Do you know that this year over 16,000 freshers have been hired in the analytics workforce of India? That’s an increase by 33% from last year’s 12,000! Join the big data bandwagon with a professional certificate from this reputed data analyst training institute in Delhi. One of the unique features of this data analyst course in Gurgaon is that it includes trainers who are industry-experts in this field and hence bring with them excellent domain experience.

 

References:

digitalistmag.com/cio-knowledge/2018/01/03/top-10-trends-for-analytics-in-2018-05668659

360logica.com/blog/10-reasons-excited-data-analytics-2018

analyticsindiamag.com/analytics-data-science-industry-in-india-study-2018-by-analytixlabs-aim

getcloudcherry.com/blog/competition-customer-experience

 

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FAQs before Implementing a Data Lake

FAQs before Implementing a Data Lake

Data Lake – is a term you must have encountered numerous times, while working with data. With a sudden growth in data, data lakes are seen as an attractive way of storing and analyzing vast amounts of raw data, instead of relying on traditional data warehouse method.

But, how effective is it in solving big data related problems? Or what exactly is the purpose of a data lake?

Let’s start with answering that question –

What exactly is a data lake?

To begin with, the term ‘Data Lake’ doesn’t stand for a particular service or any product, rather it’s an encompassing approach towards big data architecture that can be encapsulated as ‘store now, analyze later’. In simple language, data lakes are basically used to store unstructured or semi-structured data that is derived from high-volume, high-velocity sources in a sudden stream – in the form of IoT, web interactions or product logs in a single repository to fulfill multiple analytic functions and cases.

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What kind of data are you handling?

Data lakes are mostly used to store streaming data, which boasts of several characteristics mentioned below:

  • Semi-structured or unstructured
  • Quicker accumulation – a common workload for streaming data is tens of billions of records leading to hundreds of terabytes
  • Being generated continuously, even though in small bursts

However, if you are working with conventional, tabular information – like data available from financial, HR and CRM systems, we would suggest you to opt for typical data warehouses, and not data lakes.

What kind of tools and skills is your organization capable enough to provide?

Take a note, creating and maintaining a data lake is not similar to handling databases. Managing a data lake asks for so much more – it would typically need huge investment in engineering, especially for hiring big data engineers, who are in high-demand and very less in numbers.

If you are an organization and lack the abovementioned resources, you should stick to a data warehouse solution until you are in a position of hiring recommended engineering talent or using data lake platforms, such as Upsolver – for streamlining the methods of creating and administering cloud data lake without devoting sprawling engineering resources for the cause.

What to do with the data?

The manner of data storage follows a specific structure that would be suitable for a certain use case, like operational reporting but the purpose for data structuring leads to higher costs and could also put a limit to your ability to restructure the same data for future uses.

This is why the tagline: store now, analyze later for data lakes sounds good. If you are yet to make your mind whether to launch a machine learning project or boost future BI analysis, a data lake would fit the bill. Or else, a data warehouse is always there as the next best alternative.

What’s your data management and governance strategy?

In terms of governance, both data warehouses and lakes pose numerous challenges – so, whichever solution you chose, make sure you know how to tackle the difficulties. In data warehousing, the potent challenge is to constantly maintain and manage all the data that comes through and adding them consistently using business logic and data model. On the other hand, data lakes are messy and difficult to maintain and manage.

Nevertheless, armed with the right data analyst certification you can decipher the right ways to hit the best out of a data lake. For more details on data analytics training courses in Gurgaon, explore DexLab Analytics.

 

The article has been sourced from — www.sisense.com/blog/5-questions-ask-implementing-data-lake

 

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5 Trends Shaping the Future of Data Analytics

5 Trends Shaping the Future of Data Analytics

Data Analytics is popular. The future of data science and analytics is bright and happening. Terms like ‘artificial intelligence’ and ‘machine learning’ are taking the world by storm.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020, says Forbes, a leading business magazine.

 

Last year, at the DataHack Summit Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton shared some slivers of knowledge in the illuminating field of data science. He believes that the following trends will shape up the world of data analytics, and we can’t agree more.

Dive down to pore over a definitive list – thank us later!

Internet of Things (IoT)

Does IoT ring any bell? Yes, it does, because it’s nothing but evolved wireless networks. The market of this fascinating new breed of tech is expected to grow from $170.57 billion in 2017 to $561.04 billion by 2022 – reasons being advanced analytics and superior data processing techniques.

Artificial Intelligence

An improved version of AI is Augmented Intelligence – instead of replacing human intelligence, this new sophisticated AI program largely focuses on AI’s assistive characteristic, enhancing human intelligence. The word ‘Augmented’ stands for ‘to improve’ and together it reinforces the idea of amalgamating machine intelligence with human conscience to tackle challenges and form relationships.

Augmented Reality

Look forward to better performances and successful models? Data is the weapon of all battles. Augmented Reality is indeed a reality now. The recent launch of Apple ARkit is a pivotal development in bulk manufacturing of AR apps. The power of AR is now in the fingertips of all iPhone users, and the development of Google’s Tango is an added thrust.

Hyper Personalization

#KnowYourCustomer, it has become an indispensable part of today’s retail marketing; the better you know your customers, the higher are the chances of selling a product. Yes, you heard that right. And Google Home and Amazon Echo is boosting the ongoing operations.

Graph Analytics

Mapping relationships across wide volumes of well connected critical data is the essence of graph analytics. It’s an intricate set of analytics tools used for unlocking insightful questions and delivering more accurate results. A few use cases of graph analytics is as follows:

  • Optimizing airline and logistic routes
  • Extensive life science researches
  • Influencer analysis for social network communities
  • Crime detection, including money laundering

 
Advice: Be at the edge of data accumulation – because data is power, and data analytics is the power-device.

Calling all data enthusiasts… DexLab Analytics offers state of the art data analytics training in Gurgaon within affordable budget. Apply now and grab amazing discounts and offers on data analyst course.

 

The article has been sourced from – yourstory.com/2017/12/data-analytics-future-trends

 

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A Comprehensive Study on Analytics and Data Science India Jobs 2018

A Comprehensive Study on Analytics and Data Science India Jobs 2018

India accounts for 1 in 10 data science job openings worldwide – with about 90,000 vacancies, India ranks as the second-biggest analytics hub, next to the US – according to a recent study compiled by two renowned skilling platforms. The latest figure shows a 76% jump from the last year.

With the advent of artificial intelligence and its overpowering influence, the demand for skill-sets in machine learning, data science and analytics is increasing rapidly. Job creation in other IT fields has hit a slow-mode in India, making it imperative for people to look towards re-skilling themselves with new emerging technologies… if they want to stay relevant in the industry. Some newer roles have also started mushrooming, with which we are not even acquainted now.

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Top trends in analytics jobs in 2018 as follows:

  • The total number of data science and analytics jobs nearly doubled from 2017 to 2018.
  • There’s been a sharp contrast in the percentage increase of analytics job inventory in the past years – from 2015 to 2016, the number of analytics jobs increased by 52%, which increased by only 40% from 2014 to 2015.
  • Currently, if we go by the reports, nearly 50000 analytics job positions are currently available to get filled by suitable candidates. Although the exact numbers are difficult to ascertain.
  • Amazon, Goldman Sachs, Citi, E&Y, Accenture, IBM, HCL, JPMorgan Chase, KPMG and Capgemini – are 10 top-tier organizations with the highest number of analytics opening in India.

City Figures

Bengaluru is the IT hub of India and accounts for the largest share of the data science and analytics jobs in India. Approximately, it accounted for 27% of jobs till the quarter of the last year.

Tier-II cities also witnessed a surging trend in such roles from 7% to 14% in between 2017 and 2018 – as startups started operating out of these locations.

Delhi/NCR ranks second contributing 22% analytics jobs in India, followed by Mumbai with 17%.

Industry Figures

Right from hospitality, manufacturing and finance to automobiles, job openings seem to be in every sector, and not just limited to hi-tech industries.

Banking and financial sector continued to be the biggest job drivers in analytics domain. Almost 41% of jobs were posted from the banking sector alone, though the share fell from last year’s 46%.

Ecommerce and media and entertainment followed the suit and contributed to analytics job inventory. Also, the energy and utilities seem to have an uptick in analytics jobs, contributing to almost 15% of all analytics jobs, 4% hike from the last year’s figure.

Education Requirement Figures

In terms of education, almost 42% of data analytics job requirements are looking for a B.Tech or B.E degree in candidates. 26% of them prefer a postgraduate degree, while only 10% seeks an MBA or PGDM.

In a nutshell, 80% of employers resort to hiring analytics professionals who have an engineering degree or a postgraduate degree.

As a result, Data analyst course has become widely popular. It’s an intensive, in-demand skill training that is intended for business, marketing and operations managers, data analyst and professionals and financial industry professionals. Find a reputable data analyst training institute in Gurgaon and start getting trained from the experts today.

 

The article has been sourced from:

https://qz.com/1297493/india-has-the-most-number-of-data-analytics-jobs-after-us

https://analyticsindiamag.com/analytics-and-data-science-india-jobs-study-2017-by-edvancer-aim

 

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