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

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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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Explaining the Job Nitty Gritty of a Data Scientist

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Explaining the Job Nitty Gritty of a Data Scientist

What do data scientists do? Since the inception of the term data science, we’ve heard about how it transforms all major sectors, including retail, agriculture, health, legal, telecommunications and automobile industry, but little do we know what exactly the job entails.

Following a recent DataCamp podcast DataFramed, we found out a set of key things about data scientists, and they are as follows:

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Not only tech, but other industries are being explored

A prominent data scientist from Convoy shared insights about how their company is leveraging data science to revolutionize North American trucking industry. Then again, data science is also deemed to make a significant impact on cancer research. So, from this we can understand that data science is not only limited within the walls of technology but has started to seep through different industry verticals.

via GIPHY

It’s beyond AI and self-driving cars

Sure, deep learning and machine learning are powerful applications, but not all data scientists are lost waddling around these top notch techniques. Instead, most of the regular data scientists earn their daily bread and butter through data accumulation and cleaning, creating reports and dashboards, data viz, statistical inference, communicating and convincing decision-makers about key outcomes.

Skill evolution

“Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” – The latter is crucial, so is communicating results.

However, these skills are likely to change very quickly. In a very short span of time. Rapid development across diverse open-source ecosystem is evident; as a result any kind of skill or expertise is unlikely to last long.

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Specialization is the key

It’s better to break down data science into three main components: Business Intelligence, which talks about pulling out data and presenting it to the right people in the form of reports, dashboards and mails; Decision Science, which is all about gathering company data and analyzing it for decision-making; and Machine Learning, which deals with the ways in which we can use data science models and put them into production.

Choosing a distinct career path is an emerging trend and it’s gaining a lot of popularity for all the right reasons.

Ethics is a driving factor

No wonder, this profession is full of uncertainty; at a time, when most of our daily interactions are influenced by algorithms designed by data scientists, what role do you think ethics play? On this context, this is what Omuji Miller, the senior machine learning data scientist at GitHub has to say:

‘We need to have that ethical understanding, we need to have that training, and we need to have something akin to a Hippocratic oath. And we need to actually have proper licenses so that if you actually do something unethical, perhaps you have some kind of penalty, or disbarment, or some kind of recourse, something to say this is not what we want to do as an industry, and then figure out ways to remediate people who go off the rails and do things because people just aren’t trained and they don’t know.’

Soon, we’re approaching a state where the need to maintain ethical standards would come from within data science itself and advocates, legislators and other stakeholders. Hope this consensus comes soon.

The data science revolution is quite the order of the day, and it’s going to stay for a while. So, if you want to ace up your data skills, we’ve superior Data Science Courses in Delhi. Just, visit our website and pore over our course offerings.

 

The blog has been sourced from — hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

 

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The Big Data Driven Future of Fashion: How Data Influences Fashion

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The Big Data Driven Future of Fashion: How Data Influences Fashion

Big Data is revolutionizing every industry, including fashion. The nuanced notion of big data is altering the ways designers create and market their clothing. It’s not only aiding designers in understanding customer preferences but also helps them market their products well. Hadoop BI is one of the potent tools of technology that provides a wide pool of information for designers to design range of products that will sell.

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How Does the Mechanism Work?

Large sets of data help draw patterns and obviously trends play a crucial role across the fashion industry. In terms of nature, fashion and trends both are social. Irrespective of the nature of data, structured or unstructured, framing trends and patterns in the fashion industry leads to emerging ideas, strategies, shapes and styles, all of which ushers you into bright and blooming future of fashion.

What Colors To Choose For Your Line?

KYC (Know Your Customer) is the key here too. A fashion house must know which colors are doing rounds amongst the customers. Big data tells a lot about which color is being popular among the customers, and based on that, you can change your offerings subject to trend, style picks and customer preferences.

Men’s or Women’s Clothing: Which to Choose?

Deciding between men’s or women fashion is a pivotal point for any designer. Keep in mind, target demographic for each designer is different, and they should know who will be their prospective customers and who doesn’t run a chance.

Big data tool derive insights regarding when customers will make purchases, how large will be the quantity and how many items are they going to buy. Choosing between men’s and women’s fashion could make all the difference in the world.

Arm yourself with business analyst training courses in Gurgaon; it’s high time to be data-friendly.

Transforming Runway Fashion into Retail Merchandise

Launching a brand in the eyes of the public garners a lot of attention, and the designs need to be stellar. But, in reality the fashion that we often see on runways is rarely donned by the ordinary customers; because, the dresses and outfits that are showcased on the ramp are a bit OTT, thus altered before being placed in the stores. So, big data aids in deciphering which attires are going to be successful, and which will fail down the line. So, use the power of big data prudently and reap benefit, unimaginable across the global retail stores.

Deciding Pricing of the Product

As soon as the garbs leave the runway, they are tagged with prices, which are then posted inside the stores, after analyzing how much the customers are willing to pay for a particular product. For averaging, big data is a saving grace. Big data easily averages the prices, and decides a single mean price, which seems to be quite justifiable.

However, remember, while pricing, each garments are designed keeping in mind a specified customer range. Attires that are incredibly expensive are sold off to only a selected affluent user base, while the pricing of items that are designed for general public are pegged down. Based on previous years’ data, big data consultants can decide the pricing policy so that there’s something for all.

The world of fashion is changing, and so is the way of functioning. From the perspective of fashion house owner, collect as much data as possible of customers and expand your offerings. Big data analytics is here to help you operate your business and modify product lines that appeals to the customers in future.

And from the perspective of a student, to harness maximum benefits from data, enroll in a data analyst course in Gurgaon. Ask the consultants of DexLab Analytics for more deets.

 

The article has been sourced from –

channels.theinnovationenterprise.com/articles/8230-big-data-hits-the-runway-how-big-data-is-changing-the-fashion-industry

iamwire.com/2017/01/big-data-fashion-industry/147935

bbntimes.com/en/technology/big-data-is-stepping-into-the-fashion-world

 

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Business Intelligence: How to Enhance User Adoption?

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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

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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

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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

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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

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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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How Aspiring Data Scientists Should Choose a Suitable Programming Language for Data Science

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How Aspiring Data Scientists Should Choose a Suitable Programming Language for Data Science

Data science is a fascinating and one of the fastest growing fields in the world to work in. This is why it’s becoming increasingly popular for data scientists to consider the potentials of programming languages-they form an integral part of data science.

Possessing incredible skills of programming instantly pumps up the chances of bagging a high-profile data science job, whereas the novices, who have never studied programming in their entire life have to struggle hard.

However, this is not all – only a sack of all-round programming skills won’t help you grab the sexiest job of 21st century, there are several things to consider before you set off on becoming a successful data scientist. And they are as follows:

Generality

For a true blue data scientist, it’s not enough to possess encompassing programming skills but also the aptitude for crunching numbers. Remember, a data scientist’s day is largely spent on sourcing and processing raw data for the purpose of data cleaning – no amount of smart set of programming languages or machine learning models would be of any help.

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Specificity

In advanced data science, learning knows no bounds – each time you get to reinvent something new. Learn to ace a wide array of packages and modules available in a chosen language. However, the extent of the use and application is subject to the domain-particular packages you are working on.

Performance

In few cases, optimizing the performance of the codes is essential, especially when tackling huge volumes of crucial data. Compiled languages are normally faster as compared to interpreted ones; in the same way, statically typed languages are more fail-proof than dynamically typed. As a result, an apparent trade-off exists against productivity.

With all these in mind, it’s time to delve into the most popular languages used in the field of data science – let’s start with R – it’s the most powerful open source language used for a gamut of statistical and data visualization applications, including neural networks, advanced plotting, non-linear regression, phylogenetics and lot more.

Next, we can’t help but brag about an excellent all-rounder – Python – a top notch programming language choice for all types of data scientists, seasoned and freshers. A large chunk of the data science process revolves around the cutting edge ETL process – this makes Python a universal language to excel at. Google’s Tensorflow is an added bonus point.

Lastly, SQL tops rank as a leading data processing language instead of being just an advanced analytical tool. Owing to its longevity and efficiency, SQL is deemed to be one of the most powerful weapons that modern data scientist should know of.

Parting Thoughts

In the end of the discussion, we now have a set of languages to consider for excelling data science – what you need to do is comprehend your usage requirements and compare generality, specificity and performance factors. This will help you surge towards a successful career minus the complexities associated.

DexLab Analytics offers top of the line Data Science Courses in Delhi for data enthusiasts. If you are interested in a data analyst course in Noida, drop by this esteemed institute and navigate through our in-demand courses.

 

The blog has been sourced from – 

https://medium.freecodecamp.org/which-languages-should-you-learn-for-data-science-e806ba55a81f

https://towardsdatascience.com/what-programming-language-should-aspiring-data-scientists-learn-875017ad27e0

http://bigdata-madesimple.com/how-i-chose-the-right-programming-language-for-data-science

 

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