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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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How Data Science Is Getting Better, Day by Day?

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HOW DATA SCIENCE IS GETTING BETTER, DAY BY DAY?

In the latest Star Wars movie, the character of Rose Tico – a humble maintenance techie with a talent for tinkering is relatable; her role expands and responsibilities increase as the movie gets going, just like our data scientists. A chance encounter with Finn puts her into the frontlines of action, and by the end of the movie, she’s flying ski-speeders in the new galactic civil war, one of the most critical battles in the movie – with time, her role becomes more complex and demanding, but she never quivers and embraces the challenges to get the job done.

A lot many data scientists draw similarities with Rose’s character. In the last 5 years, the job role and responsibility of data analysts has undergone an unrecognizable change – as data proliferation is increasing in capacity and complexity, the responsibility is found shifting base from dedicated consultants to cross-functional, highly-skilled data teams, proficient enough in integrating skills together. Today’s data consultants need to complete tasks collaboratively to formulate trailblazing analysis that let businesses predict future success and growth pattern, effectively.

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Quite conventionally, the intense role of prediction falls on the sophisticated crop of data scientists, while business analysts are more oriented towards measuring churn. On the other hand, intricate tasks, like model construction or natural language processing are performed by an elite team of data professionals, armed with strong engineering expertise.

Said differently, the emergence of data manipulation languages, such as R and Python is surging – owing to their extensive usage and adaptability, businesses are biased towards implementing these languages for advanced analysis. Drawing inspiration from Rose’s character, each data scientist should adapt to newer technology and expectations, and enhance expertise and skills that’s needed for the new role.

However, acing the cutting edge programming languages and tools isn’t enough for the challenge – today, data teams need to visualize their results, like never before. The insights churned out of advanced machine learning are curated for consumption by business pioneers and operation teams. Thus, the results have to be crisp, clear and creatively presented. As a result, predictive tools are being combined with effective capability of Python and R with which analysts and stakeholders are quite familiar.

The whole big data industry is changing, and the demand for skilled big data analysts is sky-rocketing. In this tide of change, if you are not relying on advanced data analysis tools and predictive analytics, you are going to lag behind. Companies that analyze data, boost decision-making, and observe social media trends – changing with time – will have immense advantages over companies that don’t pay attention to these crucial parameters.

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No second thoughts, it’s an interesting time for data aspirants to make significant impacts in the whole data community and trigger fabulous business results. For professional training or to acquire new skills – drop by DexLab Analytics – their data Science Courses in Noida are outstanding.

The blog has been sourced from –  dataconomy.com/2018/02/whole-new-world-data-teams

 

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Estimator Procedure under Simple Random Sampling: EXPLAINED

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Estimator Procedure under Simple Random Sampling: EXPLAINED

In continuation with the previous introductory blog on sampling: An ABC Guide to Sampling Theory, we will take a closer look into the concept of the estimator procedure under Simple Random Sampling with the help of mathematical examples. It will help us understand the underlying phenomenon, the manner to be precise in which the estimator function of sampling works.

Simple random sampling (SRS) is a method of selecting a sample comprising ‘n’ number of sampling units out of the population of ‘N’ number of sampling units such that every sampling unit has an equal chance of being chosen.

The Estimator Procedure under Simple Random Sampling

The process of selection of a sample under SRS (Simple Random Sampling) is random. This means, each number of the population has an equal probability of getting selected, which makes each of the observation identical and independently distributed.

The statistic chosen by the investigation of estimation of random samples need to satisfy a set of certain properties given below:

  1. Unbiasedness
  2. Consistency
  3. Sufficiency
  4. Efficiency

As a matter of fact, investigation is always about coming up with an idea regarding the population parameters based on the sample observations. The best part would be to formulate an unbiased, consistent estimator, which is also efficient. Normally, a sample mean for a set of sample observations is considered to be a very desirable estimator to form ideas about population parameters.

In detail, let’s examine the relevance of each of the properties of an estimator:

Unbiasedness of an estimator

Take a look at the below examples to understand the very idea of unbiasedness.

Example 1:

Answer:-

According to the problem, we have

Adding (1) & (2), we get,

So, from (3), we get:-

 is called an unbiased estimators for .

Now, subtracting (2) & (1), we get –

Example 2:

Assume that an investigator draws a sample from this population using SRSWR. Then show that the sample mean is an unbiased estimator for the population mean.

Now, by specification we have:-

We are redefined to show that:-

L.H.S  :

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics Presents #BigDataIngestion

 

Data sampling is the key to business analytics and data science. On that note, DexLab Analytics offers state of the art Data Science Certification for all data enthusiasts. Recently, they have organized a new admission drive #BigDataIngestion offering exclusive 10% off on in-demand courses, including big data, machine learning and data science courses.

 

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Here’s How Technology Made Education More Enjoyable and Interactive

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Here’s How Technology Made Education More Enjoyable and Interactive

Technology is revamping education. The entire education system has undergone a massive change, thanks to technological advancement. The institutions are setting new goals and achieving their targets more effectively with the help of new tools and practices. These cutting edge methods not only enhances the learning approach, but also results in better interaction and fuller participation between teachers and students.

The tools of technology have turned students into active learners; they are now more engaged with their subjects. In fact, they even discover solutions to the problems on their own. The traditional lectures are now mixed with engaging illustrations and demonstrations, and classrooms are replaced with interactive sessions in which students and teachers both participate equally.

Let’s take a look at how technology has changed the classroom learning experience:

Online Classes

No longer, students have to sit through a classroom all day. If a student is interested in a particular course or subject, he or she can easily pursue degrees online without going anywhere. The internet has made interactions between students and teachers extremely easy. From the comfort of the home, anyone can learn anything.

DexLab Analytics offers Data Science Courses in Noida. Their online and classroom training is over the top.

Free educational resources found online

The internet is full of information. From a vast array of blogs, website content and applications, students as well as teachers can learn anything they desire to. Online study materials coupled with classroom learning help the students in strengthening their base on any subject as they get to learn concepts from different sources with examples and practice enough problems. This explains why students are so crazy for the internet!

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Webinars and video streaming

The facilitators and educationists are nowadays looking up to video streaming to communicate ideas and knowledge to the students. Videos are anytime more helpful than other digital communications; they help deliver the needful content, boosting the learning abilities among the learners, while making them understand the subject matter to the core. Webinars (seminars over the web) replaces classroom seminars; teachers look up to new methods of video conferencing for smoother interaction with the students.

Podcasts

Podcasts are digital audio files. Users can easily download them. They are available over the internet for a bare subscription fee. It’s no big deal to create podcasts. Teachers can easily create podcasts that syncs well with students’ demand, thus paving a way for them to learn more efficiently. In short, podcasts allow students a certain flexibility to learn from anywhere, anytime.

Laptops, smartphones and tablets

For a better learning experience overall, both students and teachers are looking forward to better software and technology facilities. A wide number of web and mobile applications are now available for students to explore the wide horizon of education. The conventional paper notes are now replaced with e-notes that are uploaded on the internet and can be accessible from anywhere. Laptops and tablets are also used to manage course materials, research, schedules and presentations.

No second thoughts, by integrating technology with classroom training, students and teachers have an entire world to themselves. Sans the geographical limitations, they can now explore the bounties of new learning methods that are more fun and highly interactive.

DexLab Analytics appreciates the power of technology, and in accordance, have curated state of the art Data Science Courses that can be accessed both online and offline for students’ benefit. Check out the courses NOW!

 

The article has been sourced from – http://www.iamwire.com/2017/08/technology-teaching-education/156418

 

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain.  The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

Let’s Take Your Data Dreams to the Next Level

At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

 

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