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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

Posted on May 10, 2018May 23, 2020 by Dexlab

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.

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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5 Best Data Science Resources to Ace the Game of Data

Posted on April 12, 2018May 19, 2020 by Dexlab

Wondering how a data scientist makes advances in his data career? Or how does he expand his skills in the future? Reading is the most common answer; nothing helps better than keeping a close eye on the industry news. Data science is evolving at a rapid speed; to be updated with the latest innovations and technology discoveries would be the best thing to stay ahead of the curve.

If you are a newbie in this field, make sure you are well-read about the current industry trends and articulate it well to the HR heads that you are someone who is always a step ahead to consume knowledge about data science and its related fields. This helps!

A wide number of data science blogs and articles are available over the internet, but with so many options, it’s easy to feel lost. For this and more, we have compiled a comprehensive list of 5 best data science blog recommendations that would help aspiring data scientists maneuver smoothly through this sphere.

Data Elixir

For a one stop destination for all things DATA, Data Elixir is the right choice. Crafted by ex-NASA data scientist Lon Riesberg, Data Elixir offers a list-wise view of the posts; easy categorization of content is anytime preferable and renders easy search options.

Data Science Weekly

The brain child of Hannah Brooks and Sebastian Gutierrez, Data Science Weekly is the ultimate hub for recent news, well-curated articles and promising jobs related to data science. You can either sign up for their newsletter or simply scroll through their archives dated back to 2013.

The Analytics Dispatch

The Analytics Dispatch is more like a newsletter content creating hub, wherein they send weekly emails about data science related stuff to its readers. Collected, analyzed and developed by a robust team at Mode Analytics, which also happens to be an Udacity partner, the newsletters focus on practical advices on data analysis and how data scientists should work.

O’Reilly Media’s data science blog

To read some of the most amazing articles on AI and data science, make O’Reilly Media’s data science blog your best companion. The articles are curated, researched and written by influencers and data science pundits, who are technically sound and understands the advanced nuances of the field in-depth.

Cloudera

Being top notch big data software, Cloudera’s contribution to the world of data science is immense. Time to time, it publishes interesting articles, know-hows and guides on a plethora of open source big data software, like Hadoop, Flume, Apache, Kafka, Zookeeper and more.

Besides, DexLab Analytics, a pioneering analytics training institute headquartered in Gurgaon, India also publishes technical articles, amazing blogs, riveting case studies and interviews with analytics leaders on myriad data science topics, including Apache Spark, Retail Analytics and Risk Modeling. The content is crisp, easy to understand and offers crucial insights on a gamut of topics: it helps the aspiring readers to broaden their horizons.

The realms of data science are fascinating and intimidating as well; but with the right knowledge partner, carry suave data skill in your sleeves – Data Science Courses in Noida from DexLab Analytics are the best in town! Also, their Business Analytics Training Courses in Noida are worth checking for.

Some of the parts of the blog have been sourced from – http://dataconomy.com/2018/01/5-awesome-data-science-subscriptions-keep-informed/ and https://www.springboard.com/blog/data-science-blogs

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Facial Recognition Technology: Where Opportunities are Endless and Science is Terrific

Posted on April 11, 2018April 11, 2018 by Dexlab

We are on the verge of the Fourth Industrial Revolution – where massive amounts of texts, tweets, photos, videos, status updates, GPS coordinates, reposts and clickstreams are being pumped out into the digital universe. This data is like the food for colossal artificial intelligence.

If we talk about resources, the ocean that AI-induced data has filled up is nothing if compared to California gold rush, Texas Oil boom or similar events. Huge amounts of data are clogging the digital space all over. Algorithms, based on AI are driving innovation in every field of work, right from products to services, and the more data you possess, the more accurate the algorithm is expected to be. As a result, collection and analysis of big data have become a prime focus of companies, big and small.

Introducing Deep Learning

But how does this mammoth AI works? How does it digest this amount of data? Of course through interconnected, high-end devices powered by embedding “eyes”, named as Deep Learning. These artificial neural networks work on the principle of machine learning algorithms and simulate the complex structure of human brains. Employing mammoth data pools and lakes, deep learning determines and interprets intricate patterns, just the way humans do. In fact, some of the artificial neural networks are so adept at incorporating these patterns that they can even mimic the manner in which humans recognize faces.

DeepFace: A Stiff Competitor of Human Brain

In terms of facial data, Facebook is the largest reservoir of facial data, and back in 2015, it came out with a cutting edge version of “tag photos” feature, DeepFace – it features a nine layer neural network that resembles characteristics in individual photographs with 97.25% accuracy. This fabulous technology not only connects your name with your face, but it can easily pick you out of a crowd, and the figure says a human brain is only 0.28% more effective than DeepFace.

Of late, Facebook has acquired a new patent, “Techniques for emotion detection and content delivery,” – it helps in capturing user’s facial expressions through the camera in real time while they scroll across their feed, recording their emotions for various content. This new-age technology can not only customize your Facebook feed, but can also link numerous live in-store cameras for a better shopping experience, piling up data from Facebook and determining the shopper’s present mood and preference.

Facebook and Beyond

Though Facebook is dominating the waters of facial recognition, there are several other companies that are trying their luck into this domain. Ebookers, a sub-site of Expedia has launched a tool named SenseSational, which employs real time facial recognition software to monitor users’ faces, while they peruse over images and sounds that appeal to the senses.

On the other hand, Singapore Technologies Electronics is using facial recognition technology to identify the faces of commuters, as they walk across fare gates and charges their prepaid account respectively. No longer the commuters have to show their fare card while standing in queue; thus it eases the crowd buildup during rush business hours.

In conclusion, companies can anytime look up to deep learning from any angle. The giant of artificial intelligence is forever hungry, you can feed it with data whenever you like, and see it expand and flourish.

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The original article first appeared on – https://www.entrepreneur.com/article/311228

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How India is driving towards Data Governance

Posted on March 27, 2018May 23, 2020 by Dexlab

Data is power – it’s the quintessential key to proper planning, governance, policy decisions and empowering communities. In the recent times, technological expansion is found to be contributing immensely towards ensuring a sustainable future and building promising IT base. Robust developments in IT related services have resulted into key breakthroughs, including Big Data, which as a result have triggered smooth data governance.

According to a NASSCOM report, India’s analytics market is expected to grow from $1 billion to $2.3 billion in the year 2017-18. However, the fuller benefits of data analytics are yet to be channelized by the public sector.

In a varied country like India, data collection is a lengthy procedure. At present, information is being collected by various government departments straight from Panchayat levels to state levels. Though, most of the data remains trapped within department walls, it is largely used to pan out performance reports. Also, certain issues in timely collection of data pops up, while sometimes the quality of data collected becomes questionable, hence delaying the entire analysis.

Quality data plays an integral role, if analyzed properly at the proper time. They can be crucial for decision-making, delivery of services and important policy revisions. As a matter of fact, last year, Comptroller and Auditor General (CAG) initiated Centre for Data Management and Analytics (CDMA) to combine and incorporate relevant data for the purpose of auditing. The main purpose here is to exploit the data available in government archives to build a more formidable and powerful Indian audit and accounts department.

Indian government is taking several steps to utilize the power of data – Digital India and Smart Cities initiatives aim to employ data for designing, planning, managing, implementing and governing programs for a better, digital India. Many experts are of the opinion that government reforms would best work if they are properly synchronized with data to determine the impact of services, take better decisions, boost monitoring programmes and improve system performances.

Open Data Policy is the need of the hour. Our government is working towards it, under the jurisdiction of the Department of Information and Technology (DIT) to boost the perks of sharing information across departments and ministries. Harnessing data eases out the load amongst the team members, while ensuring better accountability.

Tech startups and companies that probe into data and looks for solutions in data hoarding and analytics to collect and manage complicated data streams need to be supported. The government along with local players should encourage citizens to help them in collecting adequate information that could help them in long-run. India is walking towards a rapid economic development phase, where commitment towards information technology, data governance and open-source data is of prime importance. For the overall economy, bulk investments in capacity building, technology implementation and data-facilitating structures should be considered and implementable to bring plans and participation into place to hit off a better tech-inspired reality.

For data analyst certification in Delhi NCR, drop by DexLab Analytics – it’s a prime data science online training centre situated in the heart of Delhi.

The original article appeared on – https://economictimes.indiatimes.com/small-biz/security-tech/technology/indias-investment-in-big-data-will-ensure-governance/articleshow/57960046.cms

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New Intelligence is being added to Massive Storage Management System

Posted on March 26, 2018May 23, 2020 by Dexlab

Pioneers of High Performance Storage System (HPSS) are devising ways to streamline and rationalize data management products for its upcoming eighth generation. 25 years back, US Department of Energy research laboratories and IBM together built HPSS to support massive government science research projects. Why? The Hierarchical storage solution is undeniably a rewarding concept which uses organization policies and software automatic tricks to decide which data to save, the location where it should be saved, the best time to move it to different storage devices and when to delete it.

“How do you know what you’re archiving? We’re talking about archives now that are hundreds of petabytes to an exabyte. We think we’re going to be there in 2-3 years,” asked Todd Herr, a storage architect for supercomputing from Lawrence Livermore National Laboratory, CA.

The HPSS website catalogues 37 publicly disclosed customers, while other customers are kept discreet. At present, version 7.5.1 from last year is on the run, but version 7.5.2 might be hit, while the next year will see 7.5.3, as given in the online roadmap.

However, version 8 is not yet available on the official roadmap, but here’s what the insiders have to say about it…

“What I think our challenge is, is to become good data curators. And I think that’s where we’re going to point the product,” Herr shared. This will turn HPSS become more capable for data mining and assign metadata to itself.

In order to do that, the first thing to be done is to reveal information in the archive about a few overarching namespace applications. Herr explained, “Right now we are working on that (referring to software made by companies such as Atempo, Robinhood, Starfish, and StrongLink). I think the next step there is scaling out metadata performance, such as database partitioning and virtualizing multiple processors when performing searches.”

Another important part of HPSS is related to the software that works with tape storage – “What we’re trying to do is enable fast access to tape. If you look across the industry spectrum, the words fast and tape generally don’t go together,” Herr intimidated. The scientists at Livermore are capable of accessing research data on tape, even that existed more than 50 years ago.

Speed-matching buffers can save the day – when placed between primary disk storage and archive tape storage, they can be used to both read and write. Some other physical improvements include faster head placements and tape motors.

“We’re going to hit a problem way faster than most sites, and certainly faster than the vendors themselves because they cannot replicate our environment in most testing,” Herr asserted.

Herr’s employer’s next supercomputer, Sierra is going to operate at up to 125 petaflops and will have a 125-petabyte file system for performing ample tests to find new ways of speeding up performance and administer advanced data storage mechanisms.

The article has been sourced from – https://www.techrepublic.com/article/fed-and-ibm-researchers-adding-new-intelligence-to-massive-storage-management-system

For more such interesting ideas and discussions, stay tuned to DexLab Analytics. It is a premier analytics training institute headquartered in Delhi, NCR. Their data science certification courses are excellent.

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How This Bengaluru Startup Is Using AI to Detect Early Stage Breast Cancer in Women

Posted on March 20, 2018May 23, 2020 by Dexlab

The World Health Organization says – one out of two women diagnosed with breast cancer dies within five years in India. In the US, the fatality is less than one out of five, and in China, one out of four. Shortages of technology for early detection and radiographers coupled with the expense of regular screening, which normal people find too expensive to afford in India have led to an increasing number of breast cancer cases of late. Today, breast cancer has outstripped cervical cancer as the major cause of cancer death among women in this country.

A Bengaluru-based tech startup and the brainchild of Geetha Manjunatha (CEO) and Nidhi Mathur (COO), NIRAMAI offers breast cancer screening solution by combining artificial intelligence, machine learning and cloud. It aims at tackling the issue of accessibility and expenses of breast cancer screening. These two dynamic women had seen cancer very closely in their family and feel an emotional connect with anyone who is diagnosed with this deadly disease. This led to the conceptualization of NIRAMAI, which means BEING WITHOUT DISEASES in Sanskrit. Also, it’s an acronym for “Non-Invasive Risk Assessment through MAchine Intelligence”.

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The Working Principle

The breast cancer screening solution by NIRAMAI is non-invasive, non-contact and non-radiation process of detecting early stage breast cancer amongst women of all ages. The deep technology that it claims to have patented is Thermalytix technology – a fusion of top-grade machine learning algorithms over thermal images.

“Thermography is well known to sense earliest signs of cancer. However, traditional manual interpretation of a thermogram has not been accurate enough to become accepted as a standard of care. Interpreting 400000 colour values in thermograms and to diagnose breast abnormality is a huge cognitive overload to a radiologist – use of machine learning enables automated analysis and helps in better interpretation of thermal images and considerably improves the overall accuracy of diagnosis”, says Geetha, one of the cofounders of NIRAMAI.

The working mechanism of screening in NIRAMAI is quite simple, and effective. The women who want to get screened need to relax for the first 10 minutes before taking up the test. Then a high resolution thermal sensor is kept at a distance of 3 feet from her to measure the temperature distribution on her chest and generate thermal images. Next, the NIRAMAI software scans these thermal images to automatically initiate a screening/diagnostic report and hands over a radiologist-certified report to the women. The test is performed in a highly intimate manner, the women undertaking the screening is neither touched nor seen by anyone.

“This is unlike mammography which is based on X-Ray and is recommended for women above 45 years only once in 2 years. It is also noncontact and doesn’t require any breast compression; hence not painful. Since the equipment is very portable, it is amenable to be used in outreach programs being a rural camp or urban corporate screening,” she shares.

Overcoming challenges

In healthcare space, analytics and AI are dubious topics. It takes a lot to coax a doctor to use an AI tool as an aid in his diagnostic procedure – countless discussions, several experimental trials and after a lot of effort, NIRAMAI could finally step into and create a niche of their own.

Another challenge was to have an edge over their competitors, who once knew that they are out with a revolutionizing technology, would like to sell everything to copy that. For that, they have armed themselves with 10 patents in this area, which is somewhat protecting them from other players.

Since breast cancer is a big health issue in India, the NIRAMAI team feels that it is extremely important for women to go for regular screening. It is safe and in most cases, early detection helps keep cancer at bay.

The power of analytics is huge. Arm yourself with a powerful data analyst certification Delhi NCR. It will help you go a long way!

Some parts in this blog have been sourced from:

https://analyticsindiamag.com/this-women-led-startup-is-using-ai-thermal-imaging-to-detect-breast-cancer

https://www.techinasia.com/startup-patented-ai-tech-breast-cancer-screening

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Data Analytics: The Key to Track and Curb Leakages in GST

Posted on March 19, 2018May 23, 2020 by Dexlab

Though our country may have got a One Nation, One Tax Policy, in the face of GST, its revenue collection figures are not so encouraging. In the beginning, GST revenue collection for the first three months went over 90000 crore, but the figures started dropping from October to 83346. And in November, it further slipped to 80808 crore. Since then, the figures mostly lingered around 86000 in the recent months.

The Union Ministry of Finance had to figure out the reason of this discrepancy, the reason behind such huge revenue leakage in GST collection before it’s too late, and for that data analytics came to the rescue. After carrying out a thorough analysis, on its 26^th meeting on Saturday, GST Council discovered several major data gaps between the self-declared liability in FORM GSTR-1 and FORM GSTR-3B.

Highlighting the outcome of basic data analysis, the GST Council stated that the GST Network (GSTN) and the Central Board of Excise and Customs have found some inconsistency between the amount of Integrated GST (IGST) and Compensation cess paid by importers at customs ports and input tax credit of the same claimed in GSTR-3B.

“Data analytics and better administration controls can help solve GST collection challenges” – said Pratik Jain, a national leader and partner, Indirect Tax at PricewaterhouseCoopers (PwC).

He added, “Government has a lot of data now. They can use the data analytics to find out what the problem areas are, and then try and resolve that.” He also said that to stop the leakage, the government need to be a lot more vigilant and practice better controls over the administration.

Moreover, of late a parliamentary committee has found that the monthly collection from GST is not up to the mark due to constant revisions of the rates, which has undoubtedly affected the stability of the tax structure and had led to an adverse impact for trade and business verticals.

“The Committee is constrained to observe the not-so-encouraging monthly revenue collections from GST, which still have not stabilised with frequent changes in rates and issue of notifications every now and then. Further, the Committee is surprised to learn that no GST revenue targets have been fixed by the government,” said M Veerappa Moily, the head of Standing Committee on Finance and a veteran Congress leader in a recent report presented in the Parliament.

The original article appeared in – analyticsindiamag.com/government-using-data-analytics-to-track-leakages-in-gst/

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Big Data, Hadoop and Cloud: The Looming Challenges and How to Peg Them?

Posted on February 27, 2018September 24, 2018 by Dexlab

Data is regarded as the “new oil” in the industry – though you can’t fill your car’s gas tank with binary digits, but yes, you can definitely think of driving an autonomous car with data. Self-driven cars are a reality now!

About 10 years ago, with the advent of big data hype, organizations, big and small joined the bandwagon involving data so as not to miss out the ‘next big thing’. The whole thing started with the ‘data land grab’ phase. Next came the delineation phase, in which industry started chalking out clearly big data boundaries and where it has to be applied. After this, we have moved into an efficiency phase – whereby we extract the maximum out of data by merging right expertise with the right technology.

Notwithstanding all the exciting stuffs surrounding big data, many challenges have even come out during the delineation phase and they still continue to cripple company functioning. So, here we will talk about the challenges faced and ways to tackle them…

Big Data Challenges

Now, it’s the time for humongous volumes of unstructured data – companies have as a result shifted their focus from traditional big data storage solutions to more agile, cost-efficient open source strategies like Spark and Hadoop. Navigating through a turbulent sea of big data tools is another daunting task in itself, so here we will address the issue of Hadoop challenges only.

Though Hadoop has solved a multitude of data problems, yet its implementation and management is a difficult task, and ends up causing more problems than doing good. Also, scaling Hadoop on premise is a taxing procedure, involving a lot more investment in physical infrastructure – for this, many companies are turning towards cloud-based Hadoop solutions because they are agile and less complicated to use.

Cloud Migration Challenges

Cloud-based solutions help companies maneuver in a more agile manner, while enhancing their data needs. This acts as a robust solution to the issue of adding more on-prem infrastructure over time, but as it’s said, there’s no gain without pain – migrating data analytics to a purely cloud infrastructure has its own cons.

The biggest challenges associated with cloud network are related to reliability, performance, scalability and accessibility of data. Data security also remains a matter of concern – a handful number of recent high-profile data breaches have made us vulnerable, while showing on our face how less protected we are in the digital world.

How To Tackle the Emerging Challenges?

Think beyond today! Companies need to make their headstrong big data solutions future proofed, because no one likes to do the same thing again and again in a time span of two-three years. If you are incorporating steady solutions today, make sure they stay in practice for the coming 5-10 years or so.

As we have mentioned earlier, Hadoop implementation and management is not as easy as it sounds, and gaining access to a deft pool of experts who understands the intricacies of Hadoop has become the need of the hour. This means, make sure you choose the right internal talent pool and work with uber talented experts.

Now, when it comes to ensuring data security over cloud infrastructure, make sure you think beyond the perimeter security, focus on identifying sensitive data, both structured and unstructured and then secure it in a Hadoop lake just the way it’s ingested. This will help you closely monitor cloud data sources and check violations right from the start.

Join DexLab Analytics data analyst certification and stand a chance of making a successful career as a data scientist. After all, enrolling in India’s best data analyst training institute in Delhi NCR will surely help you master the art of data science.

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3 Ways to Increase ROI with Data Science

Posted on February 26, 2018May 23, 2020 by Dexlab

In 2018, companies have decided to invest $3.7 trillion on machine learning and digital transformation so as to embrace a promising return on that sizeable investment for professionals involved in managerial roles. Nevertheless, 31% of the companies using the potent tools of machine learning and data science are not yet tracking their ROI or are in no mood to do so in the near future.

But to be on the side, ROI is very crucial for any business success – if you fail to see the ROI you expect from data science implementation, look into bigger and complex processes at work – and adjust likewise.

Take cues from these 3 ways, explained below:

Implementing data science strategy into C-Suite

According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost nonexistent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite basically gets its name from a series of titles of top level executives who job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data and unveil new trends and opportunities that the company has been attempted to tab for years.

The core responsibility of a CDO is to address a proper data management strategy and then decode it into simple, implementable steps for business operations. Its prime time to integrate data science into bigger processes of business, and soon company heads are realizing this fact and working towards it.

Your time and resources are valuable, don’t waste them

Before formulating any strategy, CDOs need to ensure the pool of professionals working with data have proper access to the desired data tools and support or not. One common problem that persists is that the data science work that takes place within an organization is done on silo, and therefore remains lost or underutilized. This needs to be worked out.

Also, besides giving special attention on transparency, data science software platforms are working towards standardizing data scientists’ efforts by limiting their resources for a given project, thereby ensuing cost savings. In this era of digitization, once you start managing your data science teams efficiently, half the battle is won then and there.

Stay committed to success

Implementing a sophisticated data science model into production process can be a challenging, lengthy and expensive process. Any kind of big, complicated project will take years to get completed but once they do, you expect to see the ROI you desire from data science but the journey might not be all doodle. It will have its own ups and downs, but if you stay committed and deploy the right tools of technology, better outcome is meant to happen.

In a nutshell, boosting of ROI is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of your data science strategy, which will help in predicting success accurately and thus help taking ROI-supported decisions.

If you are looking for a good data analyst training institute in Delhi NCR, end your search with DexLab Analytics. Their data analyst certification is student-friendly and right on the point.

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