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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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Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

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Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

Is crunching numbers your favorite hobby?

Are you interested in deciphering how many people use smartphones, regularly?

Do you feel fascinated by the way businesses use data to frame decisions?

If yes, then you are at the right place – a career, where you could leverage this inquisitiveness and knack for numbers is just carved for you. Not necessarily it has to be data science career option, but we’ve charted down top 5 career choices for the data curious you!

Data Scientist

Tagged as the sexiest job of 21st century, data scientist jobs are irresistible. First of all, the field of data science is expanding steadfastly – IBM prediction says the demand for data scientists will increase by 28% by the end of 2020. This brings good news for job seekers, who are on toes to enter the fascinating world of data science, where the salaries are pumping up – already they have touched six figures.

The main objective of data scientists is to collect meaningful data to help businesses formulate strategic decisions. Cleaning up and structuring the data is of primary importance – followed by cutting edge tool implementation, such as algorithms, statistical models and deep learning structures – all of them aids in extracting insights out of relevant data.

Statistician

Other than data geeks, very few love the very idea of becoming a statistician. But for guys who love churning data, the role of statistician is the most fascinating in the world. They help solve the toughest problem with data, while finding and providing answers to crucial questions.

Statisticians’ aptitude for numbers knows no bounds – and the range of projects on which they work is diverse. From ascertaining unemployment rates to nabbing the discerning the effectiveness of prescription drugs to calculating the number of endangered animals living in a given area – from designing the strategies for data collection to nabbing the latest trends, statisticians need to juggle between a lot of tasks, and solve crucial problems.

Computer Scientist

The computers are lifeline of today’s businesses – so jobs related to computing power is selling like hot cakes. The field of computer science is encompassing – nerds in love with data can discover a treasure trove of career options under this umbrella term. If you are a true blue crime buff, choose computer forensics as your leading career option. Or else, are you a major computer game aficionado? Then aspire to become a game developer or architect.

 Today, software developers and architects are witnessing surging demand, and most of the jobs in this technology domain help draw salaries over $100000 annually. So, what you waiting for?!

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Database Administrator

Data is next to oil; of late, it’s been treated as a valuable resource. Thus, we should look for ways to keep it safe and well-protected. Database administrators are ideal for this defensive job. They not only toil to set up fortified databases but also are responsible for maintenance, model up-keeping and implementing security measures. Undeniably, it’s one of the most challenging jobs in the world of data but at the same time, it’s also the most rewarding one – at present, it ranks as the world’s #7 best technology job, according to a notable US tabloid.

Done reading? Now, data-lovers, when are you taking the next step to turn your avocation into your vocation? Pretty soon, right!

Quick Note: DexLab Analytics is offering state of the art Data Science Courses at affordable prices. For more details on Data Science Certification, visit the official page today.

 

The blog has been sourced from – dataconomy.com/2018/06/five-careers-to-consider-for-data-enthusiasts

 

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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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An ABC Guide to Sampling Theory

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An ABC Guide to Sampling Theory

Sampling theory is a study involving collection, analysis and interpretation of data accumulated from random samples of a population. It’s a separate branch of statistics that observes the relationship existing between a population and samples drawn from the population.

In simple terms, sampling means the procedure of drawing a sample out of a population. It aids us to draw a conclusion about the characteristics of the population after carefully studying only the objects present in the sample.

Here we’ve whisked out a few sampling-related terms and their definitions that would help you understand the nuanced notion of sampling better. Let’s have a look:

Sample – It’s the finite representative subset of a population. It’s chosen from a population with an aim to scrutiny its properties and principles.

Population – When a statistical investigation focuses on the study of numerous characteristics involving items on individuals associated with a particular group, this group under study is known as the population or the universe. A group containing a finite number of objects is known as finite population, while a group with infinite or large number of objects is called infinite population.

Population parameter – It’s an obscure numerical factor of the population. It’s no brainer that the primary objective of a survey is to find the values of different measures of population distribution; and the parameters are nothing but a functional variant inclusive of all population units.

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Estimator – Calculated based on sample values, an estimator is a functional measure.

Sampling fluctuation of an estimator – When you draw a particular sample from a given population, it contains different set of population members. As a result, the value of the estimator varies from one sample to another. This difference in values of the estimator is known as the sampling fluctuations of an estimator.

Next, we would like to discuss about the types of sampling:

There are mainly two types of random sampling, and they are as follows:

Simple Random Sampling with Replacement

In the first case, the ‘n’ units of the sample are drawn from the population in such a way that at each drawing, each of the ‘n’ numbers of the population gets the same probability 1⁄N of being selected. Hence, this methods is called the simple random sampling with replacement, clearly, the same unit of population may occur more than once inj a simple. Hence, there are N^n samples, regard being to the orders in which ‘n’ sample unit occur and each such sample has the probability 1/N^n .

Simple Random Sampling Without Replacement

In the second case each of the ‘n’ members of the sample are drawn one by one but the members once drawn are not returned back to the population and at each stage remaining amount of the population is given the same probability of being includes in the sample. This method of drawing the sample is called SRSWOR therefore under SRSWOR at any r^th number of draw there remains (N-r+1) units. And each unit has the probability of 1/((N-r+1) ) of being drawn.

Remember, if we take ‘n’ individuals at once from a given population giving equal probability to each of the observations, then the total number of possible example in (_n^N)C i.e.., combination of ‘n’ members out of ‘N’ numbers of the population will from the total no. of possible sample in SRSWOR.

The world of statistics is huge and intensively challenging. And so is sampling theory.

But, fret now. Our data science courses in Noida will help you understand the nuances of this branch of statistics. For more, visit our official site.  

P.S: This is our first blog of the series ‘sampling theory’. The rest will follow soon. Stay tuned.

 

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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.

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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.

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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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How American Express Uses Data Analytics to Promote a Data-Driven Culture

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data analytics training institute

Since 2010, American Express, with an encompassing database crossing over 100 million credit cards accounting for more than $ 1 trillion in charge volume annually, is harnessing the power of big data. Undeniably, it resulted in incredible improvements in speed and performance.

In the last four decades, the entire financial services industry has undergone a massive change, notably in the spheres of:

Electronic payments – Online payments, comprising credit and debit cards have dramatically increased over cash, globally.

E-commerce – An excessive reliance on smartphones and internet have boosted E-commerce capabilities manifold times.

With an increasing interaction between company and customers, the latter’s online and offline identity is being collaborated for an encompassing 360-degree view. This eventually drives innovation in product designing and marketing.

Formulating a Data-Driven Culture

Data analytics is like the bull’s eye of effective marketing, and servicing and risk management. Data curation and management is now a prerequisite for competitive excellence.

Since its inception, American Express flaunts transformation: the company has transformed itself from being a trivial freight forwarding business to a top notch player in payments and customized service industry. Over the years, the working mechanism of the firm has changed dramatically, and today, it is #1 small business card issuer in the whole of the US.

No matter, while the company strives to evolve, its core values remain somewhat same. Keeping their customers above anything else and behave like a good citizen are two core values of American Express that are beyond alterations. To become a successful data-driven organization, they believe in investing on technology, analytics, along with human talent, emphasizing on a proper synthesis between technology and human cognition to trigger robust growth and future success.

How American Express Stays Relevant and Fresh?

Risk 2020 – American Express envisions how an economy or marketplace might look like after a few years, and in the process, assesses the risks to combat to address the weaker issues in the economy. A comprehensive approach, including cloud, deep learning, mobile computing and AI is the solution.

Cornerstone – This is an encompassing, global big data ecosystem. The data is stored and shared with global potentialities across trusted sources. In any organization, data is the centre of attraction, and the consultants at American Express recognize the essence of innovation lies at company’s DNA and not somewhere on the top.

The data-driven culture in American Express is simple, natural and nuanced. A huge data base is created, from acquisition to customer management, which eventually needs to be shared with third parties and partners to derive insightful conclusions for better customer experience and risk assessment. “At American Express, we take our responsibility to serve customers and the public seriously, always ensuring that solutions are best-in-class and valuable to our customers,” says Ash Gupta, president, Global Credit Risk & Information Management, American Express.

“American Express’ closed-loop data allows us to analyze a large volume of real spending that can help marketers across a range of industries connect with customers and provide unique value,” he further adds.

Data Science Machine Learning Certification

To know more about data-driven customer experience, visit DexLab Analytics, a premier data analyst training institute in Delhi. They offer a plethora of data analyst training courses for interested candidates.

 

The blog has been sourced from:

https://www.forbes.com/sites/ciocentral/2018/03/15/how-american-express-excels-as-a-data-driven-culture/#5c5ed1a81635

https://digit.hbs.org/submission/american-express-using-data-analytics-to-redefine-traditional-banking/

 

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5 Best Data Science Resources to Ace the Game of Data

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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.

5 Best Data Science Resources to Ace the Game of Data

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.

Let’s Take Your Data Dreams to the Next Level

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

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

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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