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The Glassdoor Report last year (Glassdoor’s 50 Best Jobs In America For 2018) named data scientist as the best job in the US for three years running.

The report took into consideration three key factors, namely job satisfaction rating, median annual base salary, and the number of job openings. Each of these three factors was given equal importance, and it was found that data science jobs excelled across all three.

Apart from $110,000 as a median base salary, data science jobs were found to have a job satisfaction score and a job score of 4.4 and 4.8 (out of 5) respectively.

Similar findings were made public in a related report of where jobs in data science were shown to have one of the best growth rates in the industry over the next decade and continue to be one of the most difficult positions to be filled.

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According to statistics, these findings were supported and it was stated that over the past four years, merely 50% of the projected 19,500 data scientist positions got filled. All these statistics and predictions indicate how popular the job of a data scientist is and will become in the coming days.

Let’s take a deeper look into certain aspects to understand the driving factors behind this trend that makes data scientist the hottest job of the 21st century.

IoT is transforming how we interact with the digital world of data

A research conducted by Business Insider some time ago predicted that by 2020, over 24 billion internet-connected devices will get installed globally. In other words, every person on this planet will have more than four devices to use. Together, these devices comprise the Internet of Things (IoT), and its presence is permanently changing our world.

IoT can be called the link between the digital world of data and physical world inhabited by humans. From your smartphones and smartwatches, to tablets, computers, smart TVs, and wearables — all come under the IoT.

What’s more, even your everyday appliances like lights, fans, smoke detectors, and thermostats have started boasting of smart capabilities, which make them a part of the IoT. Even how you socialize, or get from one place to other (via the transportation system) is changing and will change further because of the IoT. The tech giant has been enhanced by the time over.

Why do you need a data scientist?

If you are wondering how the IoT is connected to data, here’s the answer: all these varied smart devices and appliances draw a large amount of data. A number of sources are used to collect this data, which can be categorized into two types: unstructured data and structured data, both of which come under the domain of big data.

Human input is more likely to contribute to unstructured data, which is the fastest growing type of big data.

This includes your social media posts, the emails you send and receive from various sources, the videos your stream or share, the customer reviews you post etc. since unstructured data isn’t streamlined, it’s difficult to sort and manage with technology. On the other hand, structured data is collected by products, services, and electronic devices.

For example, your website traffic data, or GPS coordinates collected by your smartphone fall under this category. Since such data is organized, usually by categories, a computer or a program can be used to read, sort, and organize it automatically because of demand in data.

A data scientist works with both structured and unstructured data, and sorts, organizes and analyses them to present them in easily understandable forms to the stakeholders. This in turn would help the stakeholders examine if their departmental, business and revenue goals are being met, and also help them take important business decisions.

In other words, a data scientist’s job isn’t just to process and analyze data.

Rather, he/she should be able to translate departmental or company goals into data-based deliverables like pattern detection analysis, prediction engines, optimization algorithms etc, which would offer the stakeholders useful insights and facilitate informed decision making.

How to Become a Data Scientist? - Magnimind Academy

I wrote about that here.

Not having a data scientist on your team would mean that even if you sit on a pile of data, you won’t be able to leverage it for your benefit as you can’t get any meaningful insights or use them to predict trends (like the surge in interest in a particular item), which would have helped you to make timely business decisions.

In today’s competitive business landscape where data never stops flowing and the nature of challenges undergoes a continuous change, it’s the data scientists who can help decision makers make a transition from ad hoc analysis to enjoying an ongoing dialogue with data.

Now that you have an idea of the role of a data scientist, let’s see what makes it the hottest job of the 21st century.

Top 3 reasons that make the position of a data scientist the most coveted job

1- Rising demand for skilled professionals

Lack of qualified talent is one of the key reasons why data scientist jobs are in high demand.

Even for the positions that are vacant at present, employers are finding it difficult to fill them as there aren’t enough skilled and qualified people around. The problem is that though companies need more data scientists, most are still not certified yet or are still studying for their degrees.

And this gap between demand and the availability of talents is set to worsen since IBM has predicted that by 2020, the demand for data scientists will skyrocket to 28%.

This sets the prefect stage for aspirants seeking jobs as data scientists. Thanks to the huge vacancy in this field (which is set to increase further in the future), these aspirants can apply for and land such in-demand jobs a lot faster than their counterparts seeking other jobs. Thus, companies want skilled person who is worker.

2- You earn well and get to work with the latest technologies

As already mentioned before, the median salary of a data scientist in the US is close to $110,000. Elsewhere in the world too, the job pays extremely well.

According to Burtch Works Study: Salaries of Data Scientists, the base salary of these professionals is up to 36% higher than their counterparts working with other predictive analytics.

As the demand for competent data scientists is set to grow significantly, the salary for the post is likely to become better.

Apart from the lure of a fat paycheck, the excitement of working with the latest technologies is also a big draw.

From Artificial Intelligenceand Machine Learning (with progressive future prospects) to R and Python (considered as the most popular technologies), and MongoDB (the most popular database), a data scientist gets to work with the constant evolution of technologies.

This first-hand experience together with the future prospects of these popular technologies make the position of data scientists the most coveted one.

3- Companies in different domains and of different sizes are hiring

Once, data scientists were thought to be employable only in the IT and finance sectors, and that too in large companies. But the scenario has changed today. While the bigger names in IT, Finance and Insurance sectors continue to hire these professionals in career stage, even the medium and smaller companies are now hiring them as they have realized the importance of data-driven decision making.

Though these smaller companies don’t have a data bandwidth as large as their bigger counterparts, they have started hiring qualified data scientists, who can help them get valuable insights from their metrics. With this, these smaller and medium companies can get a comparable “big data” advantage as the larger companies, which in turn would help them stay competitive.

And the good news is that it’s no longer just the IT, Professional Services, and Finance and Insurance that offer jobs to data scientists.

From companies in telecom, e-commerce, and BFSI (banking, financial services and insurance), to transportation and more, a lot of industries that generate or have access to a massive amount of data have woken up to the potential of leveraging such data to their business advantage.

And they are now hiring data scientists to process this huge amount of data to make the most of their business decision-making potential.

Be it proactive (where you anticipate what the problem could be and try to address it before it disrupts business operations) or preventive decision making, data science professionals can help. Even spotting trends to decide on the future course of business, or steering the business to an entirely new direction (in line with changing demands, preferences etc) becomes easy with the insights generated by data science.

Automating many small decisions is another key thing which can be done easily when the right data is collected and utilized.

For example, financial institutions using automated credit scoring systems to forecast their customers ‘credit-worthiness’ would not only free their employees from the task, but also bring a higher degree of accuracy, while speeding up the process and lowering the risk of not getting a return on the loans in case the customer wasn’t worthy of being granted a loan.

As the future scope of data science is extremely bright, it’s no wonder why there’s almost a mad rush to get qualified as a data scientist and find jobs in this highly lucrative domain withdata science in 6 weeks in Silicon Valley. Data science bootcamp in Bay Area provides an advantage to be accepted for a job.[data-twttr-rendered="true"] background-color: transparent;.twitter-tweet margin: auto !important;function notifyResize(height) height = height ? height : document.documentElement.offsetHeight; var resized = false; if (window.donkey && donkey.resize) donkey.resize(height); resized = true;if (parent && parent._resizeIframe) var obj = iframe: window.frameElement, height: height; parent._resizeIframe(obj); resized = true;if (window.location && window.location.hash === "#amp=1" && window.parent && window.parent.postMessage) window.parent.postMessage(sentinel: "amp", type: "embed-size", height: height, "*");if (window.webkit && window.webkit.messageHandlers && window.webkit.messageHandlers.resize) window.webkit.messageHandlers.resize.postMessage(height); resized = true;return resized;'rendered', function (event) notifyResize(););'resize', function (event) notifyResize(););if (parent && parent._resizeIframe) var maxWidth = parseInt(window.frameElement.getAttribute("width")); if ( 500 < maxWidth) window.frameElement.setAttribute("width", "500");

If you liked this article, I think you might be interested in this one as well…

5 Reasons to Move to Silicon Valley for a Data Science Job - Magnimind Academy

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The Hottest Job Of The 21st Century(Data Scientist!) was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

As the biggest sales and marketing technology firms mature, they are all turning to AI and machine learning to advance the field. This morning it was Oracle’s turn, announcing several AI-fueled features for its suite of sales tools.

Rob Tarkoff, who had previous stints at EMC, Adobe and Lithium, and is now EVP of Oracle CX Cloud says that the company has found ways to increase efficiency in the sales and marketing process by using artificial intelligence to speed up previously manual workflows, while taking advantage of all the data that is part of modern sales and marketing.

For starters, the company wants to help managers and salespeople understand the market better to identify the best prospects in the pipeline. To that end, Oracle is announcing integration with DataFox, the company it purchased last fall. The acquisition gave Oracle the ability to integrate highly detailed company profiles into their Customer Experience Cloud, including information such as SEC filings, job postings, news stories and other data about the company.

DataFox company profile. Screenshot: Oracle

“One of the things that DataFox helps you you do better is machine learning-driven sales planning, so you can take sales and account data and optimize territory assignments,” he explained.

The company also announced an AI sales planning tool. Tarkoff says that Oracle created this tool in conjunction with its ERP team. The goal is to use machine learning to help finance make more accurate performance predictions based on internal data.

“It’s really a competitor to companies like Anaplan, where we are now in the business of helping sales leaders optimize planning and forecasting, using predictive models to identify better future trends,” Tarkoff said.

Sales forecasting tool. Screenshot: Oracle

The final tool is really about increasing sales productivity by giving salespeople a virtual assistant. In this case, it’s a chatbot that can help handle tasks like scheduling meetings and offering task reminders to busy sales people, while allowing them to use their voices to enter information about calls and tasks. “We’ve invested a lot in chatbot technology, and a lot in algorithms to help our bots with specific dialogues that have sales- and marketing-industry specific schema and a lot of things that help optimize the automation in a rep’s experience working with sales planning tools,” Tarkoff said.

Brent Leary, principal at CRM Essentials, says that this kind of voice-driven assistant could make it easier to use CRM tools. “The Smarter Sales Assistant has the potential to not only improve the usability of the application, but by letting users interact with the system with their voice it should increase system usage,” he said.

All of these enhancements are designed to increase the level of automation and help sales teams run more efficiently with the ultimate goal of using data to more sales and making better use of sales personnel. They are hardly alone in this goal as competitors like Salesforce, Adobe and Microsoft are bringing a similar level of automation to their sales and marketing tools

The sales forecasting tool and the sales assistant are generally available starting today. The DataFox integration will GA in June.

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No matter what companies say, AI is not going to solve the problem of content moderation online. It’s a promise we’ve heard many times before, particularly from Facebook CEO Mark Zuckerberg, but experts say the technology is just not there — and, in fact, may never be.

Most social networks keep unwanted content off their platforms using a combination of automated filtering and human moderators. As The Verge revealed in a recent investigation, human moderators often work in highly stressful conditions. Employees have to click through hundreds of items of flagged content every day — everything from murder to sexual abuse — and then decide whether or not it violates a platform’s rules, often working on tightly-controlled schedules and without adequate training or support.

When presented with the misery their platforms are creating (as well as other moderation-adjacent problems, like perceived bias) companies often say more technology is the solution. During his hearings in front of congress last year, for example, Zuckerberg cited artificial intelligence more than 30 times as the answer to this and other issues.

“AI is Zuckerberg’s MacGuffin,” James Grimmelmann, a law professor at Cornell Tech, told The Washington Post at the time. “It won’t solve Facebook’s problems, but it will solve Zuckerberg’s: getting someone else to take responsibility.”

So what is AI doing for Facebook and other platforms right now, and why can’t it do more?

The problem of automating human culture

Right now, automated systems using AI and machine learning are certainly doing quite a bit to help with moderation. They act as triage systems, for example, pushing suspect content to human moderators, and are able to weed out some unwanted stuff on their own.

But the way they do so is relatively simple. Either by using visual recognition to identify a broad category of content (like “human nudity” or “guns”), which is prone to mistakes; or by matching content to an index of banned items, which requires humans to create said index in the first place.

The latter approach is used to get rid of the most obvious infringing material; things like propaganda videos from terrorist organizations, child abuse material, and copyrighted content. In each case, content is identified by humans and “hashed,” meaning it’s turned into a unique string of numbers that’s quicker to process. The technology is broadly reliable, but it can still lead to problems. YouTube’s ContentID system, for example, has flagged uploads like white noise and bird song as copyright infringement in the past.

 Image: Facebook
AI systems are being trained to parse new sorts of images, like memes.

Things become much trickier when the content itself can’t be easily classified even by humans. This can include content that algorithms certainly recognize, but that has many shades of meaning (like nudity — does breast-feeding count?) or that are very context-dependent, like harassment, fake news, misinformation, and so on. None of these categories have simple definitions, and for each of them there are edge-cases with no objective status, examples where someone’s background, personal ethos, or simply their mood on any given day might make the difference between one definition and another.

The problem with trying to get machines to understand this sort of content, says Robyn Caplan, an affiliate researcher at the nonprofit Data & Society, is that it is essentially asking them to understand human culture — a phenomenon too fluid and subtle to be described in simple, machine-readable rules.

“[This content] tends to involve context that is specific to the speaker,” Caplan tells The Verge. “That means things like power dynamics, race relations, political dynamics, economic dynamics.” Since these platforms operate globally, varying cultural norms need to be taken into account too, she says, as well as different legal regimes.

One way to know whether content will be difficult to classify, says Eric Goldman, a professor of law at Santa Clara University, is to ask whether or not understanding it requires “extrinsic information” — that is, information outside the image, video, audio, or text.

“For example, filters are not good at figuring out hate speech, parody, or news reporting of controversial events because so much of the determination depends on cultural context and other extrinsic information,” Goldman tells The Verge. “Similarly, filters aren’t good at determining when a content republication is fair use under US copyright law because the determination depends on extrinsic information such as market dynamics, the original source material, and the uploader’s other activities.”

How far can we push AI systems?

But AI as a field is moving very swiftly. So will future algorithms be able to reliably classify this sort of content in the future? Goldman and Caplan are skeptical.

AI will get better at understanding context, says Goldman, but it’s not evident that AI will soon be able to do so better than a human. “AI will not replace [...] human reviewers for the foreseeable future,” he says.

Caplan agrees, and points out that as long as humans argue about how to classify this sort of material, what chance do machines have? “There is just no easy solution,” she says. ”We’re going to keep seeing problems.”

It’s worth noting, though, that AI isn’t completely hopeless. Advances in deep learning recently have greatly increased the speed and competency with which computers classify information in images, video, and text. Arun Gandhi, who works for NanoNets, a company that sells AI moderation tools to online businesses, says this shouldn’t be discounted.

“A lot of the focus is on how traumatic or disturbing the job of content moderator is, which is absolutely fair,” Gandhi tells The Verge. “But it also takes away the fact that we are making progress with some of these problems.”

Machine learning systems need a large number of examples to learn what offending content looks like, explains Gandhi, which means those systems will improve in years to come as training datasets get bigger. He notes that some of the systems currently in place would look impossibly fast and accurate even a few years ago. “I’m confident, given the improvements we’ve made in the last five, six years, that at some point we’ll be able to completely automate moderation,” says Gandhi.

Others would disagree, though, noting that AI systems have yet to master not only political and cultural context (which is changing month to month, as well as country to country) but also basic human concepts like sarcasm and irony. Throw in the various ways in which AI systems can be fooled by simple hacks, and a complete AI solution looks unlikely.

Sandra Wachter, a lawyer and research fellow at the Oxford Internet Institute, says there are also legal reasons why humans will need to be kept in the loop for content moderation.

“In Europe we have a data protection framework [GDPR] that allows people to contest certain decisions made by algorithms. It also says transparency in decision making is important [and] that you have a right to know what’s happening to your data,” Wachter tells The Verge. But algorithms can’t explain why they make certain decisions, she says, which makes these systems opaque and could lead to tech companies getting sued.

Wachter says that complaints relating to GDPR have already been lodged, and that more cases are likely to follow. “When there are higher rights at stake, like the right to privacy and to freedom of speech, [...] it’s important that we have some sort of recourse,” she says. “When you have to make a judgement call that impacts other people’s freedom you have to have a human in the loop that can scrutinize the algorithm and explain these things.”

“A challenge no other media system has ever had to face.”

As Caplan notes, what tech companies can do — with their huge profit margins and duty of care to those they employee — is improve working conditions for human moderators. “At the very bare minimum we need to have better labor standards,” she says. As Casey Newton noted in his report, while companies like Facebook do make some effort to properly reward human moderators, giving them health benefits and above-average wages, it’s often outweighed by relentless drive to better accuracy and more decisions.

Caplan says that pressure on tech companies to solve the problem of content automation could also be contributing to this state of affairs. “That’s when you get issues where workers are held to impossible standards of accuracy,” she says. The need to come up with a fix as soon as possible plays into Silicon Valley’s often-maligned “move fast and break things” attitude. And while this can be a great way to think when launching an app, it’s a terrible mindset for a company managing the subtleties of global speech.

“And we’re saying now maybe we should use machines to deal with this problem,” says Caplan, “but that will lead to a whole new set of issues.”

It’s also worth remembering that this is a new and unique problem. Never before have platforms as huge and information-dense as Facebook and YouTube existed. These are places where anyone, anywhere in the world, any time, can upload and share whatever content they like. Managing this vast and ever-changing semi-public realm is “a challenge no other media system has ever had to face,” says Caplan.

What we do know is that the status quo is not working. The humans tasked with cleaning up the internet’s mess are miserable, and the humans creating that mess aren’t much better off. Artificial intelligence doesn’t have enough smarts to deal with the problem, and human intelligence is stretched coming up with solutions. Something’s gotta give.

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