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I have a lot to say about education and how it has, in the years leading to our next industrial revolution, failed in it’s the purpose, and instead, has become a backdoor to ingenious stabilizations of political systems. Through the money it generates, it has vast influences on the policies our system emphasizes today while leaving it’s own troubles in a shady mist of it’s own.
That’s not to say such a system is bad for today’s time- in fact, having a backdoor funnel to help us learn and debate how to handle climate change, gun violence, healthcare, and other systems is a great way to keep the baseline economics that drives politics stable.
Such unifying financial sources seem to at least incentivize our politicians from holding back on the, apparently, successful tactic, of saying some of the most extreme things they can in their minds and to run away for it.
But there is a massive, and blatant problem with our education system. Well, there are many, but culminated together, the system we have today could be incredibly influential in putting us through an economic crisis in the shift we are all about to face.
This sounds extreme-but let me explain, because, interestingly, this ties directly into the economics of AI and where wether or not we like it, our future will be heading to.
There is a certain book I recently finished, called Prediction Machines, which explains how the economics of artificial intelligence will shift how the economy and the workforce will be operating in the future.
3. Real vs Fake Tweet Detection using a BERT Transformer Model in few lines of code
The book centralizes around one central fact- that AI is absolutely nothing in the way that we have framed it. It has one clear function, and that is all- it is a prediction. The ability to predict what we may need next, where orders will be placed, what the weather will be- and more importantly, what decisions will be made, and what they can lead to.
It is this sole ability of AI that will transform our workforce, as AI has the capacity to help businesses better understand their clients, solve practical problems in many different capacities (such as finance, engineering, biology, marketing- pretty much everything).
The reason why is because we run on prediction. We make choices by the second, and AI takes that burden off of us, making our lives more efficient, and cheaper, in the long run. Meaning, it’s going to be taking big roles in big companies- roles, that, yes, even incredibly smart people can’t compete with. An AI module can help make managerial decisions, strategic recommendations, PR coverage, growth systems, execution, and so much more- and as a result, the way humans think and bring value needs to change.
See there is one component of the economy that has a record of being able to do well- the idea of complimenting. Like selling shovels for the gold, creating value that accelerates the ability to, create massive value, is an incredibly lucrative way to make money and function when done right- and in the case of where we will lie in an economy when one of the most valuable components of our brain is taken- complimenting AI is where humans will stand the best and most lucrative chance.
The economy will now have a greater want to utilize those who can capitalize on irreplaceable human tendencies- the ability to imagine and visualize outcomes, creativity, emotional output/understanding, to talk and converse, entertain, lead, and so on and so forth. Essentially- the best at what AI cannot thoroughly replace.
However, right now, we live in an education system in which so much of what we do could be replaced by AI.
Think about it- right now, an algorithm sitting next to you, would almost definitely be able to do better then you did in school, and thus be considered the better student, and presumptively, economic utility. Not only can it show up every day- it doesn’t forget, and can learn and compile wrote information better than humans- and predict what kinds of problems will be on the tests. It doesn’t have to deal with life, or sleep, for that matter.
To put it simply, if a class were a problem, AI would be a far more effective solution then a human.
Of course, these are for the classes where such a system is implemented.
Right now, our students, and what skills they re learning, can very easily be replaced by the likes of AI, which means, eventually, the time will come when AI takes the world by storm- and the jobs of today won’t be the jobs we have. Instead, we will have more jobs that emphasize the human sides of us, and the capacity to think in depths AI may never truly be able to touch, thanks to the incredible complexities of the human brain.
So herein, the question stands- how do we build an economy where humans can thrive in which the most repetitive, or at the least, replaceable functions of ourselves is no longer the gateways to earning money?
The answer is simple- you teach kids what makes humans the most powerful creatures on the face of the earth. Teach them the art of effective learning, communication, creativity, critical thinking, conversation, conduction, empathizing, the ability to predict enough to bring value, the ability to see, learn, and get ahead of trends, to visualize, capitalize, and emotional awareness.
If schooling changed it’s a system to emphasize these skills- all of which, thanks to computing, is actively achievable form an informational and tactical standpoint, we would be building ourselves back to what makes us run the economy the way we do- irreplaceability and value.
Also- I highly recommend you read Prediction Machines if you want to learn what’s going to be ahead of the curve. That’s all for today.
Education and the economy as we know it. was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
How can we cluster images, even WITHOUT labels? And not based on colors, but based on the object, the HIGHER level features.
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In the U.S., data scientists are experiencing some of the best job choices, based on the average salary and the number of available opportunities in the field. With the emergence of big data, businesses have explored a new asset — data that can help them experience huge growth when leveraged properly. You may already know that almost all the giant tech companies including Google, Facebook and Amazon have leveraged data in order to create their own business platforms. This perfectly explains the need for a different set of trained professionals who can organize, analyze, and derive valuable insights from data — the data scientists.
Typically, data scientists have a solid understanding of software development, database systems, predictive analysis, and statistics. This makes the role somewhat different that needs the skills of both a statistician and a computer scientist and this is the main reason why data scientists are experiencing high demand.
At its simplest form, data science revolves around the collection, storage, and analysis of huge amounts of data. In the entire process, data scientists make use of a lot of advanced tools and technologies that help them to a good extent in performing their activities. For example, advanced solutions like artificial intelligence, machine learning, powerful analytics tools etc enable them to process and understand huge volumes of data at unprecedented speeds.
3. Real vs Fake Tweet Detection using a BERT Transformer Model in few lines of code
Data scientists are also the person responsible for translating the insights for other people in the organization, including stakeholders and senior executive — decision-makers in short. For example, they may decide on the form of data that is needed to be filtered into a storage system or pass details about consumer behavior on to other departments for building more successful and targeted campaigns.
Now consider the fast pace at which more-advanced and diverse tools and technologies in the field of data science are emerging. What we can learn from all these is how different this industry will be in the coming future. Probably you are already aware of the fact that the job of data scientists has already been considered as the 21st century’s sexiest job. And these days, job analysts across the globe are strengthening the statement as well. But how are they predicting this? What are the probable reasons? Let’s explore.
Most of us have gone through some articles portraying that the field of data science is already saturated. While it’s a fact that there is a huge number of data scientists are working in the field and a lot of aspirants are waiting to join the league, but that isn’t going to impact the promising future of data scientists anyway. Despite all those noises, there are no real reasons to believe that there’ll be a paucity of jobs for skilled data scientists. In fact, the very arguments utilized to form those statements are actually the reasons not to worry at all. Let’s have a look at the reasons for which the future of data scientists seems to be bright.
A huge amount of data is being generated by both businesses and common people on a regular basis. A recent study reveals that the number of consumers that interact with data daily will be a whopping 6 billion by 2025. In addition, in 2018, the amount of total data in the world was 33 zettabytes and now this is projected to become 133 zettabytes by 2025. As the world is becoming more and more connected than ever through the increasing use of connected devices, data generation will keep on rising. And data scientists will be central in helping businesses leverage that data effectively.
It’s evident now that a significant number of tasks performed by data scientists is getting commoditized increasingly — a huge number of machine learning frameworks now come with libraries that contain off-the-shelf models which are pre-trained, pre-architectured, and pre-tuned. The resulting effect is that a well-rounded data scientist is now able to solve in a much shorter timeframe what an entire time wasn’t able to solve in several months a decade ago. It means that hiring a well-rounded data scientist has become viable for a significant number of domains for which the idea was too complex or too expensive before.
Tools and technologies will keep on appearing and disappearing, but they’ll be targeted at increasing the productivity of data scientists and thus, their net value to a business.
It’s a fact that any field without growth potential becomes stagnant at some point in time. It also indicates that the jobs within those fields need to change in order to stay relevant, but that isn’t the case with the data scientist job. Since there is no sign of slowing down with a significant number of opportunities gearing up to appear, probably it’s the best time for people looking to become data scientists to start preparing. Of course, there’ll be some probable minor changes like someone working in the position of a data scientist in an organization may not be doing the same thing at another company.
In a way, it’ll be helpful for aspiring data scientists as they’ll be able to focus on learning more specialized skills and do what’s most meaningful to them.
You may already know that in the European Union, the GDPR (General Data Protection Regulation) took effect in May 2018 for countries. This implementation increased the need for data scientists to the organizations because of the need for storing data responsibly. One major aspect of the GDPR is that it allows consumers to ask the companies to delete some sorts of data. These days, people have become increasingly conscious about their online privacy and security and thus, they consider different aspects of giving away their personal information before actually doing it. They now understand what can be the probable consequences of the occurrence of a data breach.
As a result, it has become impossible for companies to handle customer data irresponsibly. In addition, the GDPR is probably just the beginning with some more privacy rules pertaining to consumer data waiting to be implemented. In this scenario, data scientists are the best people who can guide the businesses on adhering to those regulations while leveraging the power of that data.
Businesses may have the opportunities to capture a massive volume of data regarding website interactions, customer transactions etc from different sources. But what if they aren’t in a position to store, analyze, and derive insights from that data? Simply, the data is of no use. And that’s exactly where data scientists come into the picture. Equipped with huge skillsets, these trained professionals only can help the businesses to get a competitive edge and accomplish their business goals.
As we’ve already discussed that the increasing use of high-end devices will result in a more connected world where more amount of data will be generated on a regular basis, the scenario will become even more complex without the help of data scientists. And for data scientists, it’ll be something like an ongoing opportunity.
While the above points demonstrate the key factors that will be instrumental in making the future for data scientists bright, aspiring data scientists also need to focus on some crucial things. First of all, there’s no denying that now there is a steady supply of average data scientists to the industry who can surely perform at a good level but may not be able to reach the exceptional mark. And to become a top-notch data scientist, you’ve to prepare yourself through the best way possible. Second, the industry will become competitive at some point of time in future, so it’s better to start planning now to rise above the competition.
Assuming you’ve your fundamentals right, you need to decide on the avenue you’ll be taking to become a good data scientist wisely. There’re a significant number of options available out there like self-learning, traditional way, bootcamps etc. All of them come with their own pros and cons. However, there’re some factors that differentiate a bootcamp from other avenues. For instance, if you’re ready to become a data scientist the hard way, joining such a program would be your best bet. There, you’ll be able to learn the concepts, tools, and technologies that are only applicable to real-life business issues and that too in a much shorter amount of time and from professionals working in the industry.
In addition, most of the bootcamps offer job assistance after successful completion of the programs, so stepping into the professional field shouldn’t be an issue. But again, if you notice, we’ve mentioned the term ‘hard way’ because, from a data science bootcamp, you’ll only get what you’ll be putting in during the program in terms of time, effort, and diligence.
Data scientist and their future is seen bright for job analysts – Magnimind Academy
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Data scientist and their future is seen bright for job analysts was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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source https://365datascience.weebly.com/the-best-data-science-blog-2020/overview-of-data-distributions
Data Scientist Job Descriptions 2020
The market for Data Science has been growing extensively over recent years. As a result, the position of data scientist has emerged as a truly attractive career path option with an abundance of rewarding job opportunities.
So, to help you stay at the forefront, we’ve conducted an in-depth study on job offers in the field of data science.
And in this article, we’ll share our insights based on 1,170 data scientist job descriptions in the USA. We’ve extracted valuable information about the companies offering the position, the required educational credentials, and sought-after work experience, as well as the desired skills and techniques involved.
Let’s explore the intriguing findings together, shall we?
The 1,170 data scientist positions in our study were posted by 357 unique companies. This is a positive sign, as:
That being said, let’s take a look at the distribution of offers against the size of the company making the offer. Here’s a chart of the number of openings posted by companies with their respective number of employees:
It’s easy to see that the majority of job offers come from very big companies, with more than 10,000 employees. This could significantly skew our data towards the necessities of big corporations. However, looking beyond that, 823 of the total 1,170 job offers were posted by companies that didn’t actually have a profile on the website. Therefore, their size hasn’t been determined and is not present in the chart.
With that in mind, we can assume that bigger companies tend to register on more employment websites, while their smaller counterparts do not engage as much. So, these 823 offers could have been made by companies small enough to not register.
The data scientist job descriptions we studied originated from 38 states in the U.S. Here are the top 12: California, Virginia, Washington, New York, Massachusetts, Maryland, Texas, Colorado, Michigan, Ohio, New Jersey, and Florida.
And here are the same states highlighted on a map:
When it comes to education, 544 job offers stated that they require at least a Bachelor’s degree, 367 – a Master’s and 50 were looking for a Ph.D. While at the same time in 209 job offers, the level of education was not stated.
As for the preferred fields of study, here are the results. We collected the data by extracting only the first three mentioned fields. Data science takes the lead, followed by Statistics, Mathematics, Computer Science, and Engineering. IT, Economics, and Physics are much less popular, according to the numbers.
We set the years of experience in these 2 categories: ‘years of experience as a data scientist’ and ‘general work experience.’ Bear in mind that in most job offers, general work experience should be in a related field.
What we found out is that on average companies demand that candidates have at least 4.2 years of previous experience as a data scientist and 5.2 years of experience in related fields.
Here are the most quoted programming languages in the 1170 job offers (there may have been more than one language per offer):
No big surprises here – Python is the most popular one, as expected, followed by R and SQL. The other languages with a significant number of mentions are Scala, Java, and C++.
Here they are:
As expected, the most important skills to have are Machine Learning, Statistics, and Python programming, while the most in-demand machine learning techniques are Deep Learning, Clustering, and Natural Language Processing (NLP).
The numbers show you should definitely consider adding Spark, AWS, or Hadoop to your data scientist toolbelt.
Regarding data visualization, it all comes down to Tableau and Power BI. Tableau was mentioned in 228 job offers, whereas Power BI in 79.
That was true in 368 offers, while in the rest 802 there was no mention of communication or teamwork at all.
Here is what we found:
As you can see, there is no real significant difference between the preferred work experience for the different degrees. However, there are two very important factors to consider here:
For this analysis, we have grouped the companies into 5 categories: small (1 – 100 employees), medium (100 – 1,000 employees), big (1,000 – 10,000 employees), sizeable (10,000+ employees), and those with No size data. It is very important to remember that the sample size here is rather small.
Quite surprisingly, it looks like the smallest companies have the highest requirements for experience. Apart from the sample limitation, we can assume that smaller companies have a limited number of employees. So, to expand and become successful, it needs more experienced professionals. The sizeable companies, in contrast, may not necessarily need an experienced individual but someone they can train to become a useful tool for the company in the future.
Due to the small samples, we have decided to summarize the data in a table, rather than a graph. Smaller companies don’t really look for Ph.D.s and prefer Master’s degree holders. At the other end of the spectrum, the bigger companies have somewhat more balanced requirements with an approximately equal number of positions asking for either a Bachelor’s or a Master’s degree.
We hope this information will be useful and advantageous for you in your path to landing your dream data scientist job.
Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up for 12 hours of beginner to advanced video content for free by clicking on the button below.
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