The importance of being Data Scientist

Header-Image by Clint Adair on Unsplash.

The incredible results of Machine Learning and Artificial Intelligence, Deep Learning in particular, could give the impression that Data Scientist are like magician. Just think of it. Recognising faces of people, translating from one language to another, diagnosing diseases from images, computing which product should be shown for us next to buy and so on from numbers only. Numbers which existed for centuries. What a perfect illusion. But it is only an illusion, as Data Scientist existed as well for centuries. However, there is a difference between the one from today compared to the one from the past: evolution.

The main activity of Data Scientist is to work with information also called data. Records of data are as old as mankind, but only within the 16 century did it include also numeric forms — as numbers started to gain more and more ground developing their own symbols. Numerical data, from a given phenomenon — being an experiment or the counts of sheep sold by week over the year –, was from early on saved in tabular form. Such a way to record data is interlinked with the supposition that information can be extracted from it, that knowledge — in form of functions — is hidden and awaits to be discovered. Collecting data and determining the function best fitting them let scientist to new insight into the law of nature right away: Galileo’s velocity law, Kepler’s planetary law, Newton theory of gravity etc.

Such incredible results where not possible without the data. In the past, one was able to collect data only as a scientist, an academic. In many instances, one needed to perform the experiment by himself. Gathering data was tiresome and very time consuming. No sensor which automatically measures the temperature or humidity, no computer on which all the data are written with the corresponding time stamp and are immediately available to be analysed. No, everything was performed manually: from the collection of the data to the tiresome computation.

More then that. Just think of Michael Faraday and Hermann Hertz and there experiments. Such endeavour where what we will call today an one-man-show. Both of them developed parts of the needed physics and tools, detailed the needed experiment settings, conducting the experiment and collect the data and, finally, computing the results. The same is true for many other experiments of their time. In biology Charles Darwin makes its case regarding evolution from the data collected in his expeditions on board of the Beagle over a period of 5 years, or Gregor Mendel which carry out a study of pea regarding the inherence of traits. In physics Blaise Pascal used the barometer to determine the atmospheric pressure or in chemistry Antoine Lavoisier discovers from many reaction in closed container that the total mass does not change over time. In that age, one person was enough to perform everything and was the reason why the last part, of a data scientist, could not be thought of without the rest. It was inseparable from the rest of the phenomenon.

With the advance of technology, theory and experimental tools was a specialisation gradually inescapable. As the experiments grow more and more complex, the background and condition in which the experiments were performed grow more and more complex. Newton managed to make first observation on light with a simple prism, but observing the line and bands from the light of the sun more than a century and half later by Joseph von Fraunhofer was a different matter. The small improvements over the centuries culminated in experiments like CERN or the Human Genome Project which would be impossible to be carried out by one person alone. Not only was it necessary to assign a different person with special skills for a separate task or subtask, but entire teams. CERN employs today around 17 500 people. Only in such a line of specialisation can one concentrate only on one task alone. Thus, some will have just the knowledge about the theory, some just of the tools of the experiment, other just how to collect the data and, again, some other just how to analyse best the recorded data.

If there is a specialisation regarding every part of the experiment, what makes Data Scientist so special? It is impossible to validate a theory, deciding which market strategy is best without the work of the Data Scientist. It is the reason why one starts today recording data in the first place. Not only the size of the experiment has grown in the past centuries, but also the size of the data. Gauss manage to determine the orbit of Ceres with less than 20 measurements, whereas the new picture about the black hole took 5 petabytes of recorded data. To put this in perspective, 1.5 petabytes corresponds to 33 billion photos or 66.5 years of HD-TV videos. If one includes also the time to eat and sleep, than 5 petabytes would be enough for a life time.

For Faraday and Hertz, and all the other scientist of their time, the goal was to find some relationship in the scarce data they painstakingly recorded. Due to time limitations, no special skills could be developed regarding only the part of analysing data. Not only are Data Scientist better equipped as the scientist of the past in analysing data, but they managed to develop new methods like Deep Learning, which have no mathematical foundation yet in spate of their success. Data Scientist developed over the centuries to the seldom branch of science which bring together what the scientific specialisation was forced to split.

What was impossible to conceive in the 19 century, became more and more a reality at the end of the 20 century and developed to a stand alone discipline at the beginning of the 21 century. Such a development is not only natural, but also the ground for the development of A.I. in general. The mathematical tools needed for such an endeavour where already developed by the half of the 20 century in the period when computing power was scars. Although the mathematical methods were present for everyone, to understand them and learn how to apply them developed quite differently within every individual field in which Machine Learning/A.I. was applied. The way the same method would be applied by a physicist, a chemist, a biologist or an economist would differ so radical, that different words emerged which lead to different langues for similar algorithms. Even today, when Data Science has became a independent branch, two different Data Scientists from different application background could find it difficult to understand each other only from a language point of view. The moment they look at the methods and code the differences will slowly melt away.

Finding a universal language for Data Science is one of the next important steps in the development of A.I. Then it would be possible for a Data Scientist to successfully finish a project in industry, turn to a new one in physics, then biology and returning to industry without much need to learn special new languages in order to be able to perform each tasks. It would be possible to concentrate on that what a Data Scientist does best: find the best algorithm. In other words, a Data Scientist could resolve problems independent of the background the problem was stated.

This is the most important aspect that distinguish the Data Scientist. A mathematician is limited to solve problems in mathematics alone, a physicist is able to solve problems only in physics, a biologist problems only in biology. With a unique language regarding the methods and strategies to solve Machine Learning/A.I. problems, a Data Scientist can solve a problem independent of the field. Specialisation put different branches of science at drift from each other, but it is the evolution of the role of the Data Scientist to synthesize from all of them and find the quintessence in a language which transpire beyond all the field of science. The emerging language of Data Science is a new building block, a new mathematical language of nature.

Although such a perspective does not yet exists, the principal component of Machine Learning/A.I. already have such proprieties partially in form of data. Because predicting for example the numbers of eggs sold by a company or the numbers of patients which developed immune bacteria to a specific antibiotic in all hospital in a country can be performed by the same prediction method. The data do not carry any information about the entities which are being predicted. It does not matter anymore if the data are from Faraday’s experiment, CERN of Human Genome. The same data set and its corresponding prediction could stand literary for anything. Thus, the result of the prediction — what we would call for a human being intuition and/or estimation — would be independent of the domain, the area of knowledge it originated.

It also lies at the very heart of A.I., the dream of researcher to create self acting entities, that is machines with consciousness. This implies that the algorithms must be able to determine which task, model is relevant at a given moment. It would be to cumbersome to have a model for every task and and every field and then try to connect them all in one. The independence of scientific language, like of data, is thus a mandatory step. It also means that developing A.I. is not only connected to develop a new consciousness, but, and most important, to the development of our one.

Accelerate your AI Skills Today: A Million Dollar Job!

The skyrocketing salaries ($1m per year) of AI engineers is not a hype. It is the fact of current corporate world, where you will witness a shift that is inevitable.

We’ve already set our feet at the edge of the technological revolution. A revolution that is at the verge of altering the way we live and work. As the fact suggests, humanity has fundamentally developed human production in three revolutions, and we’re now entering the fourth revolution. In its scope, the fourth revolution projects a transformation that is unlike anything we humans have ever experienced.

  • The first revolution had the world transformed from rural to urban
  • the emergence of mass production in the second revolution
  • third introduced the digital revolution
  • The fourth industrial revolution is anxious to integrate technologies into our lives.

And all thanks to artificial intelligence (AI). An advanced technology that surrounds us, from virtual assistants to software that translates to self-driving cars.

The rise of AI at an exponential rate has disrupted almost every industry. So much so that AI is being rated as one-million-dollar profession.

Did this grab your attention? It did?

Now, what if we were to tell you that the salary compensation for AI experts has grown dramatically. AI and machine learning are fields that have a mountain of demand in the tech industry today but has sparse supply.

AI field is growing at a quicker pace and salaries are skyrocketing! Read it for yourself to know what AI experts, AI researchers and any other AI talent are commanding today.

  • A top-class AI research laboratory, OpenAI says that techies in the AI field are projected to earn a salary compensation ranging between $300 to $500k for fresh graduates. However, expert professionals could earn anywhere up to $1m.
  • Whopping salary package of above 100 million yen that amounts to $1m is being offered to AI geniuses by a Japanese firm, Start Today. A firm that operates a fashion shopping website named Zozotown.

Does this leave you with a question – Is this a right opportunity for you to jump in the field and make hay while the sun is shining? 

And the answer to this question is – yes, it is the right opportunity for any developer seeking a role in the AI industry. It can be your chance to bridge the skill shortage in the AI field either by upskilling or reskilling yourself in the field of AI.

There are a wide varieties of roles available for an AI enthusiast like you. And certain areas are like AI Engineers and AI Researchers are high in demand, as there are not many professionals who have robust AI knowledge.

According to a job report, “The Future of Jobs 2018,” a prediction was made suggesting that machines and algorithms will create around 133 million new job roles by 2022.

AI and machine learning will dominate the tech world. The World Economic Forum says that several sectors have started embracing AI and machine learning to tackle challenges in certain fields such as advertising, supply chain, manufacturing, smart cities, drones, and cybersecurity.

Unraveling the AI realm

From chatbots to financial planners, AI is impacting the way businesses function on a day-today basis. AI makes the work simpler, as it provides variables, which makes the work more streamlined.

Alright! You know that

  • the demand for AI professionals is rising exponentially and that there is just a trickle of supply
  • the AI professionals are demanding skyrocketing salaries

However, beyond that how much more do you know about AI?

Considering the fact that our lives have already been touched by AI (think Alexa, and Siri), it is just a matter of time when AI will become an indispensable part of our lives.

As Gartner predicts that 2020 will be an important year for business growth in AI. Thus, it is possible to witness significant sparks for employment growth. Though AI predicts to diminish 1.8 million jobs, it is also said to replace it with 2.3 million jobs that will be created. As we look forward to stepping into 2020, AI-related job roles are set to make positive progress of achieving 2 million net-new employments by 2025.

With AI promising to score fat paychecks that would reach millions, AI experts are struggling to find new ways to pick up nouveau skills. However, one of the biggest impacts that affect the job market today is the scarcity of talent in this field.

The best way to stay relevant and employable in AI is probably by “reskilling,” and “upskilling.” And  AI certifications is considered ideal for those in the current workforce.

Looking to upskill yourself – here’s how you can become an AI engineer today.

Top three ways to enhance your artificial intelligence career:

  1. Acquire skills in Statistics and Machine Learning: If you’re getting into the field of machine learning, it is crucial that you have in-depth knowledge of statistics. Statistics is considered a prerequisite to the ML field. Both the fields are tightly related. Machine learning models are created to make accurate predictions while statistical models do the job of interpreting the relationship between variables. Many ML techniques heavily rely on the theory obtained through statistics. Thus, having extensive knowledge in statistics help initiate the first step towards an AI career.
  2. Online certification programs in AI skills: Opting for AI certifications will boost your credibility amongst potential employers. Certifications will also enhance your earning potential and increase your marketability. If you’re looking for a change and to be a part of something impactful; join the AI bandwagon. The IT industry is growing at breakneck speed; it is now that businesses are realizing how important it is to hire professionals with certain skillsets. Specifically, those who are certified in AI are becoming sought after in the job market.
  3. Hands-on experience: There’s a vast difference in theory and practical knowledge. One needs to familiarize themselves with the latest tools and technologies used by the industry. This is possible only if the individual is willing to work on projects and build things from scratch.

Despite all the promises, AI does prove to be a threat to job holders, if they don’t upskill or reskill themselves. The upcoming AI revolution will definitely disrupt the way we work, however, it will leave room for humans to perform more creative jobs in the future corporate world.

So a word of advice is to be prepared and stay future ready.

The Data Scientist Job and the Future

A dramatic upswing of data science jobs facilitating the rise of data science professionals to encounter the supply-demand gap.

By 2024, a shortage of 250,000 data scientists is predicted in the United States alone. Data scientists have emerged as one of the hottest careers in the data world today. With digitization on the rise, IoT and cognitive technologies have generated a large number of data sets, thus, making it difficult for an organization to unlock the value of these data.

With the constant rise in data science, those fail to upgrade their skill set may be putting themselves at a competitive disadvantage. No doubt data science is still deemed as one of the best job titles today, but the battles for expert professionals in this field is fierce.

The hiring market for a data science professional has gone into overdrive making the competition even tougher. New online institutions have come up with credible certification programs for professionals to get skilled. Not to forget, organizations are in a hunt to hire candidates with data science and big data analytics skills, as these are the top skills that are going around in the market today. In addition to this, it is also said that typically it takes around 45 days for these job roles to be filled, which is five days longer than the average U.S. market.

Data science

One might come across several definitions for data science, however, a simple definition states that it is an accumulation of data, which is arranged and analyzed in a manner that will have an effect on businesses. According to Google, a data scientist is one who has the ability to analyze and interpret complex data, being able to make use of the statistic of a website and assist in business decision making. Also, one needs to be able to choose and build appropriate algorithms and predictive models that will help analyze data in a viable manner to uncover positive insights from it.

A data scientist job is now a buzzworthy career in the IT industry. It has driven a wider workforce to get skilled in this job role, as most organizations are becoming data-driven. It’s pretty obnoxious being a data professional will widen job opportunities and offer more chances of getting lucrative salary packages today. Similarly, let us look at a few points that define the future of data science to be bright.

  • Data science is still an evolving technology

A career without upskilling often remains redundant. To stay relevant in the industry, it is crucial that professionals get themselves upgraded in the latest technologies. Data science evolves to have an abundance of job opportunities in the coming decade. Since, the supply is low, it is a good call for professionals looking to get skilled in this field.

  • Organizations are still facing a challenge using data that is generated

Research by 2018 Data Security Confidence from Gemalto estimated that 65% of the organizations could not analyze or categorized the data they had stored. However, 89% said they could easily analyze the information prior they have a competitive edge. Being a data science professional, one can help organizations make progress with the data that is being gathered to draw positive insights.

  • In-demand skill-set

Most of the data scientists possess to have the in-demand skill set required by the current industry today. To be specific, since 2013 it is said that there has been a 256% increase in the data science jobs. Skills such as Machine Learning, R and Python programming, Predictive analytics, AI, and Data Visualization are the most common skills that employers seek from the candidates of today.

  • A humongous amount of data growing everyday

There are around 5 billion consumers that interact with the internet on a daily basis, this number is set to increase to 6 billion in 2025, thus, representing three-quarters of the world’s population.

In 2018, 33 zettabytes of data were generated and projected to rise to 133 zettabytes by 2025. The production of data will only keep increasing and data scientists will be the ones standing to guard these enterprises effectively.

  • Advancement in career

According to LinkedIn, data scientist was found to be the most promising career of 2019. The top reason for this job role to be ranked the highest is due to the salary compensation people were being awarded, a range of $130,000. The study also predicts that being a data scientist, there are high chances or earning a promotion giving a career advancement score of 9 out of 10.

Precisely, data science is still a fad job and will not cease until the foreseeable future.

Closing the AI-skills gap with Upskilling

Closing the AI-skills gap with Upskilling

Artificial Intelligent or as it is fancily referred as AI, has garnered huge popularity worldwide.  And given the career prospects it has, it definitely should. Almost everyone interested in technology sector has them rushing towards it, especially young and motivated fresh computer science graduates. Compared to other IT-related jobs AI pays way higher salary and have opportunities. According to a Glassdoor report, Data Scientist, one of the many related jobs, is the number one job with good salary, job openings and more. AI-related jobs include Data Scientists, Analysts, Machine Learning Engineer, NLP experts etc.

AI has found applications in almost every industry and thus it has picked up demand. Home assistants – Siri, Ok Google, Amazon Echo — chatbots, and more some of the popular applications of AI.

Increasing adoption of AI across Industry

The advantages of AI like increased productivity has increased its adoption among companies. According to Gartner, 37 percent of enterprise currently use AI in one way or the other. In fact, in the last four year adoption of AI technologies among companies has increased by 270 percent. In telecommunications, for instance, 52 percent of companies have chatbots deployed for better and smoother customer experience. Now, about 49 percent of businesses are now on their way to alter business models to integrate and adopt AI-driven processes. Further, industry leaders have gone beyond and voiced their concerns about companies that are lagging in AI adoption.

Unfortunately, it has been extremely difficult for employers to find right skilled or qualified candidates for AI-related positions. A reports suggests that there are total 300,000 AI professionals are available worldwide, while there’s demand for millions. In a recent survey conducted by Ernst & Young, 51 percent AI professionals told that lack of talent was the biggest impediment in AI adoption.

Further, O’Reilly, in 2018 conducted a survey, which found the lack of AI skills, among other things, was the major reason that was holding companies back from implementing AI.
The major reason for this is the lack of skills among people who aspire to get into AI-related jobs. According to a report, there demand for millions for jobs in AI. However, only a handful of qualified people are available.

Bridging the skill gap in AI-related jobs

Top companies and government around the world have taken up initiatives to close this gap. Google and Amazon, for instance, have dedicated facilities which trains in AI skills.  Google’s Brain Toronto is a dedicated facility to expand their talent in AI.  Similarly, Amazon has facility near University of Cambridge which is dedicated to AI. Most companies either already have a facility or are in the process of setting up one.

In addition to this, governments around the world are also taking initiatives to address the skill gap. For instance, government across the world are pushing towards AI advancement and are develop collaborative plans which aims at delivering more AI skilled professionals. Recently, the white house launched ai.gov which is further helping to promote AI in the US. The website will offer updates related to AI projects across different sectors.

Other than these, companies have taken this upon themselves to reskills their employees and prepare them for future roles. According to a report from Towards Data Science, about 63 percent of companies have in-house training programs to train employees in AI-related skills.

Overall, though there is demand for AI professionals, lack of skilled talent is a major problem.

Roles in Artificial Intelligence
Artificial Intelligence is the most dominant role for which companies hire across artificial Intelligence. Other than that, following are some of the popular roles:

  1. Machine learning Engineer: These are the people who make machines learn with complex algorithms. On advance level, Machine learning engineers are required to have good knowledge of computer vision. According to Indeed, in the last year, demand for Machine Learning Engineer has grown by 344 percent.
  2. NLP Experts: These experts are equipped with the understanding of making machines computer understand human language. Their expertise includes knowledge of how machines understand human language. Text-to-speech technologies are the common areas which require NLP experts. Demand for engineers who can program computers to understand human speech is growing continuously. It was the fast growing skills in Upwork’s list of in-demand freelancing skills. In Q4, 2016, it had grown 200 percent and since then has been on continuously growing.
  3. Big Data Engineers: This is majorly an analytics role. These gather huge amount of data available from sources and analyze it to derive insights and understand patter, which may be further used for machine learning, prediction modelling, natural language processing. In Mckinsey annual report 2018, it had reported that there was shortage of 190,000 big data professionals in the US alone.

Other roles like Data Scientists, Analysts, and more also in great demand. Then, again due to insufficient talent in the market, companies are struggling to hire for these roles.

Self-learning and upskilling
Artificial Intelligence is a continuously growing field and it has been advancing at a very fast pace, and it makes extremely difficult to keep up with in-demand skills. Hence, it is imperative to keep yourself up with demand of the industry, or it is just a matter of time before one becomes redundant.

On an individual level, learning new skills is necessary. One has to be agile and keep learning, and be ready to adapt new technologies. For this, AI training programs and certifications are ideal.  There are numerous AI programs which individuals can take to further learn new skills. AI certifications can immensely boost career opportunities. Certification programs offer a structured approach to learning which benefits in learning mostly practical and executional skills while keeping fluff away. It is more hands-on. Plus, certifications programs qualify only when one has passed practical test which is very advantageous in tech. AI certifications like AIE (Artificial Intelligence Engineer) are quite popular.

Online learning platforms also offer good a resource to learn artificial intelligence. Most schools haven’t yet adapted their curriculum to skill for AI, while most universities and grad schools are in their way to do so. In the meantime, online learning platforms offer a good way to learn AI skills, where one can start from basic and reach to advance skills.

Business Intelligence Organizations

I am often asked how the Business Intelligence department should be set up and how it should interact and collaborate with other departments. First and foremost: There is no magic recipe here, but every company must find the right organization for itself.

Before we can talk about organization of BI, we need to have a clear definition of roles for team members within a BI department.

A Data Engineer (also Database Developer) uses databases to save structured, semi-structured and unstructured data. He or she is responsible for data cleaning, data availability, data models and also for the database performance. Furthermore, a good Data Engineer has at least basic knowledge about data security and data privacy. A Data Engineer uses SQL and NoSQL-Technologies.

A Data Analyst (also BI Analyst or BI Consultant) uses the data delivered by the Data Engineer to create or adjust data models and implementing business logic in those data models and BI dashboards. He or she needs to understand the needs of the business. This job requires good communication and consulting skills as well as good developing skills in SQL and BI Tools such like MS Power BI, Tableau or Qlik.

A Business Analyst (also Business Data Analyst) is a person form any business department who has basic knowledge in data analysis. He or she has good knowledge in MS Excel and at least basic knowledge in data analysis and BI Tools. A Business Analyst will not create data models in databases but uses existing data models to create dashboards or to adjust existing data analysis applications. Good Business Analyst have SQL Skills.

A Data Scientist is a Data Analyst with extended skills in statistics and machine learning. He or she can use very specific tools and analytical methods for finding pattern in unknow or big data (Data Mining) or to predict events based on pattern calculated by using historized data (Predictive Analytics). Data Scientists work mostly with Python or R programming.

Organization Type 1 – Central Approach (Data Lab)

The first type of organization is the data lab approach. This organization form is easy to manage because it’s focused and therefore clear in terms of budgeting. The data delivery is done centrally by experts and their method and technology knowledge. Consequently, the quality expectation of data delivery and data analysis as well as the whole development process is highest here. Also the data governance is simple and the responsibilities clearly adjustable. Not to be underestimated is the aspect of recruiting, because new employees and qualified applicants like to join a central team of experts.

However, this form of organization requires that the company has the right working attitude, especially in the business intelligence department. A centralized business intelligence department acts as a shared service. Accordingly, customer-oriented thinking becomes a prerequisite for the company’s success – and customers here are the other departments that need access to the capacities of those centralized data experts. Communication boundaries must be overcome and ways of simple and effective communication must be found.

Organization Type 2 – Stakeholder Focus Approach

Other companies want to shift more responsibility for data governance, and especially data use and analytics, to those departments where data plays a key role right now. A central business intelligence department manages its own projects, which have a meaning for the entire company. The specialist departments, which have a special need for data analysis, have their own data experts who carry out critical projects for the specialist department. The central Business Intelligence department does not only provide the technical delivery of data, but also through methodical consulting. Although most of the responsibility lies with the Business Intelligence department, some other data-focused departments are at least co-responsible.

The advantage is obvious: There are special data experts who work deeper in the actual departments and feel more connected and responsible to them. The technical-business focus lies on pain points of the company.

However, this form of Ogranization also has decisive disadvantages: The danger of developing isolated solutions that are so special in some specific areas that they will not really work company-wide increases. Typically the company has to deal with asymmetrical growth of data analytics
know-how. Managing data governance is more complex and recruitment is becoming more difficult as the business intelligence department is weakened and smaller, and data professionals for other departments need to have more business focus, which means they are looking for more specialized profiles.

Organization Type 3 – Decentral Approach

Some companies are also taking a more extreme approach in the other direction. The Business Intelligence department now has only Data Engineers building and maintaining the data warehouse or data lake. As a result, the central department only provides data; it is used and analyzed in all other departments, specifically for the respective applications.

The advantage lies in the personal responsibility of the respective departments as „pain points“ of the company are in focus in belief that business departments know their problems and solutions better than any other department does. Highly specialized data experts can understand colleagues of their own department well and there is no no shared service mindset neccessary, except for the data delivery.

Of course, this organizational form has clear disadvantages since many isolated solutions are unavoidable and the development process of each data-driven solution will be inefficient. These insular solutions may work with luck for your own department, but not for the whole company. There is no one single source of truth. The recruiting process is more difficult as it requires more specialized data experts with more business background. We have to expect an asymmetrical growth of data analytics know-how and a difficult data governance.

 

Deep Learning and Human Intelligence – Part 1 of 2

Many people are under the impression that the new wave of data science, machine learning and/or digitalization is new, that it did not exist before. But its history is as long as the history of humanity and/or science itself.  The scientific discovery could hardly take place without the necessary data. Even the process of discovering the numbers included elements of machine learning: pattern recognition, comparison between different groups (ranking), clustering, etc. So what differentiates mathematical formulas from machine learning and how does it relate to artificial intelligence?

There is no difference between the two if seen from the perspective of formulas however, such a perspective limits the type of data to which they can be applied. Data stored via tables consist of structured data and are stored in so-called relational databases. The reason for such a data storage is the connection between different fields that assume a well-established structure in advance, such as a company’s sales or balance sheet. However, with the emergence of personal computers, many of the daily activities have been digitalized: music, pictures, movies, and so on. All this information is stored unrelated to other data and therefore called unstructured data.

IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

Copyright: IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

The essence of scientific discoveries was and will be structure. Not surprisingly, the mathematical formulas revolve around relations between variables – information, in general. For example, Galileo derived the law of falling balls from measuring the successive hight of a falling ball. The main difficulty was to obtain measurements at regular time intervals. What about if the data is not structured, which mathematical formula should be applied then? There is a distribution of people’s height, but no distribution for the pictures taken in all holidays for the last year, there is an amplitude for acoustic signals, but no function that detects the similarity between two songs. This is one of the reasons why machine learning focuses heavily on clustering and classification.

Roughly speaking, these simple examples are enough to categorize the difference between scientific discovery and machine learning. Science is about discovering relationships between different variables, Machine Learning tries to automatize processes. Every technical improvement is part of the automation, so why is everything different in this case? Because the current automation deals with human intelligence. The car automates the walking, the kitchen stove the fire, but Machine Learning parts of the human intelligence. There is a difference between the previous automation steps and those of human intelligence. All the previous ones are either outside the human body – such as Fire – or unconsciously executed (once learned) – walking, spinning, etc. The automation induced by Machine Learning affects a part of the human intelligence that we consciously perceive. Of course, today’s machine learning tools are unable to automate all human intelligence, but it is a fascinating step in that direction.

A breakthrough in Machine Learning tasks was achieved in 2012 when the first Deep Learning algorithm for detecting types of images, reached near-human accuracy. It could appreciate the likelihood that the image is a human face, a train, a ball or a fish without having “seen” the picture before. Such an algorithm can be used in various areas:  personally – facial recognition in pictures and/or social media – as tagging of images or videos, medicine – cancer detection, etc. For understanding such cutting-edge issues of classification, one cannot avoid understanding how Deep Learning works. To see the beauty of such algorithms and, at the same time, to be able to comprehend the difficulty of working with them, an example will be the best guide.

The building blocks of Deep Learning are neurons, operational units, which perform mathematical operations or logical operations like AND, OR, etc., and are modelled after the neurons in the brain. Already in the 1950’s two neuroscientist, Hubel and Wiesel, observed that not all neurons in the brain are responding in the same fashion to visual stimuli. Some responded only to horizontal lines, whereas others to vertical lines, with other words, the brain is constructed with specialized neurons. Groups of such neurons are called, in the Machine Learning community, layers. Like in the brain, neurons with different properties are clustered in different layers. This implies that layers have also specific properties and have to be arranged in a specific way, called architecture. It is this architecture which differentiates Deep Learning from Artificial Neuronal Networks (ANN are similar to a layer).

Unfortunately, scientists still haven’t figured out how the brain works, thus to discover how to train Deep Learning from data was not an easy task, and is also the reason why another example is used to explain the training of Deep Learning: the eye. One has always to remember: once it is known how Deep Learning works, it is simple to find example which illustrates the working mechanism.  For such an analogy, it is sufficient for someone without any knowledge about Deep Learning, to keep in mind only the elements that compose such architectures: input data, different layers of neurons, output layers, ReLu’s.

Input data are any type of information, in our example it is light. Of course, that Deep Learning is not limited only to images or videos, but also to sound and/or time series, which would imply that the example would be the ear and sound waves, or the brain and numbers.

Layers can be seen as cells in the eye. It is well known that the eye is formed of different layers connected to each other with each of them having different properties, functionalities. The same is true also for the layers of a Deep Learning architecture: one can see the neurons as cells of the layer as the tissue. While, mathematically, the neurons are nothing more than simple operations, usually linear weight functions, they can be seen as the properties of individual cells. Each layer has one weight matrix, which gives the neuron (and layer) specific properties depending on the data and the task at hand.

It is here that the architecture becomes very important. What Deep Learning offers is a default setting of the layers with unknown weights. One can see this as trying to build an eye knowing that there are different types of cells and different ways how tissues of such cells can be arranged, but not which cell exactly is needed (with what properties) and which arrangement of layers works best. Such an approach has the advantage that one is capable of building any type of organ desired, but the disadvantage is also very obvious: it is time consuming to find the appropriate cell properties and layers arrangements.

Still, the strategy of Deep Learning is a significant departure from the Machine Learning approaches. The performance of Machine Learning methods is as good as the features engineering performed by Data Scientists, and thus depending on the creativity of the Data Scientist. In the case of Deep Learning the engineers of the features is performed automatically as part of the model building. This is a huge improvement, as the only difficult task is to have enough data and computer power to find the right weights matrices. Such an endeavor was performed also by nature for the eye — and is also the reason why one can choose it as an example for Deep Learning — evolution. It is not surprising that Deep Learning is one of the best direction scientists have of Artificial Intelligence today.

The evolution of the eye can be seen, from the perspective of Data Scientists, as the continuous training of a Deep Learning architecture which enables to recognize and track one or more objects. The performance of the evolutional process can be summed up as the fine tuning of the cells which are getting more and more susceptible to light and the adaptation of layers to enable a better vision. Different animals in different environments and different targets — as the hawk and the fly — developed different eyes than humans, but they all work according to the same principle. The tasks that Deep Learning is performing today are similar, for example it can be used to drive cars but there is still a difference:  there is no connection to other organs. Deep Learning is not the approximation of an Artificial Organism, like an android, but a simplified Artificial Organ that can work on its own.

Returning to the working mechanism of the Deep Learning architecture, we can already follow the analogy of what happens if a ray of light is hitting the eye. Once the eye is fully adapted to the task, one can followed how the information enters the Deep Learning architecture (Artificial Eye) by penetrating the input layer. already here arises the question, what kind of eye is the best? One where a small source of light can reach as many neurons as possible, or the one where the light sources reaches only few neurons? In order to take such a decision, a last piece of the puzzle is required: ReLu. One can see them as synapses between neurons (cells) and/or similarly for tissue. By using continuous functions, such as the shape of the latter ‘S’ (called sigmoid), the information from one neuron will be distributed over a large number of other neurons. If one uses the maximum function, then only few neurons are updated with processed information from earlier layers.

Such sparse structures between neurons, was a major improvement in the development of the technique of training Deep Learning architectures. Again, it has a strong evolutionary analogy: energy efficiency. By needing less neurons, the tissues and architecture are both kept to a minimal size which enables flexibility in development and less energy. As the information is process by the different layers, the Artificial Eye is gathering more and more complex (non-linear) structures — the adapted features –, which help to decide, from past experience, what kind of object is detected.

This was part 1 of 2 of the article series. Continue with Part 2.

The 6 most in-demand AI jobs and how to get them

A press release issued in December 2017 by Gartner, Inc explicitly states, 2020 will be a pivotal year in Artificial Intelligence-related employment dynamics. It states AI will become “a positive job motivator”.

However, the Gartner report also sounds some alarm bells. “The number of jobs affected by AI will vary by industry-through 2019, healthcare, the public sector and education will see continuously growing job demand while manufacturing will be hit the hardest. Starting in 2020, AI-related job creation will cross into positive territory, reaching two million net-new jobs in 2025,” the press release adds.

This phenomenon is expected to strike worldwide, as a report carried by a leading Indian financial daily, The Hindu BusinessLine states. “The year 2018 will see a sharp increase in demand for professionals with skills in emerging technologies such as Artificial Intelligence (AI) and machine learning, even as people with capabilities in Big Data and Analytics will continue to be the most sought after by companies across sectors, say sources in the recruitment industry,” this news article says.

Before we proceed, let us understand what exactly does Artificial Intelligence or AI mean.

Understanding Artificial Intelligence

Encyclopedia Britannica explains AI as: “The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with human beings.” Classic examples of AI are computer games that can be played solo on a computer. Of these, one can be a human while the other is the reasoning, analytical and other intellectual property a computer. Chess is one example of such a game. While playing Chess with a computer, AI will analyze your moves. It will predict and reason why you made them and respond accordingly.

Similarly, AI imitates functions of the human brain to a very great extent. Of course, AI can never match the prowess of humans but it can come fairly close.

What this means?

This means that AI technology will advance exponentially. The main objective for developing AI will not aim at reducing dependence on humans that can result in loss of jobs or mass retrenchment of employees. Having a large population of unemployed people is harmful to economy of any country. Secondly, people without money will not be able to utilize most functions that are performed through AI, which will render the technology useless.

The advent and growing popularity of AI can be summarized in words of Bill Gates. According to the founder of Microsoft, AI will have a positive impact on people’s lives. In an interview with Fox Business, he said, people would have more spare time that would eventually lead to happier life. However he cautions, it would be long before AI starts making any significant impact on our daily activities and jobs.

Career in AI

Since AI primarily aims at making human life better, several companies are testing the technology. Global online retailer Amazon is one amongst these. Banks and financial institutions, service providers and several other industries are expected to jump on the AI bandwagon in 2018 and coming years. Hence, this is the right time to aim for a career in AI. Currently, there exists a great demand for AI professionals. Here, we look at the top six employment opportunities in Artificial Intelligence.

Computer Vision Research Engineer

 A Computer Vision Research Engineer’s work includes research and analysis, developing software and tools, and computer vision technologies. The primary role of this job is to ensure customer experience that equals human interaction.

Business Intelligence Engineer

As the job designation implies, the role of a Business Intelligence Engineer is to gather data from multiple functions performed by AI such as marketing and collecting payments. It also involves studying consumer patterns and bridging gaps that AI leaves.

Data Scientist

A posting for Data Scientist on recruitment website Indeed describes Data Scientist in these words: “ A mixture between a statistician, scientist, machine learning expert and engineer: someone who has the passion for building and improving Internet-scale products informed by data. The ideal candidate understands human behavior and knows what to look for in the data.

Research and Development Engineer (AI)

Research & Development Engineers are needed to find ways and means to improve functions performed through Artificial Intelligence. They research voice and text chat conversations conducted by bots or robotic intelligence with real-life persons to ensure there are no glitches. They also develop better solutions to eliminate the gap between human and AI interactions.

Machine Learning Specialist

The job of a Machine Learning Specialist is rather complex. They are required to study patterns such as the large-scale use of data, uploads, common words used in any language and how it can be incorporated into AI functions as well as analyzing and improving existing techniques.

Researchers

Researchers in AI is perhaps the best-paid lot. They are required to research into various aspects of AI in any organization. Their role involves researching usage patterns, AI responses, data analysis, data mining and research, linguistic differences based on demographics and almost every human function that AI is expected to perform.

As with any other field, there are several other designations available in AI. However, these will depend upon your geographic location. The best way to find the demand for any AI job is to look for good recruitment or job posting sites, especially those specific to your region.

In conclusion

Since AI is a technology that is gathering momentum, it will be some years before there is a flood of people who can be hired as fresher or expert in this field. Consequently, the demand for AI professionals is rather high. Median salaries these jobs mentioned above range between US$ 100,000 to US$ 150,000 per year.

However, before leaping into AI, it is advisable to find out what other qualifications are required by employers. As with any job, some companies need AI experts that hold specific engineering degrees combined with additional qualifications in IT and a certificate that states you hold the required AI training. Despite, this is the best time to make a career in the AI sector.

My Desk for Data Science

In my last post I anounced a blog parade about what a data scientist’s workplace might look like.

Here are some photos of my desk and my answers to the questions:

How many monitors do you use (or wish to have)?

I am mostly working at my desk in my office with a tower PC and three monitors.
I definitely need at least three monitors to work productively as a data scientist. Who does not know this: On the left monitor the data model is displayed, on the right monitor the data mapping and in the middle I do my work: programming the analysis scripts.

What hardware do you use? Apple? Dell? Lenovo? Others?

I am note an Apple guy. When I need to work mobile, I like to use ThinkPad notebooks. The ThinkPads are (in my experience) very robust and are therefore particularly good for mobile work. Besides, those notebooks look conservative and so I’m not sad if there comes a scratch on the notebook. However, I do not solve particularly challenging analysis tasks on a notebook, because I need my monitors for that.

Which OS do you use (or prefer)? MacOS, Linux, Windows? Virtual Machines?

As a data scientist, I have to be able to communicate well with my clients and they usually use Microsoft Windows as their operating system. I also use Windows as my main operating system. Of course, all our servers run on Linux Debian, but most of my tasks are done directly on Windows.
For some notebooks, I have set up a dual boot, because sometimes I need to start native Linux, for all other cases I work with virtual machines (Linux Ubuntu or Linux Mint).

What are your favorite databases, programming languages and tools?

I prefer the Microsoft SQL Server (T-SQL), C# and Python (pandas, numpy, scikit-learn). This is my world. But my customers are kings, therefore I am working with Postgre SQL, MongoDB, Neo4J, Tableau, Qlik Sense, Celonis and a lot more. I like to get used to new tools and technologies again and again. This is one of the benefits of being a data scientist.

Which data dou you analyze on your local hardware? Which in server clusters or clouds?

There have been few cases yet, where I analyzed really big data. In cases of analyzing big data we use horizontally scalable systems like Hadoop and Spark. But we also have customers analyzing middle-sized data (more than 10 TB but less than 100 TB) on one big server which is vertically scalable. Most of my customers just want to gather data to answer questions on not so big amounts of data. Everything less than 10TB we can do on a highend workstation.

If you use clouds, do you prefer Azure, AWS, Google oder others?

Microsoft Azure! I am used to tools provided by Microsoft and I think Azure is a well preconfigured cloud solution.

Where do you make your notes/memos/sketches. On paper or digital?

My calender is managed digital, because I just need to know everywhere what appointments I have. But my I prefer to wirte down my thoughts on paper and that´s why I have several paper-notebooks.

Now it is your turn: Join our Blog Parade!

So what does your workplace look like? Show your desk on your blog until 31/12/2017 and we will show a short introduction of your post here on the Data Science Blog!

 

Show your Data Science Workplace!

The job of a data scientist is often a mystery to outsiders. Of course, you do not really need much more than a medium-sized notebook to use data science methods for finding value in data. Nevertheless, data science workplaces can look so different and, let’s say, interesting. And that’s why I want to launch a blog parade – which I want to start with this article – where you as a Data Scientist or Data Engineer can show your workplace and explain what tools a data scientist in your opinion really needs.

I am very curious how many monitors you prefer, whether you use Apple, Dell, HP or Lenovo, MacOS, Linux or Windows, etc., etc. And of course, do you like a clean or messy desk?

What is a Blog Parade?

A blog parade is a call to blog owners to report on a specific topic. Everyone who participates in the blog parade, write on their blog a contribution to the topic. The organizer of the blog parade collects all the articles and will recap those articles in a short form together, of course with links to the articles.

How can I participate?

Write an article on your blog! Mention this blog parade here, show and explain your workplace (your desk with your technical equipment) in an article. If you’re missing your own blog, articles can also be posted directly to LinkedIn (LinkedIn has its own blogging feature that every LinkedIn member can use). Alternative – as a last resort – it would also be possible to send me your article with a photo about your workplace directly to: redaktion@data-science-blog.com.
Please make me aware of an article, via e-mail or with a comment (below) on this article.

Who can participate?

Any data scientist or anyone close to Data Science: Everyone concerned with topics such as data analytics, data engineering or data security. Please do not over-define data science here, but keep it in a nutshell, so that all professionals who manage and analyze data can join in with a clear conscience.

And yes, I will participate too. I will propably be the first who write an article about my workplace (I just need a new photo of my desk).

When does the article have to be finished?

By 31/12/2017, the article must have been published on your blog (or LinkedIn or wherever) and the release has to be reported to me.
But beware: Anyone who has previously written an article will also be linked earlier. After all, reporting on your article will take place immediately after I hear about it.
If you publish an artcile tomorrow, it will be shown the day after tomorrow here on the Data Science Blog.

What is in it for me to join?

Nothing! Except perhaps the fun factor of sharing your idea of ​​a nice desk for a data expert with others, so as to share creativity or a certain belief in what a data scientist needs.
Well and for bloggers: There is a great backlink from this data science blog for you 🙂

What should I write? What are the minimum requirements of content?

The article does not have to (but may be) particularly long. Anyway, here on this data science blog only a shortened version of your article will appear (with a link, of course).

Minimum requirments:

  • Show a photo (at least one!) of your workplace desk!
  • And tell us something about:
    • How many monitors do you use (or wish to have)?
    • What hardware do you use? Apple? Dell? Lenovo? Others?
    • Which OS do you use (or prefer)? MacOS, Linux, Windows? Virtual Machines?
    • What are your favorite databases, programming languages and tools? (e.g. Python, R, SAS, Postgre, Neo4J,…)
    • Which data dou you analyze on your local hardware? Which in server clusters or clouds?
    • If you use clouds, do you prefer Azure, AWS, Google oder others?
    • Where do you make your notes/memos/sketches. On paper or digital?

Not allowed:
Of course, please do not provide any information, which could endanger your company`s IT security.

Absolutly allowed:
Bringing some joke into the matter 🙂 We are happy to vote in the comments on the best or funniest desk for election, there may be also a winner later!


The resulting Blog Posts: https://data-science-blog.com/data-science-insights/show-your-desk/


 

The importance of domain knowledge – A healthcare data science perspective

Data scientists have (and need) many skills. They are frequently either former academic researchers or software engineers, with knowledge and skills in statistics, programming, machine learning, and many other domains of mathematics and computer science. These skills are general and allow data scientists to offer valuable services to almost any field. However, data scientists in some cases find themselves in industries they have relatively little knowledge of.

This is especially true in the healthcare field. In healthcare, there is an enormous amount of important clinical knowledge that might be relevant to a data scientist. It is unreasonable to expect a data scientist to not only have all of the skills typically required of a data scientist, but to also have all of the knowledge a medical professional may have.

Why is domain knowledge necessary?

This lack of domain knowledge, while perfectly understandable, can be a major barrier to healthcare data scientists. For one thing, it’s difficult to come up with project ideas in a domain that you don’t know much about. It can also be difficult to determine the type of data that may be helpful for a project – if you want to build a model to predict a health outcome (for example, whether a patient has or is likely to develop a gastrointestinal bleed), you need to know what types of variables might be related to this outcome so you can make sure to gather the right data.

Knowing the domain is useful not only for figuring out projects and how to approach them, but also for having rules of thumb for sanity checks on the data. Knowing how data is captured (is it hand-entered? Is it from machines that can give false readings for any number of reasons?) can help a data scientist with data cleaning and from going too far down the wrong path. It can also inform what true outliers are and which values might just be due to measurement error.

Often the most challenging part of building a machine learning model is feature engineering. Understanding clinical variables and how they relate to a health outcome is extremely important for this. Is a long history of high blood pressure important for predicting heart problems, or is only very recent history? How long a time horizon is considered ‘long’ or ‘short’ in this context? What other variables might be related to this health outcome? Knowing the domain can help direct the data exploration and greatly speed (and enhance) the feature engineering process.

Once features are generated, knowing what relationships between variables are plausible helps for basic sanity checks. If you’re finding the best predictor of hospitalization is the patient’s eye color, this might indicate an issue with your code. Being able to glance at the outcome of a model and determine if they make sense goes a long way for quality assurance of any analytical work.

Finally, one of the biggest reasons a strong understanding of the data is important is because you have to interpret the results of analyses and modeling work. Knowing what results are important and which are trivial is important for the presentation and communication of results. An analysis that determines there is a strong relationship between age and mortality is probably well-known to clinicians, while weaker but more surprising associations may be of more use. It’s also important to know what results are actionable. An analysis that finds that patients who are elderly are likely to end up hospitalized is less useful for trying to determine the best way to reduce hospitalizations (at least, without further context).

How do you get domain knowledge?

In some industries, such as tech, it’s fairly easy and straightforward to see an end-user’s prospective. By simply viewing a website or piece of software from the user’s point of view, a data scientist can gain a lot of the needed context and background knowledge needed to understand where their data is coming from and how their model output is being used. In the healthcare industry, it’s more difficult. A data scientist can’t easily choose to go through med school or the experience of being treated for a chronic illness. This means there is no easy single answer to where to gain domain knowledge. However, there are many avenues available.

Reading literature and attending presentations can boost one’s domain knowledge. However, it’s often difficult to find resources that are penetrable for someone who is not already a clinician. To gain deep knowledge, one needs to be steeped in the topic. One important avenue to doing this is through the establishment of good relationships with clinicians. Clinicians can be powerful allies that can help point you in the right direction for understanding your data, and simply by chatting with them you can gain important insights. They can also help you visit the clinics or practices to interact with the people that perform the procedures or even watch the procedures being done. At Fresenius Medical Care, where I work, members of my team regularly visit clinics. I have in the last year visited one of our dialysis clinics, a nephrology practice, and a vascular care unit. These experiences have been invaluable to me in developing my knowledge of the treatment of chronic illnesses.

In conclusion, it is crucial for data scientists to acquire basic familiarity in the field they are working in and in being part of collaborative teams that include people who are technically knowledgeable in the field they work in. This said, acquiring even an essential understanding (such as “Medicine 101”) may go a long way for the data scientists in being able to become self-sufficient in essential feature selection and design.