The data science and AI industry are increasingly becoming competitive with more professionals looking out for a transition into the data science domain. With a plethora of courses available online and offline, it has become easier than ever to make a transition. A study by Analytics India Magazine suggests that there has been significant growth in the number of jobs in the analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. This means that there are more professionals looking out to explore this ever-growing field.

With the increasing number of professionals in the field, there had never been a need to stand out from the crowd like today. With a lot of professionals learning skills on-the-go, there is now a need to demonstrate an exceptional capability as a data scientist to be valued by the organizations. 

Hence comes the need to be not just a data scientist but ‘Chartered’ data scientist (CDS) to achieve the highest distinction in the profession. 

Who Is A Chartered Data Scientist™?

To help you stand out from the crowd in terms of knowledge and skills as per the latest international standards, the Association of Data Scientists (ADaSci) has brought an opportunity for you to earn the CDS credential. It ensures that the candidate has a strong understanding of the advanced data science profession and in-depth understanding of applied analytics skills. 

Being a CDS would mean that the candidate is potentially better than other data science candidates not just in terms of skills and knowledge but the connections that you would be making with the elite network of expert data scientists, working in leading institutions. 

Having set a global standard for data science, the CDS program has been developed by the world’s leading data science practitioners. Attaining the title means that candidate has the mastery of the skills and knowledge needed to help organisations succeed in today’s rapidly changing global landscape. The curriculum of this program has been developed by distinguished professionals and academics of diverse backgrounds and is updated annually. 

By achieving the CDS certification, the candidate indicates their extraordinaire to the potential employers. It would provide an edge in career and personal development.

Ethics Is The Foundation Of CDS™

Being a CDS means that the candidate is required to follow the guidelines and ethical standards set as a benchmark for data scientists. As a CDS program candidate, they are required to follow code and standards set by the committee. The guidelines and ethics for Chartered Data Scientists (CDS) abide them by duties to own profession, employer, clients, industry and due as a CDS charter. 

Benefits Of Becoming A CDS™

• Proven-ability in data science skills: Having a CDS designation is a key indication that the candidate has the cutting edge knowledge of the latest tools and technologies of the data science field. 
• Has the required experience: A highly practitioner-driven exam, earning the CDS designation demonstrates to the future employers that the candidate is skilled enough to master complex real-world challenges.
• Improves employment opportunity: It exposes you to better employment opportunities as the skills will be on par with the highest industry standards.
• Specialised knowledge: It ensures a specialised knowledge and understanding of the data science tools and techniques.
• Makes you stand out from the crowd: With data science becoming highly competitive, the title of CDC means that it will attract a preference to your resume from all across the industry. 

Who Should Consider?

• Any professional who is looking to broaden their knowledge in the field of data science, analytics and artificial intelligence should consider taking the CDS exam. Whether starting out in a data science career or are already established in the field, the CDS curriculum exposes the candidate to the major strategic aspects of data science. 
• Anyone who is keen on being apprised to the latest trends in data science and is looking to challenge themselves by testing their knowledge against an elite pool of data scientists from around the world. 
• It is also highly recommended for those looking for a career change into the data science domain as it will provide the best-in-class knowledge in the field. It will help in developing specialised and practical knowledge that can be applied in the industry. 
• Students with an interest in data science could also consider the CDS exam during or immediately after completion of their studies. The CDS curriculum can complement their prior coursework or help them develop a foundation of specialized knowledge that goes beyond their academic curriculum.

How To Become A CDS™?

The CDS program is a self-study program culminating in an exam, passing which brings you a step closer to becoming a CDS charter holder.

Becoming a CDS requires passing a rigorous exam that has been designed by the experienced data science professionals from across the globe. Candidates can enrol for the program by registering for the CDS exam online via ADaSci website.

The exam which can be taken any time throughout the year requires the candidate to achieve a minimum passing score. Apart from passing the exam, the candidate must also demonstrate a minimum of two years of full-time work experience in data science or a related field to complete the certification. 

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Artificial intelligence can help make medicine more efficient and safer. China is a forerunner in this field; we must not miss the boat, says Walter Karlen.

A few weeks ago, I turned to the new Swiss TV show “Ärzte vs. Internet” (Physicians vs. internet). On the show, two teams duel to correctly diagnose patients. I was very surprised to see that they were both human teams. The answers of the “internet” team were obviously only as good as the search terms they used to access information, and the logical deductions developed by humans.

But why didn’t the physicians compete against a machine? In emerging countries such as China where there is an acute shortage of trained doctors, “Doctor vs. machine” competitions are very popular. This is illustrated by the Chinese TV broadcast of the brain tumour diagnosis and progression prediction competition between a team of 25 expert doctors against the Biomind artificial intelligence (AI) system. The 2:0 win of the AI over the humans in analysing brain images gained high visibility in China, but went almost unnoticed in our regions.

I’m convinced that the clinicians in “Ärzte vs. Internet” routinely use clinical decision support (CDS) systems in their daily practice, whether consciously or not. In fact, decision support systems are already present in many health applications, usually in a very simple form. For example, every device monitoring a patient’s condition will produce an alarm to alert health care staff; in telemedical and emergency services, CDS assist phone operators in triaging callers; medication supply chain management rely on an automated analysis of stock and prediction of demand, etc.

China’s dominance

Since the idea of using computers to improve medical services was introduced over 50 years ago, CDS has gone through regular cycles of hype and disillusionment where developments seem to stagnate. Most of these are regional, as is currently the case: whereas the recent layoffs at IBM Watson Health illustrate the struggle of Western companies with AI in medicine, the Chinese are celebrating achievements of medical AI companies, such as robots passing medical exams, and implementation into entire hospital systems, as recently announced by Guangzhou Second Provincial Central Hospital where surgery supplies are automatically dispatched and patients pre-screened.

The benefits of CDS are evident. Humans, including trained clinicians, are poor performers when it comes to assessing many data dimensions at the same time: we do not integrate data well and tend to interpret negatively. On the other hand, in their current form, machines are still poor in interpreting contextual situations and dealing with uncertain situations, and therefore dependent on access to high quality and quantity of data. Humans make errors, and so will the machines. In general, if identified early, a good clinician can redress a mistake. Current, non-AI based CDS do not have the capacity to predict intervention outcomes; this is the main reason these systems operate in an open loop where clinicians are the central part of the loop.

More accurate and safer

While in the past many systems and approaches have been explored in an academic environment, their adoption into medical practice has been very slow. The main reason being that neither health systems nor humans were ready for it. After all, like other medical devices, CDS need an appropriate design: they need to fulfill the high medical standards and performance requirement, fit into local cultural, ethical, regulatory, and organisational settings, be cost effective, and have a sustainable business model. This will also hold true for the new AI-based generation of CDS which may indeed have the capacity to deliver more accurate diagnoses than humans.

The inherent goal of automation in health care, however, is more profound: to improve the quality and safety of services. Medicine has already an established safety culture and is consequently ready for better and safer systems. If we can demonstrate that automated systems consistently increase patient safety, then any discussion of if and when CDS should be used to replace doctors is unnecessary.

No personalized medicine without AI

The harsh reality is that without CDS and intelligent agents, our health system will simply grind to a halt. The staff in intensive care units are already overwhelmed with biosignals to interpret and alarms to silence; very soon, this burden will reach other specialist and general practitioners who will be bombarded with data from wearables, genetic tests and other biomarker results that must all be integrated into a diagnosis, and treatment decisions made within 10 to 30 minutes of meeting a patient. We can’t miss this opportunity for automation if we want to make personalised medicine a reality. This data will have to be processed, classified, and analysed by automated algorithms.

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