The world of data science and machine learning is a fascinating one, both for the incredible innovations and for how deeply they affect everyday happenings around us. Businesses employ and basically rely on these methodologies to help them analyze their data and make decisions that directly affect their bottom line. Data scientists can spot trends in all kinds of information, even pinpointing commonalities among successful writers. 

It’s an awesome job, no doubt, and one that has a bright future ahead. If you’re even remotely interested in the industry, check out the Machine Learning & Data Science Certification Training Bundle. It’s the perfect way to master important concepts and either start or strengthen your position in the field – and you can now get this course pack for 97% off. It’s safe to say $35 is a steal for eight courses covering Python, R, TensorFlow, Keras and more. 

Consider this your guide to machine and deep learning, with an intro using Google’s TensorFlow and Keras frameworks in Python. Get all the intel you need to understand the basics of NumPy and Pandas (which will be something you’ll actually understand after taking these courses). As you work through the courses, you’ll learn how to create artificial neural networks and data visualizations – pretty impressive skills for someone who’s only taking 48 hours of coursework! 

This highly rated bundle can set you apart in the workforce. Case in point: the class pack includes lessons on mastering R for practical data science. Learn how to assess data from different sources in the R environment, plus, learn the basics of using R Studio. The skills you’ll gain, like learning which algorithms work best for different types of data, could help you boost your company’s performance. 

Whether you’re gunning for a promotion or looking for a new gig, this deal could be a game changer. Right now, you can get the Machine Learning & Data Science Certification Training Bundle for $35, down 97% from the original MSRP.

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To be a modern-day engineer, being a domain-specific expert is just not enough. Data Science helps engineers to make better decisions at work and for organisations at large.

Why Should Companies Be Wary Of Shadow Data?

Every year, India produces over 1.5 million engineering graduates. These are disproportionately high numbers especially considering the appropriate opportunities available. To say that the competition in the job market is cutthroat, would be an understatement. In such a situation, it becomes imperative that students skill up and get that extra edge. 

Data science, apart from being a booming industry, is also a field that is interesting and challenging. Being trained in data science alongside regular engineering opens several doors of opportunity. Analytics India Magazine caught up with Dr Dakshinamurthy V Kolluru, Founder and President, INSOFE to understand more about the scope and opportunities. Dr Kolluru speaks about the engineering and data science dual degree offered by INSOFE which will equip aspirants with skills of today’s world.

AIM: How relevant are core engineers with data science specialisation in modern-day organisations?

Dr Kolluru: To be a modern-day engineer, being a domain-specific expert is just not enough. Organisations expect you to also have an understanding of data-driven innovation across various phases of engineering. Data Science helps engineers to make better decisions at work and for organisations at large.

An engineer’s role involves efficient design, development, execution, and maintenance of systems. This calls for an in-depth analysis of a variety of dynamic factors. Data Science, on the other hand, relies extensively on building a rationale around engineering endeavours. It involves collecting data on several factors affecting a product/problem, assessing the trends, analysing correlations between them, and predicting how they will change over time. 

Using the principles of data correlation and change predictions, engineers can come up with superior designs, build better products, and run systems more efficiently than ever before!  

AIM: Could you give us some examples of such applications in the real world?

Dr Kolluru: Automobile Industry

Industries such as Transportation and Automobiles depend on Artificial Intelligence and Data Science to build components with precision. Industry leaders such as GE Transportation have envisaged brilliant factories that rely extensively on Machine Intelligence. They have arrived at digitising the processes and reducing the downtime in maintenance. 

From Tool design standardisation, predicting wear and tear of moving parts to visualising exceptional malfunctions, Data Science is being adopted seamlessly. In addition, Artificial Intelligence is used for state-of-the-art innovations like aerodynamic design and autonomous navigation. 

With innovations and developments in data science applications engineers have been able to design superior solutions, which couldn’t be thought of earlier.

Healthcare providers are using statistical simulations of clinical trials to pre-test the efficacy of the drugs. Machine Learning algorithms are being utilised by the healthcare industry to identify different chemical combinations that can be used in drug discovery. Adopting machine intelligence to arrive at the most suitable combinations, companies are now able to come up with efficient drugs. These principles have sped up the drug development process and enhanced its efficiency.

Hospitals and doctors are now adopting machine learning models for prognosis, diagnosis, and treatment of chronic ailments like diabetes and terminal diseases like cancer.

AIM: How did INSOFE come up with the dual specialisation master’s programs?

Dr Kolluru: INSOFE is a world-class institute that is imparting quality data science education. To help students break into the heart of Engineering through their data science expertise, INSOFE has collaborated with top universities to offer dual specialization programs. 

Case Western Reserve University, USA and University of Strathclyde, UK are two such universities that have the finest engineering departments in the world. Case Western Reserve University in particular has been at the forefront of integrating engineering with various domains. Their work in Biomedical Engineering is an apt example of such innovation. 

The curriculum of the dual master’s degree is designed to ensure that students master data science and its implementation in different engineering domains. INSOFE’s philosophy is to extend the student’s expertise to research, product development, and new-age engineering in their core discipline. 

We strongly believe that such partnerships provide an opportunity for the student to study at some of the world-class universities, enhance student learning through multi-cultural interactions, and be mentored by renowned professors. 

AIM: What dual specialisations are offered through INSOFE and can students apply to such options in other universities?

Dr Kolluru: Case Western Reserve University and INSOFE offer Dual Specialization Masters programs in three engineering disciplines. The broad specializations include Biomedical Engineering with Translational Health Technology, Mechanical Engineering with AI and Robotics in addition to Civil, and Environmental Engineering with AI and Machine Learning. 

Students will complete the data science specific credits at INSOFE campuses in India and the latter part of the course work would be completed at Case School of Engineering, Cleveland – OH, USA.  

These programs are offered only under the Case Western – INSOFE collaboration. This option is not available for students undertaking normal courses at the university. The competitive examination scores like GRE / TOEFL / IELTS are waived off for students applying to the programs under this collaboration. However, the students have to clear INSOFE Entrance Exam (INEX) as a pre-qualification to apply for these programs. Eligible students also stand a chance to receive a scholarship of up to 25% of the tuition fee.  

AIM: How are opportunities different in the US compared to in India for students graduating in such programs?

Dr Kolluru: This is a STEM graduate program, students will be eligible to work in the USA upon the completion of the degree. The research and manufacturing focus of companies in the USA provides opportunities that require data science skills in addition to engineering expertise.

The dual specialization masters help students extend their reach beyond IT companies as they are equipped to work across industries like automobile, healthcare, petroleum, pharmaceuticals, and manufacturing. They have an added advantage over a regular engineering graduate since a greater number of organisations are looking for engineers with data science skills.  

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An expert in machine learning and natural language processing discusses how these technologies are enhancing care and enabling the use of SDOH data and personalized analytics.

Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways.

A group’s aim could be to improve a patient’s care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services.

Healthcare IT News talked over these ideas with Niall O’Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.

Q: How is machine learning being used to comprehensively enhance a patient’s entire care plan based on personalized analytics? And how is machine learning being used to combine evidence-based care paths – with real-world historical utilization, outcomes and the latest literature – to provide first-rate patient care?

A: Evidence-based guidelines are an important component of an intelligent care path solution. In fact, they are the starting point for our models. We would never want to relearn the complexity that has been elucidated in clinical guidelines.

At the same time, guidelines were written for the average patient and can’t possibly accommodate all the comorbidity permutations that exist for patients of high acuity. This is where machine learning can help. For patients that don’t perfectly fit existing evidence-based care paths, we can employ machine learning models to infer what has been the most efficacious path for diagnostically identical patients from real world historical data.

Q: How is machine learning being used to use social determinants of health and patient lifestyle to provide precise and clinically meaningful care?

A: Data regarding social determinants of health (SDOH) and patient lifestyle are not typically captured in standard electronic health records, but diligent physicians typically refer to this type of data in their clinical notes.

We can also supplement models with SDOH data – such as the U.S. Census – that can point to access or other patient challenges and incorporate patient-reported data, whether on lifestyle or health state.

This presents a challenge for typical analysis, so we employ natural language processing to help isolate and interpret references to things like lifestyle impacts or resumption of employment following surgery. Although detection of these phrases isn’t comprehensive, when present they can help provide valuable outcome endpoints for us.

Q: How did machine learning first come to be seen as useful in these areas?

A: Clinical data analysis isn’t a big data problem; it’s a messy data problem and is plagued by the fact that much of the valuable information isn’t readily available in structured form.

Volume also plays a big part in why we use machine learning; when we end up with thousands of attributes, we need machine learning to identify the variables that are driving the model. For SDOH in particular, machine learning will be crucial for variable selection and essential to refining some signal from the noise of operational clinical data.

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Brandon Wirtz was supposed to be a fifth-generation teacher. Indeed, the founder and CEO of artificial intelligence engine developer Recognant is a teacher — of robots, not people — and not the factory floor variety of bots, either. Instead, Wirtz sees AI changing a very human process: human resources.

To reach the place where artificial intelligence and HR meet, Wirtz spends his days educating his various AIs about everything from how to order pizza to what an appropriate pickup line might be. His bots — “Loki,” “Lobby” and “Molly” — are at different stages of independence and aptitude. Loki, who identifies as female, is perhaps Wirtz’s favorite bot — and the most likely to drive him crazy with questions.

The games Wirtz plays with Loki — “I Spy” is a particular favorite — might seem frivolous, but they serve as the basis for the bot’s education in how humans think and communicate. And though it may seem unimportant that Loki understands Santa Claus, zombies and Instagram, all of that matters when it comes to artificial intelligence and human resources if she — or a bot like her — is going to work in HR dealing with prospective employees. “I know this seems creepy, but it isn’t,” Wirtz laughed.

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