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Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

How Computer Vision Is Changing Healthcare

It may seem like every new decade we have a new technology to master. But what if we’ve flipped the script? Now AI has a new technology to master. I'm talking about Computer Vision. Just like humans learn to identify shapes into objects as children, so too, must the technologies we’ve created.  Why? Because autonomous vehicles need to know the difference between a tree and a person holding their grocery bags. Because manufacturing bots need to identify defective products before they go to the public. And in healthcare, Computer Vision can help us identify disease, help doctors make diagnoses, and dig deeper into what makes humans human. Three Trends to Watch  Already, systems have a 99% accuracy rate at emulating human sight. Like our own calculations when we “see” an object, machines will have to process, analyze, and understand the image as well. Thanks to Machine Learning and Neural Networks using pattern recognition, this is possible. What could this mean for the healthcare industry? Imaging Devices like X-Rays and MRI Machines will get smaller and more mobile. This trend will allow simpler imaging, quicker workflows, and live imaging for quicker diagnoses.Next Generation Phenotyping (NGP) allows predictive diagnoses using Computer Vision and Deep Learning to analyze data at the molecular level. Telemedicine to open greater access to your doctor rather than the traditional brick-and-mortar doctor’s office visit. Electronic Health Records (EHR) for a patient profile gives direct access to patient information and could reduce the cost of logistics and gaps in expertise. And Remote Patient Monitoring (RPM) allows for real-time medical decisions to flow between patient-doctor without the ubiquitous red tape traditional medicine brings. Recent advancements in visual technologies will have a strong impact in a variety of industries. But it’s in the healthcare industry, Computer Vision, AI, and IoT will particularly shine as the technologies converge for greater progress in healthcare.  AIoT and Image-Based Data Converge for Improved Outcomes  There are such a variety of uses for Computer Vision in medicine, it can be hard to imagine where it can't be used. When you consider how much medical data is image-based such as mammograms, MRIs, CT Scans, X-Rays, and Echocardiograms, it’s easy to see how patients will benefit.  Imagine getting an early diagnosis to stop the spread of cancer or stopping dementia in its tracks. These systems alone can assist with surgery, identify problems early, and more. When your medical team of institutions, providers, and patients have access to these systems and truly partner, then this becomes the future of healthcare.    Add to improvements in computer vision, the rapidly advancing technologies of AI, and IoT and watch how quickly problem-solving scenario outcomes improve across all industries. Much like the last convergence of mobile phones and the internet, AIoT will usher in a new era of human history in similar fashion. Risk and Reward of AIoT, ML, and Computer Vision With greater advancement, comes greater risk and reward. As sensors and connectivity multiply across devices and industries, renewed focus should include privacy and security. Such large volumes of Data, even within the healthcare industry, can be targets for hackers as well as government entities. It may seem strange to consider this in the light of the healthcare vertical, but imagine the repercussions of denials due to medical issues or the inverse of identity theft.  The convergence of AIoT and Computer Vision technologies use complex algorithms for predictive analytics. Add Machine Learning into the mix and watch workflows streamline, simplified problem-solving unfold, and improved reliability and sustainability of data capture and how it can enhance an organization’s processes.  In the cumbersome world of healthcare and its institutions, Computer Vision, AI, IoT, and Machine Learning offer a simpatico balance between patient and provider that flips traditional healthcare upside down. Advancements within the last few years and in the coming decade are primed to bridge the gap between patient and provider. But it’s going to need Data professionals who have a passion for the industry and can guide these technologies to the next stages in their development. The Computer Vision industry is supercharged and is expected to reach $48.6 billion by 2022. Ready to see where the latest technologies can take you? If you’re interested in Computer Vision, Big Data, and Analytics, Robotics, and more, we may have a role for you.  Check out our current vacancies or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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