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International team publishes perspective paper on role of the clinician-data-scientist in healthcare – EurekAlert

October 12, 2022 | Author: admin

Healthcare is constantly changing, as big data analytics and advanced technologies such as artificial intelligence are now being applied in the healthcare field. These high-tech changes hold the potential to transform patient care. In response to these changes, an international team of scientists has published a perspective paper, describing the competencies of clinician-data-scientists and addressing the challenges in training these health care professionals. Their perspective paper was published in the journal Health Data Science on August 8, 2022.

The clinician-data-scientist combines in-depth clinical knowledge with skills in data science. These healthcare professionals are well prepared to identify the challenges in healthcare that accompany the growing digital transformation. They are able to lead meaningful scientific studies, communicate well across disciplines, and provide general critical interpretations. With their interdisciplinary knowledge, these clinician-data-scientists will play crucial roles in guiding the approvals for innovative technologies and creating digital health policy.

In a digital health era, clinician-data-scientists are critically important as they possess a deep understanding of both data science and the humanistic nature of medicine, and are ready to identify clinically important questions, said Luxia Zhang, a professor at the National Institute of Health Data Science, Peking University.

In their perspective paper, the researchers explore the core competencies a clinician-data-scientist needs to have, stressing that these individuals must be able to closely link medicine and data science to work efficiently and to enable data-driven discoveries in healthcare.

The core competencies of a clinician-data-scientist should include a fundamental understanding of health data, training in epidemiology, statistics, bioinformatics, and computer science, combined with an understanding of continuous healthcare improvement frameworks, socio-technical system challenges, and advanced skills in inter-disciplinary communication and collaboration, said Dr. Mai Wang, from the National Institute of Health Data Science, Peking University. The researchers believe that with a strong understanding of healthcare and the ability to identify knowledge gaps in medical practice, clinician-data-scientists will play critical roles in data science research projects.

Besides exploring the core competencies a clinician-data-scientist needs, the researchers also examined the training these health care professionals require. This training is challenging because of the increased complexity of data and the rapid advancement of analytic techniques. As an added challenge, data science is not part of conventional medical education training. While some medical schools are starting to modify their curriculum, overall, integrated formal training programs for clinician-data-scientists are scarce worldwide.

The researchers view teamwork as key to this process. To conquer training challenges, senior clinical faculties and data scientists should form a close partnership and work together to design training frameworks with flexibility and frequent updating for adaptation to various application scenarios, said Wang.

Looking to the future, the researchers stress that clinician data scientists are key team members in patient health care. Clinicians need training in data science skills. At same time, data scientists with deep technical skills are needed, so that key clinical questions are formed and prioritized.

Clinician-data-scientists are critically important as they possess a deep understanding in both science and humanistic nature of medicine and are ready to identify clinically important questions that, if addressed, can make medical advances and assure excellence in patient care, said Zhang.

The next step is to form a training framework and design curricula that focuses on the core competencies required by clinician-data-scientists. And this must be continuously updated to adapt to new developments, said Zhang.

The research team includes Fulin Wang, Lin Ma, Mai Wang from Peking University Health Science Center; Georgina Moulton from The University of Manchester; and Luxia Zhang, from Peking University and Peking University First Hospital.

Health Data Science

Clinician Data ScientistsPreparing for the Future of Medicine in the Digital World

3-Oct-2022

The authors declare that they have no conflicts of interest.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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International team publishes perspective paper on role of the clinician-data-scientist in healthcare - EurekAlert

Energy Transitions: We Need To Redefine the Problem and Reframe the Narrative – Data Science Central

October 12, 2022 | Author: admin

Climate Change, as an extension or corollary Energy Transitions, is undoubtedly one of the most critical issues that merit urgent and serious attention from policymakers, scientists, and governments across the globe. However, before looking for solutions, it is equally important to define and frame the problem in the most realistic and unbiased way to ensure the holistic nature of the solution(s). There are many issues with how the problem of energy transitions is being portrayed lately. We are already against the clock. False narratives will only exacerbate the climate crisis by delays and inconsistencies inherent in the proposed solutions.

The starting point is in defining the word itself. As Vaclav Smil, energy doyen of the 21stcentury, puts it (in his book titled Energy Transitions), using transition (singular) is not correct as it is almost equivalent to proposing a linear modernization narrative in terms of energy. Replacing it with transitions depicts that we understand the path dependency of energy transitions and realize that different countries and regions will have to define their ways for Net Zero. We should problematize the linear modernization narrative. This also relates to the idea of the Anthropocene, the proposed period when the human activity started to affect our ecosystem adversely. The problem with using this term is that it assumes humanity is a collective and the subsequent carbon emissions absolve thosewhose contribution weighs much more than others. However, in reality, there exists an asymmetry. For example, energy usage in the British Empire or the U.S. cannot be compared to that in South Asia. The asymmetry persists even to this date.

The United States and China account forabout 44 percentof CO2 emissions, albeit China emits more than 27 percentof global CO2 emissions. However, the U.S. leads the way on a per capita basis important as the U.S. population is 4.35times less than Chinas! On an aggregate basis, the U.S. has emitted more than China over the past 300 years and has the largest historical emissions(20 percent of the global total). The emission discrepancy between the richest and poorest, developed and undeveloped, is shocking. The graph below illustrates this, showing energy usage per person.

A report by Oxfam, Carbon Inequality in 2030, mentions that the emissions by top 1 percent are expected to be 30 percent higher than what is required to attain 1.5C by 2030. Moreover, by 2030, the emissions by the top 1 percent roughly 80 million people will be 25 percent higher versus 1990. Showing no sign of abatement. Another recent report by U.N.stresses that the richest need to cut down their carbon footprint by 97 percent and that the top 1 percent almost 70 million people are responsible for 15 percent of total emissions, which is more than the bottom 50 percent 3.5 billion people!

The Oxfam report highlights a startling observation. The top 1 percent need to cut their emissions by 97 percent between 2015 2030, whereas they are set to cut only 5 percent in reality. The poorest 50 percent will register an increase of only 17 percent when even a 200 percent increase will still render their emissions compatible with the 1.5 C goal!

These facts and figures speak volumes about our current definition of the problem and its narrative. First of all, techno-determinism (reliance on technology) will not help us achieve green growth (see graph below). We need decades (or a century) for these technologies to achieve the scale to have an impact. Also, how will the issue of financing be resolved for the developing world? Furthermore, how can the developing world wean itself off coal when energy and lives depend on its usage?

The current Energy Crisis in Europe has the developed world scrambling to secure coal. In developing countries where about 2.7 billionpoor people still rely on biomass for cooking and where 1 billion people still live without electricity, the expectation that they will shift to renewable energies (with inherent limitations) is wishful thinking. The boilerplate solutions will not work for a significant part of the world, and this is crucial as, according to EIA,energy demand will increase by 50 percent by 2050, with the most demand coming from Non-OECD countries.

Instead of homogenizing the problem, we need to redefine it by focusing on those 70 million people or those 100 companies responsible for 71 percent of emissions. We need a new narrative that doesnt pitch humanity as a collective but as certain regions, countries, groups, and entities and hold them accountable. We need, therefore, tailored solutions. The matter is urgent. As per our current rate of emissions, only 22 yearsare left before we the world, reach 1.75 degrees.

The sooner we redefine the problem and reframe the narrative, the better we move toward practical, viable, holistic solutions.

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Energy Transitions: We Need To Redefine the Problem and Reframe the Narrative - Data Science Central

University joins national effort to make health care data more inclusive – University of Miami: News@theU

October 12, 2022 | Author: admin

Leaders from the Universitys Institute for Data Science and Computing are working to build a larger, more diverse database for more accurate research on health care disparities.

While kidney disease affects one in seven adults in the United States, it impacts Black patients much more. They are four times more likely to suffer from kidney failure than white U.S. residents, and a 2021 study indicates that computer algorithms that determine eligibility for a kidney transplant often put Black people at a disadvantage, widening the gap for a successful recovery.

This is just one example of how artificial intelligence solutions and algorithms for data collection need to improve, so that other health disparities impacting minority populationsin diabetes, heart disease, and cancer caredo not continue to increase in the United States.

Recognizing this problem, in July the National Institutes of Health (NIH) began the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity program, or AIM-AHEAD, with a goal to reduce these health disparities by creating new algorithms and health care databases that more accurately reflect the diverse U.S. population. They selected the University of Miamis Institute for Data Science and Computing (IDSC) as one of the institutions to spearhead the programs infrastructure core, which is one of four pillars of the program. The others are partnerships, research, and data science training.

Most of our current data is biased, and often the people collecting this data are not representative of all minorities and cultural differences, said Nick Tsinoremas, the Universitys vice provost of data science and computing, professor of biochemistry and molecular biology, as well as the founding director of IDSC and lead investigator on the AIM-AHEAD grant. We want these biases eliminated, but we also want to create an infrastructure that encourages minority serving institutions to do this research because these are the people who understand biases in data and algorithms and know how to create more equitable or unbiased approaches.

As a result, the University received a $1.3 million grant to work with historically Black colleges and universities, tribal colleges, and other minority serving institutions to create a computing structure for these institutions to share patient data that is void of personal information and that will improve the quality and breadth of health care research.

Joining Tsinoremas to lead the AIM-AHEAD project are Azizi Seixas, associate professor of psychiatry at the Miller School of Medicine and director of IDSCs population health informatics program, as well as computer science professor Yelena Yesha, who also serves as the Knight Foundation chair of data science and artificial intelligence and IDSCs chief innovation officer. The three are now working with minority serving institutions across Florida to improve their access to artificial intelligence tools and help them to do more efficient health equity research. Seixas said he and his researchers at the Miller Schools Media and Innovation Lab have reached out to approximately 15 institutions across the state.

Many of these institutions have fragmented electronic health record systems, which can lead to waste, inefficiencies, and poor communication in health care delivery, which is critical. Well focus on trying to bring these fragmented systems together, said Seixas, who is also the associate director of the Center for Translational Sleep and Circadian Sciences. If we are really serious about tackling the health disparities in Florida that make us uniquelike the high prevalence of cardiovascular disease and dementiawe need to build a larger network to really unravel those issues.

On the AIM-AHEAD infrastructure team, Tsinoremas, Seixas, and Yesha are also working with the National Alliance for Disparities in Public Health, Harvard University, and Vanderbilt University. Currently, University leaders are working closely with four institutions in Florida to create pilot programs, and recently received additional funding to support two of these pilot programs.

The team is now part of an additional $500,000 grant to work with Florida Atlantic Universitys (FAU) Schmidt College of Medicine and the Caridad Center, Inc.the largest free clinic in Florida for uninsured and underserved children and families of Palm Beach Countyto improve their electronic health records, so that they can be used more often for research.

In terms of technology infrastructure, security, cloud computing, and supercomputing, we have some of the best faculty and staff working at the University of Miami. So, we are able to do more as far as research productivity and output. But if we look just up the road to many of our minority serving institutions, they dont have those infrastructures, said Seixas. We are trying to spread our resources around, so these institutions that are often under-resourced can also do cutting-edge research and provide top of the line care, too.

As a first step, leaders from IDSC are sharing a tool to collect and de-identify patient electronic health records for research that they developed a few years ago for the Miller School. The tool, called University Research Informatics Data Environment, or URIDE, is a web-based platform that collects, sorts, and helps researchers visualize this type of patient data from multiple clinical health systems. This could help health care professionals explore demographics, diagnoses, procedures, vital signs, medications, labs, allergies, co-morbidities, and other information for certain patient populations to pinpoint trends or optimal treatment practices.

A second award for $362,000 will help IDSC leaders create another pilot program with Florida Memorial University (FMU) and Miami-Dade College to train 40 existing faculty members and students to use artificial intelligence and machine-learning techniques in their clinical practice, research, and curriculum. In this program, IDSC leaders will also build upon another program they created at the University to attract and foster the careers of underrepresented minorities in science and especially in the burgeoning field of data science.

We are very excited to collaborate with FAU, Caridad, FMU, and Miami-Dade College to expand the URIDE platform and to become an important piece of the infrastructure that supports the entire AIM-AHEAD consortium, Tsinoremas said.We also want to establish an open-source community around this platform to engage data scientists, data engineers, and developers for continuous improvement of this open-source effort, which we call Hi-RiDEfor Health Informatics Research Integrated Data Environment.

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University joins national effort to make health care data more inclusive - University of Miami: News@theU

Aboitiz Data Innovation appoints new GM to lead data science and AI adoption for smart cities – ETCIO South East Asia

October 12, 2022 | Author: admin

Alvin Ng, COO & General Manager - Smart Cities, APAC, Aboitiz Data InnovationAboitiz Data Innovation (ADI) officially announced the appointment of Alvin Ng to the role of General Manager for Smart Cities, playing a key role in advancing ADIs mission to drive the adoption of Data Science and Artificial Intelligence (DSAI) tools and solutions for smart city development in the region.

Formerly the APAC Vice President of Digital Solutions at Johnson Controls, a global company providing integrated smart green building systems, services and solutions, Ng oversaw the companys advancement towards digital and sustainable growth in the region. Prior to joining Johnson Controls, he was the General Manager of GE Digital, where he was responsible for leading GE Digital's automated industrial and data-driven software solutions to drive real-time connectivity and data intelligence in Asia.

Commenting on his new position, Ng said, I am thrilled to join ADI, a company that is pushing out innovative solutions for a better future, while advancing communities throughout Southeast Asia. With ADIs goals to strengthen its footing in the development of smart cities, I look forward to bringing my own experience and passion for digital transformation and advanced technologies such as IoT and AI to the table, while working alongside a talented team.

Ng joins ADI with over 25 years of experience in sales management, global business and market development. He has held various global leadership roles in multinational companies such as Wincor Nixdorf AG, Cisco Systems and Rosenbluth International. He is also a founding member of World Economic Forums Digital ASEAN Skills Task Force.

Today, he serves as an Adjunct Associate Professor at Nanyang Business School (NBS), part of Singapores Nanyang Technological University (NTU), teaching Digital Transformation, AI for business, Internet of Things and Sustainability Leadership programs. He is also closely involved with the Nanyang MBA as an Industry-Faculty Lead for the schools Business Consulting and Leader as a Coach programmes and plays the role of Senior Career Fellow for the Global Executive and Full-time MBA students.

Commenting on Alvins new position, ADI Managing Director Dr. David R. Hardoon said, Through our work at ADI over the past year, we have proven that optimising traditional methods with Data Science and AI has brought pioneering innovations and can contribute to the wider community. Alvins experience in data-driven digital solutions in the building sector, combined with his commitment to sustainability is definitely going to be an asset for ADI in promoting the adoption of DSAI for our smart cities practices.

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Aboitiz Data Innovation appoints new GM to lead data science and AI adoption for smart cities - ETCIO South East Asia

University Librarian and Vice Provost Smith Announces Retirement – University of California, Davis

October 12, 2022 | Author: admin

The Office of the Provost released this announcement today (Oct. 11):

MacKenzie Smith has announced she will retire as University Librarian and Vice Provost of Digital Scholarship at the end of June 2023, after more than a decade at UC Davis and nearly 40 years in academic research libraries.

Smith joined UC Davis in 2012, following a long career with the libraries of MIT, Harvard, and the University of Chicago.

During her tenure as University Librarian, Smith has overseen all aspects of the UC Davis Library, including its buildings, collections, personnel, and programs. She has led initiatives to modernize the library and advance the universitys strategic priorities by enhancing the librarys support for research, teaching and learning, including:

Smith credits an outstanding team of librarians and staff for the advancement of the library over the past decade, particularly their effective response to supporting the campus during the COVID-19 pandemic. In addition to expanding online access to library materials and services, UC Davis was the first UC campus to reopen its main library.

MacKenzie has led our library during a period of transformative change in how scholars create, access and share research, Provost and Executive Vice Chancellor Mary Croughan said. She has made substantial contributions to the campuss research enterprise at every level, from data science and informatics to the establishment of an undergraduate library research prize.

MacKenzie has also elevated our librarys leadership role, within UC and far beyond, in advancing free and open access to research. We will miss her leadership and collaboration, but wish her all the best in her retirement.

As Vice Provost for Digital Scholarship, Smith has also led campuswide initiatives involving information technology, research computing and knowledge management. She helped strengthen the universitys data governance as the lead architect and inaugural co-chair of UC Davis Institutional Data Council.

Throughout her academic career, Smith has developed and led entrepreneurial programs that apply technology innovation to libraries and their parent institutions and create new models for publishing and data-driven research in the digital age. Her work with open-source software platforms, such as MITs DSpace for archiving digital research, has had a lasting impact on research libraries and many other cultural heritage and knowledge-generating institutions.

Smith feels the timing is right for a new leader to steer the next phase of the librarys evolution and implement its new strategic plan.

I am immensely proud of how our library team has responded to the opportunities and challenges of the past 10 years, from the rapid growth of digital technology that has reshaped libraries, to the many changes we all faced during the pandemic, she said. Weve evolved and grown so much, and I cant wait to see what the library and UC Davis will achieve together in the decade to come.

The Provost, who will launch a search for a new University Librarian this fall, said she is immensely grateful for Smiths service and commitment to UC Davis.

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University Librarian and Vice Provost Smith Announces Retirement - University of California, Davis

Elemental Machines and Kanomax USA announce a strategic partnership to offer an integrated digital solution for clean room technology – PR Newswire

October 12, 2022 | Author: admin

Elemental Machines partners with Kanomax USA to deliver a seamlessly integrated clean room monitoring solution. Together, the technologies will empower researchers and manufacturers with environmental and operational data directly via the cloud.

CAMBRIDGE, Mass., Oct. 12, 2022 /PRNewswire/ -- Elemental Machines ("EM") a leading provider of IoT sensors powered by data science and providing actionable insights based on operational data has partnered with, Kanomax USA is a leading manufacturer delivering the best measurements for particle detection and airflow measurement in the business. As a laboratory operations ("LabOps") intelligence pioneer, EM is dedicated to supported fully-connected lab and manufacturing facilities.

The new partnership brings together EM's lab monitoring software with Kanomax' precision measurement technology to incorporate real-time and historical operational data with Kanomax' Clean Room Monitoring System.

The Kanomax Cleanroom Monitoring System offers turnkey solutions for monitoring needs required by various industries such as pharmaceutical, medical device, aerospace, semiconductor, and automotive. With more than 80 years of experience in the business Kanomax USA has always been pushing forward. Kanomax' precision particle counters are capable of real time monitoring of temperature, humidity, airborne particles, differential pressure, energy consumption, and gasses. Via the Kanomax' system, alarms can be initiated per designated event and have dependable remote access.

"We are excited to be teaming up with Elemental Machines and starting a new journey in cleanroom solutions and technology," said Koji Miyasaka, General Manager at Kanomax USA. "We are most excited about working with the team over at EM and collaborating on future projects with their team."

The partnership will allow data collected by Kanomax' technology to be stored and accessed via EM cloud-based dashboard for ease of viewing.

Elemental Machines' IoT enabled sensors and platform connect virtually any piece of equipment to the cloud, bringing together important data from across the lab and manufacturing. EM's real-time environmental monitoring and alerting solution tracks critical data such as CO2/O2 percentage, airborne particles, temperature, and humidity, notifying users of out-of-range events.

"Partnering with Kanomax will add to EM's growing list of hardware integrations that eliminate data silos and digitally connect critical environmental conditions with real-time and historical data helping with reproducibility, compliance, and production integrity," said Dan Petkanas, National Channel Sales Manager at Elemental Machines.

About Kanomax USA

Kanomax delivers the best measurement solutions with its products and services that adapt precision measurement technology for fluids and particles. We contribute to technological innovation and quality improvements for the processes of quality and environment management. Sustaining human well-being in the areas of environment, health, and energy have always been a primary focus of Kanomax. We develop leading technology with the goal of maintaining health and safety in industries including automotive, aerospace, semiconductor, electronics manufacturing, heavy industry, steel, shipbuilding, pharmaceutical, biotechnology, food processing, medical, construction and civil engineering.

Home

About Elemental Machines

Elemental Machines is the trusted data collection, analysis, and reporting technology supplier to researchers, clinicians, and LabOps professionals around the world. The Cambridge-based company equips labs with universal cloud-based dashboards and turnkey sensors that unite data from every asset, every metric, and every location, enabling rapid collection, seamless sharing, and effortless reporting.

http://www.elementalmachines.com/

SOURCE Elemental Machines

Can big data really predict what makes a song popular? – The Conversation Canada

October 12, 2022 | Author: admin

Music is part of our lives in different ways. We listen to it on our commutes and it resounds through shopping centres. Some of us seek live music at concerts, festivals and shows or rely on music to set the tone and mood of our days.

While we might understand the genres or songs we appreciate, its not clear precisely why a certain song is more appealing or popular. Perhaps the lyrics speak to an experience? Perhaps the energy makes it appealing? These questions are important to answer for music industry professionals, and analyzing data is a key part of this.

At Carleton University, a group of data science researchers sought to answer the question: What descriptive features of a song make it popular on music/online platforms?

Revenue in the music industry is derived from two sources that are affected by different factors: live music and recorded music. During the pandemic, although live music income dropped due to the cancellation of in-person performances, the income from streaming rose.

As digital platforms like Spotify and TikTok have grown, the majority of music revenue has come to be contributed by digital media, mostly music streaming. How and whether this revenue reaches singers and songwriters at large is another matter.

The popularity of a song on digital platforms is considered a measure of the revenue the song may generate.

As such, producers seek to answer questions like How can we make the song more popular? and What are the characteristics of songs that make it the top charts?

With collaborators Laura Colley, Andrew Dybka, Adam Gauthier, Jacob Laboissonniere, Alexandre Mougeot and Nayeeb Mowla, we produced a systematic study that collected data from YouTube, Twitter, TikTok, Spotify and Billboard (Billboard Hot-100, sometimes also denoted by data researchers as Billboard hot top or in our work and others work, Billboard Top-100).

We linked the datasets from the different platforms with Spotifys acoustic descriptive metric or descriptive features for songs. These features have been derived from a dataset which yielded categories for measuring and analyzing qualities of songs. Spotifys metrics capture descriptive features such as acousticness, energy, danceability and instrumentalness (the collection of instruments and voices in a given piece).

We sought to find trends and analyze the relationship between songs descriptive features and their popularity.

The rankings on the weekly Billboard Hot-100 are based on sales, online streams and radio plays in the United States.

The analysis we performed by looking at Spotify and Billboard revealed insights that are useful for the music industry.

To perform this study, we used two different data sets pertaining to songs that were Billboard hits from the early 1940s to 2020 and Spotify data related to over 600,000 tracks and over one million artists.

Interestingly, we found no substantial correlations between the number of weeks a song remained on the charts, as a measure of popularity, and the acoustic features included in the study.

Our analysis determined that newer songs tend to last longer on the charts and that a songs popularity affects how long it stays on the charts.

In a related study, researchers collected data for Billboards Hot 100 from 1958 to 2013 and found that songs with a higher tempo and danceability often get a higher peak position on the Billboard charts.

We also used the songs features to generate machine learning models to predict Spotify song popularity. Preliminary results concluded that features are not linearly correlated, with some expected exceptions including songs energy.

This indicated that the Spotify metrics we studied including acousticness, danceability, duration, energy, explicitness, instrumentalness, liveness, speechiness (a measure of the presence of spoken words in a song), tempo and release year were not strong predictors of the songs popularity.

The majority of songs in the Spotify dataset were not listed as explicit, tended to have low instrumentalness and speechiness, and were typically recent songs.

Although one may think that some features that are innate to certain songs make them more popular, our study revealed that popularity can not be attributed solely to quantifiable acoustic elements.

This means that song makers and consumers must consider other contextual factors beyond the musical features, as captured by Spotifys measurables, that may contribute to the songs success.

Our study reinforces that elements affecting the popularity of songs change over time and should be continuously explored.

For example, in songs produced between 1985 and 2015 in the United Kingdom, songs produced by female artists were more successful.

Other aspects may substantially contribute to the success of a song. Data scientists have proposed simplicity of the lyrics, the advertising and distribution plans as potential predictors of songs popularity.

Read more: Beatles 'Get Back' documentary reveals how creativity doesn't happen on its own

Many musicians and producers make use of popular events and marketing strategies to advertise songs. Such events create social engagements and audience involvement which attaches the listener to the song being performed.

For the public, live music events, following long lockdowns, have been opportune for reuniting friends, and enjoying live artistry and entertainment.

While attending a music event or listening to a song, we invite you to reflect on what it is about the song that makes you enjoy it.

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Can big data really predict what makes a song popular? - The Conversation Canada

What is data analytics and how it may help in your career? – Economic Times

October 12, 2022 | Author: admin

In the current digitally-driven times, technology and data are omnipresent. Owing to tech advancements, data analytics is one of the important markets today. According to India Brand Equity Foundation, the Indian Data Analytics industry is expected to hit the $118.7 billion mark by 2026. The data analytics has become a vital component of businesses across industries. It provides actionable insights into customer behavior along with comprehensive market analysis thereby providing a competitive edge to organisations. Due to all these reasons, data analytics is gradually gaining popularity across the world.

Why Data analytics is useful?Data analysts are the experts who translate, gauge and capture valuable insights from the large volumes of unstructured data that has been collected for the sole purpose of organisational growth. This data can be utilized for greater sales, product development, optimized operations, and reduction of risks through accurate forecasting models. The analysts analyze the data and gather knowledge via the insights. This knowledge is further applied across the realms of processes and industries.

In todays times, data analytics is the most important component for any business. It helps them gather insights on market trends and make data-driven decisions thereby maximizing profits. The application of Data Analytics is immense spreading across sectors. Finance, E-Commerce, banking, logistics, supply chain, healthcare, etc. are just to name a few fields where the technology is marking its presence.

Applications of Data Analytics:The application of Data Analytics as a technology is diverse. One of the major applications is when it is used in the HR domain. Helping in talent acquisition, HR analytics is the data-driven approach that helps recruiters leverage the potential of this technology for sourcing deserving talent.

Another field where the tech has prominent usage is for capturing insights that improve healthcare decisions thereby offering benefits to the patients. It is due to healthcare analytics that patient care has enhanced, diagnosis has become accurate as well as quick and preventive healthcare can be practiced easily.

Financial Analytics is yet another segment where Data Analytics is applied to seek answers to specific business challenges as well as forecast the financial future of the organisation. The insights provide information on financial scenario of the company by evaluating operations, budgets, and other transactions thereby rendering a helping hand in business growth. Apart from the private sector and corporates, even the government is harnessing the power of data analytics for improving the state affairs, city governance, ensuring public welfare and is using it as a solution to deal with macro level issues.

Data analytics: The in-demand skillsetAs per IBEF reports, the Indian Data Analytics market is anticipated to play an integral role in driving the future of fourth phase of industrialization. It will also help in creating various employment opportunities. The study further points out that the industry is expected to create over 11 million jobs by 2026. It will also witness 33.49% increase in investments in AI and ML learning by the end of this year.

As organizations are adopting Data Analytics at a rapid pace, it is emerging to be a viable career option for the students. It thus becomes an ideal career move for both students and professionals to pursue this field. Various industry reports highlight that there has been a 30% spike in Data Analytics and Science jobs this year as compared to last year. According to LinkedIns Job on the Rise 2022 report, ML Engineer and Data Science Specialist are the fastest growing segments and are expected to be the in-demand job roles in the times ahead.

Courses for Data Analytics:The demand for data analysts is high. However, the fact of the matter is that talent with the required skillsets is lacking. As a result, the talent skill gap is significant and there is a growing need for professionals with Data Analytics skills who can drive organizations with a data-driven approach in the future. There are several courses and reputed institutes that are offering data analytics courses. Students can explore the courses according to their requirement. Student can also explore courses like PGDM in Data Analytics that are offered by B-schools.

These programs help the students in making data-driven decisions with the assistance of valuable insights. They indulge in holistic learning with a key focus on management in all the disciplines of analytics. The learners also get an opportunity to have hands-on learning on the diverse tech-driven integrated tools and analytic platforms complimenting the theoretical knowledge. They also gather learning about building data-driven strategies with the help of automation that would contribute in optimizing operations and inducing innovation in organizations.

What are the career options in Data AnalyticsStudents with the right balance of managerial and analytical skills can make their mark in the field of data analytics. The field is perceived to be as a lucrative employment avenue that comes with high payscale and a plethora of job roles. Being an in-demand skill, the industry standards offer handsome salary packages to data scientists and analysts. The professionals can earn anywhere between Rs 2 lakhs to Rs 12 lakhs approximately. The average salary is estimated to be around Rs 4 lakh as per the experience and skillset of the professional.

Job aspirants can seek employment across Data analyst, Data architect, Statistician, Project Manager, Chief Data Officer, and ML engineer roles amongst many more. There are different fields and degrees that you can specialize in as per your interest. For instance, Forensic data analysts help recover and protect digital data pertaining to criminal and cybercrime investigations. Data mining is another specializing field wherein professionals analyze data to decipher trends and patterns.

Summing up!

Data is set to be the future of businesses and will disrupt their operations across sectors. Hence, it would be appropriate to say that Data Science and Analytics are emerging to be promising career avenues. As organizations increasingly rely on AI, ML, and Big Data, the demand for Data Analytics is spiking exponentially. Those who are intrigued by numbers and if working with statistics as well as insights interests them, then they should certainly consider PGDM courses in this field as it can be a lucrative career option!(The writer is Professor, IT Management, FORE School of Management, New Delhi)

What is data analytics and how it may help in your career? - Economic Times

Winning The Most Challenging Crypto Prediction Challenge: The Winners’ Approach – Analytics India Magazine

October 12, 2022 | Author: admin

Rocket Capital Investment (RCI), in association with MachineHack, successfully completed the longest blockchain tournament on Sep 5, 2022. The goal was to incentivise the best in machine learning applications for finance.

Headquartered in Singapore, licensed financial institution RCI combines its financial expertise with external machine learning forecasts through a blockchain tournament on financial markets. Through this competition, RCI aimed to use a decentralised platform to source and incentivise the best machine learning applications for the finance industry.

From the many entries received, only the best of the lot made it to the top. Analytics India Magazine spoke to some of the best performers to understand their data science journey, winning approach, and overall experience at MachineHack.

Lets look at the ones that impressed the judges with their data skills.

Pathak is a BITS Pilani graduate who started exploring data science in his pre-final year. With all the complicated maths learned during college and the experience in handling data at scale using Big Data, he was naturally inclined to contribute to the data science community.

Every week, the training dataset was a numeric structured data with 2000+ features and about one lakh observations. The target was continuous. Throughout the competition period, Pathak trained different regressors on the dataset with Spearman correlation as the metric. The regressors he trained were mainly tree-based boosting regressors such as XGBoost, CatBoost and LightGBM. He also trained Random Forest and Neural Networks in a few weeks challenges.

Since the dataset was huge, LightGBM and XGBoost were relatively faster than CatBoost. He tuned the hyper-parameters using Bayesian optimisation methods without any k-Fold CV as time was a constraint.

Since the dataset was time-based, he used the most recent data (~around 10%) as his validation set. Next, Pathak used a weighted average of the predictions from different regressors to optimise Spearmans correlation and checked the ranking of predictions by sorting.

Data science fascinated Sawhney, even before he heard the term. After many years of practice as an eye surgeon, he decided to wield the keyboard. His current area of interest is Computer Vision applications. Apart from trying his hand at various hackathons, he mentors AI/ML students on the weekends.

Sawhney started by evaluating feature importance and testing different models using different numbers of features. He found that using the top 160-200 features was adequate for capturing the information contained.

He evaluated various models before finally deciding on an ensemble of Random Forest and XGBoost. Since the target variable is essentially sequential, he also experimented with various time-series models, but the results of those experiments were not satisfactory.

To account for some influence of the previous weeks position of the coin, he calculated this value for all coins where possible. Then, he combined it with the ensemble prediction, using weighted mean to assign 4% weightage to the last coin-value and 96% weightage to the ensemble prediction.

After completing his studies in mathematics a decade ago, Bessalov started working as a data scientist. He has participated in ML hackathons on the platforms for two years and has learned much from these competitions. His most memorable competitions are: Renew Power, Dare in Reality Hackathon 2021 and Rocket Capital Crypto Forecasting.

1) Datasets preparation:

Bessalov took the past three months for the evaluation set;

For the training set, he took all other periods with the gap (holdout) of 1 month to the validation set. For example, one can choose: 2022-06-01 to 2022-09-01 for the validation set, the first available month to 2022-05-01 for the training set and so on.

2) Features:

When Bessalov trained the final model, he used all numerical features 2010 in total.

3) Model:

He trained the Xgboost model with an early stop on the validation set and the following parameters:

objective: reg:squarederror,

eta: 0.05,

max_depth: 6, # -1 means no limit

subsample: 0.7, # Subsample ratio of the training instance.

colsample_bytree: 0.7, # Subsample ratio of columns when constructing each tree.

reg_alpha: 0, # L1 regularization term on weights

reg_lambda: 0, # L2 regularization term on weights

Approaches tested:

He fixed the validation set and tried to find the training set (the number of months to the validation set) that gives the best Spearman correlation score.

He explored features by calculating the stability index and then tried to remove unstable (with different criteria) features from the model.

He tried to train different models and then linearly stacked them (took all possible linear combinations with 0.01 step):

Xgboost

Random Forest

Linear models

The CryptoPrediction Challenge saw participants bring out-of-the-box solutions to the table to solve the innovative problem theyd been presented with. Having such a high level of skills at the CryptoPrediction Challenge surely made it a huge success.

Excerpt from:

Winning The Most Challenging Crypto Prediction Challenge: The Winners' Approach - Analytics India Magazine

2nd Edition of Analytics Olympiad is Live Now – Analytics India Magazine

October 12, 2022 | Author: admin

The Academy of Continuing Education at Shiv Nadar Institution of Eminence Delhi-NCR, in partnership with MachineHack, is launching the second edition of its annual Analytics Olympiad, from 30th September 2022, to 13th December 2022, for data scientists and machine learning professionals.

This two month-long championship has been primarily designed to strengthen the data science community in India and pave the way for innovation. This challenge is the perfect opportunity for data science enthusiasts, learners and professionals in India to showcase their skills and leadership potential in business analytics and advance their careers.

The vehicle insurance business is a multi-billion dollar industry. Every year millions and millions are paid in premiums, and a huge amount of claims pile up.

At the Analytics Olympiad 2022, the data science and machine learning community would step into the shoes of a data scientist and create an ML model that would help an insurance company understand which insurance claims should be accepted for reimbursement and which must be rejected.

The participants would be given a rich dataset consisting of thousands of rows of past records to learn more about customers behaviours.

Columns: [ID, AGE, GENDER, DRIVING_EXPERIENCE, EDUCATION, INCOME,

CREDIT_SCORE, VEHICLE_OWNERSHIP, VEHICLE_YEAR, MARRIED,

CHILDREN, POSTAL_CODE, ANNUAL_MILEAGE, SPEEDING_VIOLATIONS,

DUIS, PAST_ACCIDENTS, OUTCOME, TYPE_OF_VEHICLE]

Learn and predict the OUTCOME variable.

Winner: INR 1 Lakh

1st Runner-up: INR 30,000

2nd Runner-up: INR 20,000

** Note: Analytics Olympiad 2022 will be held in two phases. In the first phase, the participants will be allowed to submit their approaches against the given problem statement, which they have to solve based on the dataset provided on the MachineHack platform. The evaluation of the first phase will be done based on the leaderboard, and the top 10 participants will be given a chance to participate in the second phase, i.e. the Jury Round. The jury round will be helmed by a group of expert panellists from the industry and academia. The participant who will crack phase two of the challenge will be the Analytics Olympiad and be awarded INR 1 Lakh cash. In addition, the second & the third winner will be granted INR 30,000 & INR 20,000 each, respectively.

To understand the rules of the hackathon, click here.

Shiv Nadar Institution of Eminence is a student-centric, multidisciplinary and research-focused university offering a wide range of academic programs at the Undergraduate, Master and Doctoral levels. The University was set up in 2011 by the Shiv Nadar Foundation, a philanthropic foundation established by Mr. Shiv Nadar, founder of HCL. In the NIRF (Governments National Institutional Ranking Framework), the University has been the youngest institution in the top 100 overall list.

The universitys Academy of Continuing Education aims to facilitate best-in-class knowledge, practices and skill development offerings to the growing ecosystem of lifetime learners and leaders, both within and outside the university. With distinguished academics as the universitys faculty members and programme instructors, the Academy of Continuing Education offers uniquely crafted programmes that are delivered innovatively, bringing together the best of the universitys rich intellectual resources.

The university aims to help students prepare for today as well as their future through its unique certification programme in data sciences and business analytics. The collaboration between the Academy of Continuing Education at Shiv Nadar Institution of Eminence and MachineHack hopes to strengthen the data science community in India and pave the way for innovation in business analytics.

Excerpt from:

2nd Edition of Analytics Olympiad is Live Now - Analytics India Magazine

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Winning The Most Challenging Crypto Prediction Challenge: The Winners’ Approach – Analytics India Magazine

2nd Edition of Analytics Olympiad is Live Now – Analytics India Magazine

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