Category Archives: Data Science
Upskilling accountants in data should be preferred option for firms … – Accountancy Age
Accountancy firms should aim to upskill workforces in the face of data skills shortages, according to Clare Walsh, director of education at the Institute of Analytics (IOA).
Data consultancy group Dufrain released its 2023 state of data analytics report, revealing that 60% of corporate leaders said the biggest challenge to their business was recruiting individuals with the necessary data skills and talent.
You dont need to train accountants as much compared with if you hire a data analyst. Youve got to train data analysts on how accountancy works and how your business works, you do have to train accountants but on different things, says Walsh.
Its way easier for an accountant to upskill in data than for a data analyst to learn accountancy.
David Whitson-Black, global head of talent and performance at Azets says they have been looking at external training providers to help with its upskilling regime.
Were looking at where we have gaps and where we could have gaps and we have to fill those by looking at how we upscale our people internally.
Additionally, Azets accountancy students are currently receiving education in data analytics and data science as part of their ongoing qualifications.
We have people at the earliest stages of their career going through that. We have people who are perhaps a bit more advanced doing some data specific roles or working with technology more prominently, and were upscaling them.
Currently, Azets are acting on a case-by-case basis regarding its recruitment of data specialists, but this could change going forward, states Whitson-Black.
The American Institute of Certified Public Accountants (AICPA) released a survey in March 2023 on how accountants could increase their readiness for the profession. According to the poll, 99% of accounting firms cited data analysis, tools, and techniques as a valuable (either very valuable or somewhat valuable) skill for new hires.
Whitson-Black says that, for Azets, while there is more of a focus in hiring data specialists, firms find it difficult to anticipate the extent to which data specialists will play a significant role in the future.
On one hand, youve got jobs already that require you to have those data skills and we will actively look for those skills.
The other side is where we know that those skills will be beneficial to you, and we know that it will help us in our endeavours, but were not quite at that stage yet where its already a prominent part of your role.
Considering the substantial experience that accountants already possess in handling large data sets, the IOAs Walsh believes it would not take too much training for them to start looking at predictive analytics.
We can get them doing some more exciting things with different types of data analytics, and graph data is not a big step on for accountants which can show them some amazing things.
Accountants are also phenomenal at understanding the businesses that they work for and its regulatory environments. All these areas are where our data analysts are quite poor in.
Walsh argues that an accountant who comprehends both the financial landscape and regulatory requirements, while also excelling in data management and analysis, possesses formidable capabilities.
According to a study conducted by 365 Data Science Ltd, the typical data scientist tends to stay in their position for approximately 1.7 years on average. This is one of the biggest risks of hiring a pure data scientist, stated Walsh.
This type of data proves data specialists are needed, but it is now up to the profession to provide fulfilling roles for them, Whitson-Black argues.
If someone who has really beneficial skills is coming into a business that isnt ready for them then theyre not going to be able to give their full experience and their full self to that role. So how fulfilling is that going to be for someone who has all of these skills but cant use them.
I would say attrition high in that area because they want to go somewhere else to get fulfilment.
To access a free webinar provided by the Institute of Analytics on upskilling for accountants, please click here
Read the original here:
Upskilling accountants in data should be preferred option for firms ... - Accountancy Age
Cybersecurity engineers are among the highest paid in the growing … – Fortune
BY Preston ForeOctober 11, 2023, 5:31 PM
Photo illustration by Fortune; Original photo by Getty Images
Cybersecurity remains one of the fastest-growing industries in the world. With hundreds of thousands of open positions in the United States aloneand salaries averaging six-figuresnow is no better time to learn more about cybersecurity and potentially enhance your career.
The industry is predicted to grow by 32% over the next decade, according to the U.S. Bureau of Labor Statistics, and information security analysts earn $112,000 annually on average.
But within the realm of the cybersecurity industry, engineers are among the top-pay positions, based on tech staffing agency Mondo data cited by the University of San Diego.
The school is home to one of the nations few cybersecurity programs specific to engineering.
Haydar Majeed is a professor of practiceand alumat the University of San Diegos master of science in cybersecurity engineering program.
His journey in cybersecurity began working for the U.S. military in Iraq focusing on language as well as encryption and decryption. He says he in part chose to come to San Diego due to its hands-on learning with a virtual cyber lab that simulated real-life offensive and defensive technologies.
Additionally, the programs designation as a National Center of Academic Excellence in Cybersecurity by the National Security Agency (NSA) sealed the deal for Majeed. Hundreds of higher education institutions have the distinction.
He says one myth of cybersecurity programs is that you need to know a ton of math or hacking.
We built the programand we continuously update the programto be gradual. So, well walk you in regardless of your background, Majeed tells Fortune. So whether youre technical, non technical youre gonna find the program actually fitted to who you are.
Cybersecurity is important since it is embedded and is essential to most industries from banking to law to marketing, he adds. After graduating from San Diego, he founded his own cybersecurity firm, which was later acquired.
An additional success story, Majeed notes, is that one student in the San Diego program went from earning less than $50,000 a year as an Uber driver to working at a data analytics company with a $150,000 annual salary.
For Michael Stiber, professor at the University of Washington Bothell, teaching cybersecurity is about preparing students for technologies and challenges that dont yet exist.
New technologies like AI are not only opportunities to make systems more robust against attack; they are also tools that can make the bad guys jobs easier, Stiber tells Fortune. Cybersecurity is our defense against such things and cybersecurity professionals are the digital makers and first responders keeping our increasingly digital lives safe.
And while there are many facets ofand ways to studycybersecurity he says individuals should weigh their own background, life commitments, and career objectives to determine whats best for them.
If the intersection of engineering and cybersecurity fits that mold for you, Fortune has saved you some time and compiled a list of just some of the U.S. universities with masters programs in cybersecurity engineering.
Duke University
University of Maryland
University of San Diego
University of Southern California
University of Washington at Bothell
Washington University in St. Louis
Read more here:
Cybersecurity engineers are among the highest paid in the growing ... - Fortune
How to Plot Graphs Using the DiagrammeR Package in R – Built In
DiagrammeR is a package in R that is used to create graphs and flowcharts using Graphviz and Mermaid. This can be useful for displaying data. In this article, well cover how to create graphs using the grViz() in the DiagrammeR package, including:
DiagrammeR is a package within htmlwidgets for R. Its used to generate graphs using Graphviz and Mermaid library.
In order to install DiagrammeR to create plot graphs, there are two steps. First, youll create an R Markdown document, which will set the output to HTML. Then, youll install the DiagrammeR package. Heres how:
In RStudio Go to File New File R Markdown. Then, give the title and select the output format as HTML.
The R Markdown document will have the title and other information at the top.
Below that we will have R chunks which have delimiters ```{r} and ```.
When you render the R Markdown document, it will run each chunk and embed the results beneath the code chunk or you can run each code chunk and the result will be displayed for that code chunk.
We can knit the R Markdown document to HTML output.
Go to Packages Install on the right side of the RStudio and enter the package name DiagrammeR and click Install.
And thats it, youve installed DiagrammeR.
An error occurred.
More on RThe Ultimate Guide to Relational Operators in R
DiagrammeR package uses the grViz() function for Graphviz graphs. Lets see how to use grViz() function to create graphs.
While creating a graph, we have to mention the layout, node attributes, edge attributes and connection.In the graph below, green color circles are called nodes. A=15, B=10, etc. are the labels of the nodes. The-> arrow mark is known as the edges. Heres how to set the node, edge attributes, label and substitutions and connections required to create it.
The default layout is the dot.
Include the node shape, color, style and width, etc. that you want for your graph.
Detail your edge attributes. These include color, arrowhead, arrow tail, pen width and direction, etc.
We have to include the label and substitution for the node.
@@1 -> is the substitution.
Here,1 is the footnote number. The value 15 will be substituted in place of 1 when the graph is rendered.
In my graph A is connected to B and C. B is connected to D and C is connected to E.
Lets create another graph where nodes A and C are in the same rank and B, and E are in the same rank.
If we mention rank=same {A->C}, A and C nodes will be placed in the same line.
Lets create a node from the down arrow of another node.
Heres how to create the graph mentioned above.
First, we have to create a blank node.
After that we can make the blank node label = and the width and height to be very small (0.01).
The next step is to remove the arrow mark. It can be removed by mentioning the edge attribute dir=none.
Select the option knit -> knit to html.
The whole R Markdown document will be rendered into an HTML document.
If you dont want the r-code to be displayed in the HTML output, mention echo =False in the r-chunk code.
More on Data SciencePython Data Visualization With Seaborn & Matplotlib
DiagrammeR is a package in R that allows the creation of graphs using Graphviz and mermaid styles. In this article, we have covered using Graphviz style. We have covered how to create nodes, labels, edges, connections and layouts for the graphs using the grViz() function in the DiagrammeR package. We have also covered node attributes, edge attributes, labels and their substitutions. We have covered only the dot layout, which is the default layout in this article.
Read more:
How to Plot Graphs Using the DiagrammeR Package in R - Built In
Teaching data science in art, social studies and language class – The Hechinger Report
Editors note: This story led off this weeks Future of Learning newsletter, which is delivered free to subscribers inboxes every other Wednesday with trends and top stories about education innovation. Subscribe today!
While data science isnt a new subject, theres been growing interest recently in helping students in both K-12 and higher ed gain data science skills.
One reason is the shifting job market, said Zarek Drozda, director of Data Science 4 Everyone, a national initiative based at the University of Chicago. The top skills in demand today are data analysis, data interpretation, being able to communicate about data, Drozda said. Its hard to find a career or a sector of the economy where data skills are not important.
With the rise of artificial intelligence tools such as ChatGPT that rely on data sets, students also need to understand how to use AI in a responsible way, he added.
The adoption of data science education hasnt been without controversy. In 2020, some of Californias public universities allowed applicants to skip Algebra II and substitute data science. The universities walked back the effort this year after experts argued that students were taking less challenging coursework that limited their post-secondary opportunities.
No state is currently getting rid of algebra courses in favor of data science, Drozda said. Rather, some are introducing the subject as an additional option for students. In the last three years, 17 states have added some sort of data science education course to their K-12 offerings, Drozda said.
There are opportunities to make the barrier to entry low, but the benefit high so that students are able to see the existing school subjects in a context that is relevant to their daily life.
In higher ed, data science is often housed in a particular school or limited to one field of study, such as a mathematics or computer engineering. But North Carolina State University is taking a different approach to teaching the subject, said Rachel Levy, executive director of the schools new Data Science Academy. N.C. State launched the academy two years ago to introduce the use of the subject across disciplines, from biology and art to English and history.
To help all 10 of its colleges introduce courses incorporating data science, available to students at different levels, the university adopted the All-campus Data science through Accessible Project-based Teaching and learning model, or ADAPT. Examples of interdisciplinary classes available to students in any college include Introduction to Data Visualization, Introduction to R/Python for Data Science and R for Biological Research. The classes are project-based, and history or English major might choose to focus their class project on applying the use of data science to a topic within their major. Students are also encouraged to apply the skills they learn in these classes to other non-data science courses as well.
The universitys College of Education is also using the ADAPT model to prepare future K-12 teachers for the classroom. Using federal grants, N.C. State researchers are studying the model and its impact on teaching and learning. Meanwhile, the Data Science Academy is collaborating with the states Department of Public Instruction, hoping to roll out data science education in schools across the state, according to Levy.
Taryn Shelton, the academys K-12 data science coordinator, said the goal isnt to add yet another thing to teachers plates, but to help them use data to enrich their lesson plans and expose students to data science skills early on. Her team is working with school districts outside of the tech and research-heavy Raleigh-Durham-Chapel Hill Triangle area, as well as with more rural and underserved districts, to help educators build data science concepts into their curriculum. Sheltons team also hosts events like mini hackathons where high schoolers can work with data.
There are lots and lots of ways across the disciplines that teachers can bring in data, said Levy, the academys director. Social studies teachers can help students explore data about people, places, events and cultures, she said, while English teachers might have their students identify and count words or phrases that help create a particular mood in a piece of writing.
If educators introduce data science in authentic ways that connect to students interests, Levy said, their comfort with the topic will grow. People of all ages can engage data in ways that are useful and meaningful and challenging, she said.
The challenge right now, said Data Science 4 Everyones Drozda, is that most students dont encounter data science until they take AP Statistics or Intro to Data Science toward the end of high school, if they encounter it at all. But it doesnt have to be that way. Drozda and Levy envision data science being integrated into the elementary and middle school curriculum, with teachers using data sets in biology units about ecosystems, or to analyze economic booms and busts in social studies.
Itll be really important for students to build a comfort and familiarity with the data science way of thinking, as well as the computational and technology tools, said Drozda. There are opportunities to make the barrier to entry low, but the benefit high so that students are able to see the existing school subjects in a context that is relevant to their daily life.
This story about data science was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the Hechinger newsletter.
Related articles
The Hechinger Report provides in-depth, fact-based, unbiased reporting on education that is free to all readers. But that doesn't mean it's free to produce. Our work keeps educators and the public informed about pressing issues at schools and on campuses throughout the country. We tell the whole story, even when the details are inconvenient. Help us keep doing that.
Join us today.
Read more from the original source:
Teaching data science in art, social studies and language class - The Hechinger Report
A Comparative Overview of the Top 10 Open Source Data Science Tools in 2023 – KDnuggets
Data science is a trendy buzz that every industry is aware of. As a data scientist, your main job is extracting meaningful insights from the data. But here is the downside - with data exploding at exponential rates, it is more challenging than ever. You will often get the feeling of finding the needle in a digital haystack. This is where the data science tools emerge as our saviors. They help you mine, clean, organize, and visualize the data to extract meaningful insights from it. Now, let's address the real problem. With the abundance of data science tools, how will you navigate to find the right ones? The answer to this question rests in this article. Through a careful blend of personal experience, invaluable community feedback, and the pulse of the data-driven world, I have curated a list that packs a punch. I have focused only on open-source data science tools because of their cost-effectiveness, agility, and transparency.
Without any further delay, lets explore the top 10 open-source data science tools you need to have in your arsenal this year:
KNIME is a free and open-source tool that empowers both data science novices and experienced professionals by opening the door to effortless data analysis, visualization, and deployment. It's a canvas that transforms your data into actionable insights with minimal programming. It's a beacon of simplicity and power. You should consider using Knime for the following reasons:
Weka is a classic open-source tool that allows data scientists to preprocess data, build and test machine learning models, and visualize data using a GUI interface. Although it's quite old, it remains relevant in 2023 due to its adaptability to cater to model challenges. It provides support for various languages including R, Python, Spark, scikit-learn, etc. It is extremely handy and reliable. Here are some of the features of Weka that outshine:
Apache Spark is a well-known data science tool that offers real-time data analysis. It is the most widely used engine for scalable computing. I have mentioned it due to its lightning-fast data processing capabilities. You can easily connect to different data sources without being worried about where your data lives. Although it's impressive, it's not all sunshine and rainbows. Because of its speed, it needs a good amount of memory. Here is why you should choose Spark:
RapidMiner stands out due to its comprehensive nature. It's your true companion throughout your complete data science lifecycle. From data modeling and analysis to data deployment and monitoring, this tool covers it all. It offers a visual workflow design, eliminating the need for intricate coding. This tool can also be used to build custom data science workflows and algorithms from scratch. The extensive data preparation features in RapidMiner enable you to deliver the most refined version of data for modeling. Here are some of the key features:
Neo4j Graph Data Science is a solution that analyzes the complex relationships between the data to discover hidden connections. It goes beyond rows and columns to identify how the data points are interacting with each other. It consists of pre-configured graph algorithms and automated procedures specifically designed for the Data Scientists to quickly demonstrate value from graph analysis. It is particularly useful for social network analysis, recommendation systems, and other scenarios where connections matter. Here are some of the additional benefits that it provides:
gglot2 is an amazing data visualization package in R. It turns your data into a visual masterpiece. It is built on the grammar of graphics offering a playground for customization. Even the default colors and aesthetics are much nicer. ggplot2 utilizes the layered approach to add details to your visuals. While it can turn your data into a beautiful story waiting to be told, it's important to acknowledge that dealing with complex figures can lead to cumbersome syntax. Here is why you should consider using it:
D3 is the short form of Data-Driven Documents. It is a powerful open-source javascript library that enables you to create stunning visuals by employing DOM manipulation techniques. It creates interactive visualizations that respond to the changes in data. However, it has a steep learning curve specifically for those who are new to JavaScript. Although its complexity can be a challenge the rewards it offers are invaluable. Some of them are listed below:
Metabase is a drag-and-drop data exploration tool that is accessible to both technical and non-technical users. It simplifies the process of analyzing and visualizing the data. Its intuitive interface enables you to create interactive dashboards, reports, and visualizations. It is getting extremely popular among businesses. It provides several other benefits which are listed below:
Great Expectations is a data quality tool that enables you to assert checks on your data and to catch any violations effectively. As the name suggests, you define some expectations or rules for your data and then it monitors your data against those expectations. It enables the data scientists to have more confidence in their data. It also provides data profiling tools to accelerate your data discovery. The key strengths of Great Expectations are as follows:
PostHog is an open-source primarily in the product analytics landscape enabling businesses to track user behavior to elevate product experience. It enables the data scientists and engineers to get the data much quicker removing the need for writing SQL queries. Its a comprehensive product analysis suite with features like dashboards, trend analysis, funnels, session recording, and much more. Here are the key aspects of PostHog:
One thing that I would like to mention is that as we are progressing more in the field of Data Science, these tools are not just mere choices now, they have become the catalyst guiding you toward informed decisions. So, please dont hesitate to dive into these tools and experiment as much as you can. As I wrap up, I'm curious, Are there any tools you've come across or used that you'd like to add to this list? Feel free to share your thoughts and recommendations in the comments below.Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.
The rest is here:
A Comparative Overview of the Top 10 Open Source Data Science Tools in 2023 - KDnuggets
Future Data Scientists and Biomedical Informatics Researchers Will … – Yale School of Medicine
Yale School of Medicine (YSM) has begun a new concentration within the computational biology and bioinformatics program, giving students the opportunity to earn a masters degree and the necessary training to become biomedical data scientists/biomedical informaticians.
It also aims to train students to meet the growing demand of professionals who can organize and analyze biomedical and healthcare data using the latest developments in AI and related areas, said Lucila Ohno-Machado, MD, PhD, deputy dean and chair for biomedical informatics.
Cynthia Brandt, MD, MPH, vice-chair for education and professor in the new section of biomedical informatics & data science says this is the first time the graduate program offers the degree with a focus on biomedical informatics. It is now accepting applications until December 1. The first group of students will start in September 2024.
More than a specialized degree, this program offers students the opportunity to understand and translate computational data to the bench and bedside, says Brandt.
Without the workforce and the individuals who understand how data is created, how it's captured, how it's stored, and how different computational methods are necessary to analyze it, it causes a limitation that slows down what you can learn from the data that scientists are creating, explains Brandt. Then it makes it more difficult to translate that data, which could be used for clinical trials and for medical advances.
The terminal masters degree in computational biology and bioinformatics (CBB) with a biomedical informatics concentration consists of 9 courses, three of which are core studies, says Brandt.
Foundational classes for this masters degree are Introduction to Health Informatics, Core Topics in Biomedical Informatics and Data Science or Modeling Biological Systems II, and Biomedical Data Science: Mining and Modeling.
Without the workforce and the individuals who understand how data is created, how it's captured, how it's stored, and how different computational methods are necessary to analyze it, it causes a limitation that slows down what you can learn from the data that scientists are creating.
The remaining courses are electives and can vary, depending on each students interests and research focus. With the help of an advisor, students will choose elective courses in biomedical informatics. Examples of courses in this area include Natural Language Processing, Clinical Decision Support and Clinical Database Management Systems & Ontologies.
Students entering the program who do not have a background in biology will be required to take a course in biology or genetics.
Brandt added that additional courses students take could also be offered in the Department of Statistics and Data Science (S&DS), Computer Science (CPSC), or Engineering. Once a Department of Biomedical Informatics and Data Science (BIDS) is established at YSM, as is planned, newly developed courses will also have that designation.
To complete the graduate program successfully, students will need to pass at least two courses with honor grades and complete coursework with an average of a High Pass. Additionally, Grant detailed, students will complete a masters degree project, where they will come up with an idea, write a research paper, and defend it. They will also present their project in a seminar where they will answer questions about it as well as breadth knowledge of their coursework and track of study at the end of the two-year program.
Students interested in pursuing a doctoral degree in computational biology and bioinformatics or biomedical informatics can do so at Yale, said Brandt. According to Brandt, most of the students who apply to a bioinformatics program already have a background in biology and math, computer science, statistics, or data science. However, such programs also accept students who have a predominant clinical or biology background if they have a facility for math, statistics, or computer science.
My goal would be to have 10 to 20 students in the first year [of the masters program] where 10% or 20% of them would decide that they want to go on and get a PhD, says Brandt.
Brandt says that it is crucial to train individuals who are able to ask research questions, know how to analyze research data and other kinds of data, and understand computation and other computational methods.
The CBB PhD has existed in Yales Biological and Biomedical Sciences (BBS) since 2003. However, the biomedical informatics concentration was recently incorporated as a new independent section at YSM with several new faculty members, according to Brandt.
The masters degree in computational biology and bioinformatics was originally created by Perry Miller, MD, PhD and Mark Gerstein, PhD, in 2004 with the program intended to be a step for those interested in pursuing a doctoral degree in computational biology and bioinformatics.
The creation of a new academic unit in biomedical informatics and data science in 2023 expanded the teaching faculty significantly, said Ohno-Machado.
In addition to Brandt, senior faculty in biomedical informatics and data science include Ohno-Machado; Hua Xu, PhD, professor and vice chair for research; Daniella Meeker, PhD; associate professor in biomedical informatics & data science and chief research information officer.
Newly recruited junior faculty in biomedical informatics and data science include Qingyu Chen, PhD; Hoon Cho, PhD, Mary-Anne Hartley, MD, PhD, MPH, Xenophon Papademetris, PhD; Kei-Hoi Cheung, PhD; Huan He, PhD, Na Hong, PhD, Mark Iscoe, MD, MHS; Richard Taylor, MD, MHS; Fongci Lin, PhD, Anthony Lisi, DC; and Kalpana Raja, PhD, who join the existing team of biomedical informatics research scientists.
The growing team of 31 faculty who have secondary appointments at biomedical informatics and data science currently includes representatives from the Yale Child Study Center and YSM departments of Dermatology, Emergency Medicine, Laboratory Medicine, Molecular Biophysics, Biochemistry, Neurology, Ophthalmology, Pathology, Pediatrics, Psychiatry, Surgery, and Therapeutic Radiology, in addition to faculty at YSPH, SEAS, FAS, and YSN.
Link:
Future Data Scientists and Biomedical Informatics Researchers Will ... - Yale School of Medicine
The promise of data science for health research in Africa – Nature.com
Keshavamurthy, R., Dixon, S., Pazdernik, K. T. & Charles, L. E. Predicting infectious disease for biopreparedness and response: a systematic review of machine learning and deep learning approaches. One Health 15, 100439 (2022).
Article PubMed PubMed Central Google Scholar
Tanser, F. C. & le Sueur, D. The application of geographical information systems to important public health problems in Africa. Int. J. Health Geogr. 1, 4 (2002).
Article PubMed PubMed Central Google Scholar
Stewart, K. et al. Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria. Int J. Health Geogr. 19, 28 (2020).
Article PubMed PubMed Central Google Scholar
Georgakopoulos, S. V., Gallos, P. & Plagianakos, V. P. Using Big Data Analytics to Detect Fraud in Healthcare Provision in 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME 2020). (IEEE, New Jersy, 2020).
Gebremeskel, G. B., Yi, C., He, Z. & Haile, D. Combined data mining techniques based patient data outlier detection for healthcare safety. Int. J. Intell. Comput. Cybern. 9, 4268 (2016).
Article Google Scholar
Kumar, A., Choudhary, D., Raju, M. S., Chaudhary, D. K. & Sagar, R. K. Combating counterfeit drugs: a quantitative analysis on cracking down the fake drug industry by using Blockchain technology in Proc. 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence 2019). (IEEE, Noida, India, 2019).
United Nations. Big data for sustainable development, https://www.un.org/en/global-issues/big-data-for-sustainable-development (2023).
Arslan, J. & Benke, K. K. Artificial Intelligence and Telehealth may provide early warning of epidemics. Front. Artif. Intell. 4, 556848 (2021).
Article PubMed PubMed Central Google Scholar
Judson, S. D. et al. COVID-19 data reporting systems in Africa reveal insights for future pandemics. Epidemiol. Infect. 150, e119 (2022).
Article CAS PubMed Google Scholar
Shaw, N. & McGuire, S. Understanding the use of geographical information systems (GISs) in health informatics research: a review. BMJ Health Care Inform. 24, 940 (2017).
Kimera, R. et al. in Leveraging Data Science for Global Health (eds Celi, L. et al.) 329350 (Springer International Publishing, 2020).
Waisberg, E. et al. GPT-4: a new era of artificial intelligence in medicine. Irish J. Med. Sci. https://doi.org/10.1007/s11845-023-03377-8 (2023).
Akbasli, I. T. & Bayrakci, B. Balancing caution and innovation: exploring the potential of large language models in critical decision-making. Crit. Care 27, 172 (2023).
Article PubMed PubMed Central Google Scholar
Bair, H. & Norden, J. Large Language Models and Their Implications on Medical Education. Acad. Med. https://doi.org/10.1097/ACM.0000000000005265 (2023).
Perera Molligoda Arachchige, A. S. Large language models (LLM) and ChatGPT: a medical student perspective. Eur. J. Nucl. Med. Mol. Imaging https://doi.org/10.1007/s00259-023-06227-y (2023).
Qureshi, R. et al. Are ChatGPT and large language models the answer to bringing us closer to systematic review automation? Syst. Rev. 12, 72 (2023).
Article PubMed PubMed Central Google Scholar
Sorin, V., Barash, Y., Konen, E. & Klang, E. Large language models for oncological applications. J. Cancer Res. Clin. Oncol. https://doi.org/10.1007/s00432-023-04824-w (2023).
Dar, M. A. et al. Multiomics technologies: role in disease biomarker discoveries and therapeutics. Brief. Funct. Genomics 22, 7696 (2023).
Article CAS PubMed Google Scholar
Martnez-Garca, M. & Hernndez-Lemus, E. Data integration challenges for machine learning in precision medicine. Front. Med. 8, 784455 (2021).
Article Google Scholar
Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 10261038 (2022).
Article PubMed Google Scholar
Milam, M. E. & Koo, C. W. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin. Radiol. 78, 115122 (2023).
Article CAS PubMed Google Scholar
Goldsmith, J. et al. The emergence and future of public health data science. Public Health Rev. https://doi.org/10.3389/phrs.2021.1604023 (2021).
Cohen, I. G., Lynch, H. F., Vayena, E. & Gasser, U. Big Data, Health Law, and Bioethics (Cambridge University Press, 2018).
World Health Organization. Atlas of African health statistics 2022: health situation analysis of the WHO African Region. (World Health Organization. Regional Office for Africa, 2022).
Anon. in The Economist (The Economist Group, London, 2020).
World Health Organization. World health statistics 2022: monitoring health for the SDGs, sustainable development goals, https://www.who.int/publications-detail-redirect/9789240051157 (2022).
Gao, Y., Sharma, T. & Cui, Y. Addressing the challenge of biomedical data inequality: an Artificial Intelligence Perspective. Annu. Rev. Biomed. Data Sci. https://doi.org/10.1146/annurev-biodatasci-020722-020704 (2023).
Owoyemi, A., Owoyemi, J., Osiyemi, A. & Boyd, A. Artificial intelligence for healthcare in Africa. Front. Digital Health 2, 6 (2020).
Article Google Scholar
Ezugwu, A. E., Oyelade, O. N., Ikotun, A. M., Agushaka, J. O. & Ho, Y.-S. Machine learning research trends in Africa: a 30 years overview with bibliometric analysis review. Arch. Comput. Methods Eng. 30, 41774207 (2023).
Bellemo, V. et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digital Health 1, e35e44 (2019).
Article PubMed Google Scholar
Botwe, B. O. et al. The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers. Radiography 27, 861866 (2021).
Article CAS PubMed Google Scholar
Guo, J. & Li, B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2, 174181 (2018).
Article PubMed PubMed Central Google Scholar
Wani, S. U. D. et al. Utilization of Artificial Intelligence in disease prevention: diagnosis, treatment, and implications for the healthcare workforce. Healthcare 10, 608 (2022).
Article PubMed PubMed Central Google Scholar
Zou, J. & Schiebinger, L. Ensuring that biomedical AI benefits diverse populations. EBioMedicine 67, 103358 (2021).
Article PubMed PubMed Central Google Scholar
Kaushal, A., Altman, R. & Langlotz, C. Health care AI systems are biased. Sci. Am. 11, 17 (2020).
Google Scholar
Data Science for Health Discovery and Innovation in Africa. Our Projects https://dsi-africa.org/projects (2023).
Consortium, H. A. et al. Research capacity. Enabling the genomic revolution in Africa. Science 344, 13461348 (2014).
Article Google Scholar
de Vries, J. et al. Addressing ethical issues in H3Africa researchthe views of research ethics committee members. HUGO J. 9, 14 (2015).
Article PubMed Central Google Scholar
Bentley, A. R., Callier, S. L. & Rotimi, C. N. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom. Med. 5, 5 (2020).
Article PubMed PubMed Central Google Scholar
Ozulumba, T. Sustaining breakthroughs in health research in Africa, https://www.nature.com/articles/d44148-021-00124-y (2021).
Millum, J., Grady, C., Keusch, G. & Sina, B. Introduction: the Fogarty International Research Ethics Education and Curriculum Development Program in historical context. J. Empir. Res. Hum. Res. Ethics 8, 316 (2013).
Article PubMed PubMed Central Google Scholar
Millum, J., Sina, B. & Glass, R. International research ethics education. JAMA 313, 461462 (2015).
Article CAS PubMed PubMed Central Google Scholar
Ndebele, P. et al. Research ethics capacity building in Sub-Saharan Africa: a review of NIH Fogarty-funded programs 2000-2012. J. Empir. Res. Hum. Res. Ethics 9, 2440 (2014).
Article PubMed PubMed Central Google Scholar
Hummel, P., Adam, T., Reis, A. & Littler, K. Taking stock of the availability and functions of National Ethics Committees worldwide. BMC Med. Ethics 22, 56 (2021).
Article PubMed PubMed Central Google Scholar
Kohler, J., Reis, A. A. & Saxena, A. A survey of national ethics and bioethics committees. Bull. World Health Organ. 99, 138147 (2021).
Article PubMed Google Scholar
Bedeker, A. et al. A framework for the promotion of ethical benefit sharing in health research. BMJ Glob. Health 7, e008096 (2022).
Article PubMed PubMed Central Google Scholar
Staunton, C. & de Vries, J. The governance of genomic biobank research in Africa: reframing the regulatory tilt. J. Law Biosci. 7, lsz018 (2020).
Article PubMed PubMed Central Google Scholar
Chaudhry, I. et al. Strengthening ethics committees for health-related research in sub-Saharan Africa: a scoping review. BMJ Open 12, e062847 (2022).
Article PubMed PubMed Central Google Scholar
Sudoi, A., De Vries, J. & Kamuya, D. A scoping review of considerations and practices for benefit sharing in biobanking. BMC Med. Ethics 22, 102 (2021).
Article PubMed PubMed Central Google Scholar
Moodley, K. et al. Ethics and governance challenges related to genomic data sharing in southern Africa: the case of SARS-CoV-2. Lancet Glob. Health 10, e1855e1859 (2022).
Article CAS PubMed PubMed Central Google Scholar
Metcalf, J. & Crawford, K. Where are human subjects in Big Data research? The emerging ethics divide. Big Data Soc. 3, 2053951716650211 (2016).
Article Google Scholar
Ada Lovelace Institute. Looking before we leap: Ethical review processes for AI and data science research. (Ada Lovelace Institute, Exeter, UK, 2022).
Ferretti, A., Ienca, M., Velarde, M. R., Hurst, S. & Vayena, E. The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study. J. Empir. Res. Hum. Res. Ethics 17, 129143 (2022).
Article PubMed Google Scholar
Mahomed, S. & Labuschaigne, M. L. The evolving role of research ethics committees in the era of open data. S. Afr. J. Bioethics Law https://doi.org/10.7196/SAJBL.2022.v15i3.XX (2023).
Silaigwana, B. & Wassenaar, D. Biomedical Research Ethics Committees in Sub-Saharan Africa: A Collective Review of Their Structure, Functioning, and Outcomes. J. Empir. Res. Hum. Res. Ethics 10, 169184 (2015).
Article PubMed Google Scholar
Bain, L. E., Ebuenyi, I. D., Ekukwe, N. C. & Awah, P. K. Rethinking research ethics committees in low- and medium-income countries. Res. Ethics 14, 17 (2018).
Article Google Scholar
National Health Research Ethics Committee of Nigeria. (Federal Ministry of Health of Nigeria, Abuja, 2007).
Leonelli, S. Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 374, 20160122 (2016).
Article ADS Google Scholar
Terry, R., Littler, K. & Olliaro, P. Sharing health research data? the role of funders in improving the impact [version 2; peer review: 3 approved]. F1000Research https://doi.org/10.12688/f1000research.16523.2 (2018).
Greenleaf, G. & Cottier, B. Data privacy laws and Bills: Growth in Africa, GDPR influence. GDPR Influence 152, 1113 (2018).
Google Scholar
Daigle, B. Data protection laws in Africa: a pan-African survey and noted trends. J. Intl Com. Econ. 1, 127 (2021).
McCall, B. What does the GDPR mean for the medical community? Lancet 391, 12491250 (2018).
The rest is here:
The promise of data science for health research in Africa - Nature.com
The Future of Data Science: Emerging Trends and Technologies – Medium
Future of Data Science: Digicrome Academy
Data science has come a long way, evolving from a buzzword to a crucial discipline that influences every aspect of our lives. From healthcare to business, data science is transforming the way we make decisions and understand the world around us. But what does the future hold for this dynamic field? In this article, we will explore the exciting emerging trends and technologies that are shaping the future of data science.
Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of the data science revolution. These technologies are becoming more powerful and accessible, allowing data scientists to build smarter and more efficient models. In the future, we can expect AI and ML to play an even more significant role in data-driven decision-making.
AI and ML will continue to enhance automation, enabling systems to make real-time predictions and recommendations. Whether its personalized marketing, healthcare diagnostics, or autonomous vehicles, AI and ML are poised to revolutionize various industries.
Big Data and Cloud Computing
As our world becomes increasingly digital, the volume of data generated is growing exponentially. Big Data technologies and cloud computing platforms are essential for managing and processing this vast amount of information. The future of data science will see even more sophisticated tools and techniques for handling Big Data.
Cloud-based data storage and processing solutions will become more accessible, allowing organizations of all sizes to harness the power of Big Data. This will enable quicker decision-making and more in-depth insights, driving innovation in numerous fields.
Explainable AI
While AI and ML offer incredible capabilities, their decision-making processes can sometimes seem like a black box, making it challenging to understand why a particular decision was made. In the future, explainable AI (XAI) will gain prominence. XAI aims to provide transparency and clarity in AI models, making it easier for humans to trust and interpret their decisions.
XAI will be crucial in applications where accountability and ethics are paramount, such as healthcare and finance. As AI continues to integrate into our lives, understanding its decisions will be essential.
Edge Computing
Edge computing involves processing data closer to where it is generated, rather than sending it to a centralized data center. This approach reduces latency and is especially important for applications that require real-time processing, like autonomous vehicles and IoT devices.
The future of data science will see increased focus on edge computing, enabling more efficient and responsive systems. This trend will also address privacy concerns by keeping sensitive data closer to its source.
Natural Language Processing (NLP)
NLP is a subfield of AI that focuses on enabling computers to understand and interact with human language. We already see NLP in action with virtual assistants like Siri and chatbots. However, the future will bring even more sophisticated NLP applications.
Advanced NLP models will improve language translation, sentiment analysis, and content recommendation. This will enhance communication and information access on a global scale.
Quantum Computing
Quantum computing is still in its infancy, but it holds the promise of solving complex problems exponentially faster than classical computers. In the future, quantum computers will revolutionize data science by handling massive datasets and optimization tasks that are currently beyond our capabilities.
Although quantum computing is not yet widely accessible, its an area to watch as it could transform data science and many other fields.
Responsible AI and Data Ethics
As data science continues to advance, questions of ethics and responsibility become increasingly important. The future will see more robust frameworks for ensuring that AI and data science are used in ways that are fair, unbiased, and respect privacy.
Regulations and guidelines will evolve to keep pace with technological developments. Responsible AI practices will be integral to building trust with users and stakeholders.
Conclusion
The future of data science is bright and full of possibilities. Emerging technologies like AI, Big Data, XAI, edge computing, NLP, and quantum computing are poised to reshape the way we collect, analyze, and interpret data. However, with great power comes great responsibility. Ensuring that these technologies are used ethically and responsibly will be a critical challenge in the years to come.
As data science continues to evolve, its impact on society will grow exponentially. From healthcare breakthroughs to more personalized shopping experiences, the future of data science promises to make our lives more efficient, convenient, and insightful. The only limit to its potential is our imagination and our commitment to using it for the greater good. So, lets embrace these emerging trends and technologies in data science and look forward to a brighter, data-driven future.
More:
The Future of Data Science: Emerging Trends and Technologies - Medium
$3M award boosts program developing well-rounded graduate data … – University of Hawaii
UH Mnoa National Science Foundation Research Traineeship team
A new University of Hawaii at Mnoa graduate education program that is designed to train the next generation of data scientists in a multitude of skill sets to benefit the state, nation and the world earned a five-year, $3 million boost from the National Science Foundation (NSF).
The NSF Research Traineeship award will allow a cross-disciplinary group of researchers to develop an innovative graduate program that brings together engineering, computer science, social science, business and medicine to harness the power of data science. According to the U.S. Bureau of Labor Statistics, the employment of data scientists is projected to grow 35% from 2022 to 2032, much faster than the average for all occupations.
Currently in a time of extraordinary technological progress, particularly in data science and artificial intelligence, economic and other challenges, including the COVID-19 pandemic, have delayed the benefits of this progress for a majority of the population. In our state, progress in data science has the potential to address critical needs in power, transportation, healthcare and communications. To benefit Hawaiis residents, however, technological progress must consider economic, business and social factors.
The new cross-disciplinary program plans to train a new generation of 61 graduate students, including 41 funded trainees. The program incorporates novel mechanisms such as tailored course modules to better prepare the trainees with the unique skills needed for bringing about an inclusive data revolution and equips them with a broader perspective on the interplay between areas traditionally treated disparately in a STEM graduate curriculum.
The award is a culmination of efforts of many of my colleagues, including many not on this specific grant, to institutionalize novel research and education paradigms over several years, said project Principal Investigator and Professor Narayana Santhanam from the Department of Electrical and Computer Engineering in the College of Engineering. The grant will allow us to move in the direction of personalizing graduate instruction and allow students to see for themselves how seemingly different areas are interconnected. It will foster significantly closer research between different units and engage underrepresented groups in STEM, establishing long term mentoring structures that will persist beyond the life of the project.
Data science is becoming a core competency for many disciplines across UH Mnoa, said project co-Principal Investigator and Professor Philip Johnson from the Information and Computer Sciences Department in the College of Natural Sciences. This grant will allow us to explore novel ways to break down silos and facilitate multi-disciplinary research and education for our students.
The students will work in teams on fundamental and applied data science research on the following topics:
To solve pressing societal issues, we need to better understand and analyze human and social behavior backed by state-of-the-art data science, said project co-Principal Investigator and Professor Nori Tarui of the Department of Economics in the College of Social Sciences. With this grant, our interdisciplinary team of faculty members and students will work together with various partners toward practical, evidence-based solutions.
The program will also create outreach activities for Native Hawaiians, women and members of the military to broaden participation in the STEM workforce.
Traditional education programs have to consider the tradeoff between depthhow deep to cover conceptsand breadthhow many concepts and applications to cover, said project co-Principal Investigator and Assistant Professor June Zhang from the Department of Electrical and Computer Engineering. This grant will allow us to utilize novel educational tools like modules and knowledge bases to build a program that can offer students both depth and breadth in learning and research.
Read the original here:
$3M award boosts program developing well-rounded graduate data ... - University of Hawaii
Analytics and Data Science News for the Week of September 29 … – Solutions Review
Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of September 29, 2023.
Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.
CelerDatas real-time, open source OLAP database StarRocks is one of the few options in this space that dynamically performs join operations on tables with low latency data. Because of its architecture, this real-time database is considerably more flexible, swifter, and cost-effective than many of its competitors are.
Read on for more.
With this launch, you can deploy open-source or your own custom AI models of any type, including LLMs and Vision models, on the Lakehouse Platform. Databricks Model Serving automatically optimizes your model for LLM Serving, providing best-in-class performance with zero configuration.
Read on for more.
LLM Mesh provides the components companies need to build safe applications using LLMs at scale efficiently. With the LLM Mesh sitting between LLM service providers and end-user applications, companies can choose the most cost-effective models for their needs, both today and tomorrow, ensure the safety of their data and responses, and create reusable components for scalable application development.
Read on for more.
This platform helps accelerate AI time-to-value so teams within and across agencies can quickly onboard users, including contractors, and connect them with a broad, flexible range of innovative data science tools. Domino also connects instantly with infrastructure that is critical to build and operate AI whether in data centers, in clouds or at the edge.
Read on for more.
Groopits emphasis on high provenance data sets it apart. By collecting insights from employees those immersed in the intricacies of their roles and industry nuances Groopit guarantees data of the highest caliber. High-quality human intelligence is then analyzed by Groopits advanced AI capabilities.
Read on for more.
Business users can seamlessly integrate and manipulate disparate data in real time and import rich information into preferred analytics tools. Data scientists gain access to previously inaccessible mainframe data, enabling them to create custom queries without SQL and work with deeper, more complex datasets in real time for comprehensive analytics.
Read on for more.
This release focuses on new features that enhance viewing, like the Report Server mobile view port improvements, full screen for PBIX and RDL viewing, and mobile layout switcher. These features have been inclusively designed with better UX for report viewing and sharing.
Read on for more.
Joule will be embedded throughout SAPs cloud enterprise portfolio, delivering proactive and contextualized insights from across the breadth and depth of SAP solutions and third-party sources. By quickly sorting through and contextualizing data from multiple systems to surface smarter insights, Joule helps people get work done faster and drive better business outcomes in a secure, compliant way.
Read on for more.
Compose SDK for Fusion is a flexible development toolkit that gives developers and product leaders tools to embed context-aware analytics in a code-first, scalable, and modular way. With over 15 years as a leader in embedded analytics, offering developers full access to their battle-proven APIs marks the beginning of a new, developer-first era for Sisense, with more developer-centric offers coming in 2024.
Read on for more.
These new features including no code capabilities, as well as robust new governance and AI explainability controls enable businesses to accelerate, scale, and optimize AI/ML deployments to quickly generate business value from their AI investments.
Read on for more.
Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the webs leading insights for enterprise technology practitioners.
With the next Spotlight event, the team at Solutions Review has partnered with leading data movement tools provider Upsolver. The vendor will dive into the urgent need for application developers to deliver fresh, high-quality data to the warehouse or lake in a form ready for analytics and machine learning.
Read on for more.
For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.
Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.
Read the original post:
Analytics and Data Science News for the Week of September 29 ... - Solutions Review