Category Archives: Data Science
UW-Madison computer and data science building still short $15 million – Daily Cardinal
The School of Computer, Data & Information Sciences (CDIS) at the University of Wisconsin-Madison is currently facing a $15 million budget shortage for construction of its new building, set to open in 2025.
With the final price of the privately funded building coming to $260 million and current funding standing at $245 million, the additional $15 million would bring the project to completion.
CDIS launched a new fundraising program on March 5 called the Badger Effect as a way of countering this shortage, according to UW-Madison. The Badger Effects goal is to raise $1 million by awarding tiles in the buildings donor mosaic to the first 500 donors who contribute $2,019 or more to the building, an amount referencing CDIS founding year.
The Badger Effect comes at an important moment as CDIS prepares to open its $260 million state-of-the-art building in 2025, which is completely privately funded and would not be possible without the generosity of donors to whom we are extremely grateful, CDIS Director of Advancement Shannon Timm said in a statement to The Daily Cardinal.
For Timm, this programs goals go beyond simply securing funding for the CDIS community and extend to broader campus culture.
[The program] is an opportunity for our broad community to leave their mark in the new space and demonstrate their collective support to our students, faculty and staff who will see the donor wall each and every day, Timm said. The campaign is about inclusivity and recognizing the collective power of our community when Badgers come together, they can yield monumental results.
The new CDIS building is not the only recent UW System building project to receive substantial private funding.
A new engineering building at UW-Madison was recently approved for $197 million in state funding after being embroiled in partisan controversy for nearly a year. The remainder of the engineering buildings $347 million price tag will come from private donations raised by UW-Madison.
The Hamel Music Center, which opened in 2019, was funded in part by a $15 million grant from George Hamel and a $25 million grant from the Mead Witter Foundation before reaching its budget of $55.8 million through other grants.
Levy Hall, the new building for the College of Letters and Science expected to open in 2026, has received $20 million from the sons of Irving and Dorothy Levy and $15 million in other gifts. The remainder of its roughly $60 million cost will come from the state.
A November report from Inside Higher Ed found increased reliance on private donors in higher education gives donors more leverage in the operations of universities.
The report detailed recent high-profile incidents at Ivy League universities like Harvard University and the University of Pennsylvania, where some donors withheld funds over concerns of antisemitism on campus.
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Decades of dependency on philanthropic giving has weakened academic institutions, meaning that a highly polarizing event like this one can leave them particularly vulnerable to efforts by wealthy individuals to shape campus speech, Trinity College political science professor Isaac Kamola told Inside Higher Ed.
In Wisconsin, though, the last decade has been a choice between donors and Republican state lawmakers, the latter of whom recently used its financial power to leverage university policy changes.
The UW-Madison engineering building gained state funding only after the UW Board of Regents agreed to cap diversity, equity and inclusion (DEI) positions at the behest of Assembly Speaker Robin Vos, R-Rochester.
The Republican-controlled state Senate earlier this month fired two regents who were against the deal.
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UW-Madison computer and data science building still short $15 million - Daily Cardinal
Facebook Prophet : All you need to know – DataScientest
Since its launch, Facebook Prophet has made a name for itself in the e-commerce industry. Based on past sales data, seasonal trends and special events such as promotions or sales, this Python library can provide accurate forecasts.
This enables companies to better manage their inventories, plan their marketing campaigns and anticipate peaks in activity.
Similarly, in the financial sector, Prophet has changed everything by making it possible to analyze historical revenue and profit data to identify trends and seasons.
With this crucial performance forecasting information, decision-makers are able to anticipate periods of growth and slowdown for budget planning and investment choices.
Investors and other financial institutions can also use it to predict fluctuations in share prices, exchange rates or commodity prices. In this way, they can minimize risk.
In the healthcare sector, the tool can be used to predict time series of medical data. This includes, for example, hospital admissions, medical consultations and infection rates.
As a result, hospitals can better plan their resources, anticipate peak activity periods, and ultimately provide better patient care.
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PyCaret: Everything you need to know about this Python library – DataScientest
Pycaret is an open source, low-code Machine Learning library based on Python. This solution automates the end-to-end machine learning workflow. By automating tasks and managing ML models, PyCaret speeds up the experimentation cycle. As a result, data scientists are much more productive and able to develop even more powerful machine learning models.
PyCaret is more than just a Python-based ML library. And for good reason, it encompasses several machine learning libraries and frameworks. For example: scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, etc.
Best of all, its a ready-to-deploy Python library. In other words, every step of an ML experience can be reproduced from one environment to another.
Good to know: PyCaret also integrates with many other solutions, such as Microsoft Power BI, Tableau, Alteryx and KNIME. So you can add a layer of machine learning to all your business intelligence work. And its easy to do.
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PyCaret: Everything you need to know about this Python library - DataScientest
Grafana: Everything you need to know about this data analysis solution – DataScientest
Grafana is an open source tool for performing data analysis, obtaining metrics that make sense of huge amounts of data, and monitoring your applications with customizable dashboards. Being open source, it also lets you write plugins from scratch for integration with many different data sources.
Grafana connects to all possible data sources, commonly known as databases such as Prometheus, ElasticSearch, MySQL, PostgreSQL, Graphite, Influx DB and OpenTSDB.
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The tool helps you study, analyze and monitor data over a period of time, technically known as time series analysis.
It allows you to track user behavior, application behavior, the frequency of errors that appear in a production or pre-prod environment, the type of errors that appear and contextual scenarios by providing relative data.
One of the great advantages of Grafana is that it can be deployed on site. This is advantageous for organizations that dont want their data to be transmitted to a cloud provider for security and other reasons. Over time, this framework has grown in popularity and is deployed by big names such as PayPal, eBay, Intel and many others.
In addition to the fact that this solution is open source, the Grafana team offers two other services for businesses: Grafana Cloud and Grafana Enterprise.
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Grafana: Everything you need to know about this data analysis solution - DataScientest
Upwork Unveils Most In-Demand Work Skills in 2024 – GlobeNewswire
SAN FRANCISCO, Calif., March 19, 2024 (GLOBE NEWSWIRE) -- Upwork (Nasdaq: UPWK), the worlds work marketplace that connects businesses with independent talent, today announced the most in-demand skills that organizations are expected to seek from skilled professionals in 2024.
As new technologies continue to emerge and companies recognize the need for new skills to stay competitive, Upworks study reveals the extent to which businesses are increasingly turning to skilled freelance professionals to meet key project needs and address skills gaps. While businesses are looking to freelance talent for scale and specialization, independent professionals are also leading the charge in adopting new technologies and upskilling. In particular, the AI & machine learning subcategory on Upwork saw 70% year-over-year growth in the fourth quarter of 2023, as clients and independent professionals collaborate on todays most cutting-edge projects.
Every company is vying for the best talent and there remains huge demand for a broad range of skills across the Upwork marketplace as businesses big and small are finding solutions in the growing reservoir of skilled independent professionals, said Kelly Monahan, managing director of the Upwork Research Institute. In 2024, emergent technologies like generative AI are having a major impact on the skills-based economy. Of course business demand for these types of skills is increasing, but were also seeing a complementary impact, whereby AI technology is driving greater demand for all types of work across our marketplace.
Among the lists of most in-demand skills, Data Science & Analytics is one of the fastest-growing types of work, and generative AI modeling and machine learning were the two fastest-growing skills in that category. Additionally, notable new skills to make the top 10 most in-demand skills lists this year include medical and executive virtual assistance, as well as development & IT project management and digital marketing campaign management.
As technology rapidly changes and more specific expertise is needed, more and more businesses are coming to Upwork to find the solutions they need, said Jacqueline DeStefano-Tangorra, an AI consultant on Upwork. Consequently, the demand for my skill set has never been higher. Upskilling and becoming an AI professional on Upwork has opened many doors. I get to work on interesting projects and I am a stronger partner for my clients as Im more efficient, productive, and can deliver better outcomes.
Here are the in-demand work skills for 2024:
Upworks Data Science & Analytics Skills for 2024:
Upworks Coding & Web Development Skills for 2024:
Upworks Sales & Marketing Skills for 2024:
Upworks Accounting & Consulting Skills for 2024:
Upworks Customer Service & Admin Support Skills for 2024:
Upworks Design & Creative Skills for 2024:
Connect with skilled, global, in-demand talent on Upworks work marketplace. For further information on these skills and how to apply them, tune into an online Q&A with Kelly Monahan, managing director of Upworks Research Institute, on April 10, 2024 at 9:00 a.m. PST/12:00 p.m. EST on our community events page.
Methodology
Skills data was sourced from the Upwork database and is based on U.S. freelancer earnings from January 1, 2023 to December 31, 2023. Each skill had a minimum of 250 projects with active work during the period. Year-over-year growth was estimated by comparing freelancer earnings in the full year 2023 to freelancer earnings over the same period in 2022.
About Upwork
Upwork is the worlds work marketplace that connects businesses with independent talent from across the globe. We serve everyone from one-person startups to large, Fortune 100 enterprises with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential. Our talent community earned over $3.8 billion on Upwork in 2023 across more than 10,000 skills in categories including website & app development, creative & design, data science & analytics, customer support, finance & accounting, consulting, and operations. Learn more at upwork.com and join us on LinkedIn, Facebook, Instagram, TikTok and X.
Contact: press@upwork.com ________ 1 Note: This skill was not available in 2022, but was the fastest-growing in terms of percentage growth in 2023.
Photos accompanying this announcement are available at https://www.globenewswire.com/NewsRoom/AttachmentNg/91597152-a0cf-4409-ac96-adbb1f816b0bhttps://www.globenewswire.com/NewsRoom/AttachmentNg/2fe2a4fa-fcaa-492e-8ffb-e0524b4c702a
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Upwork Unveils Most In-Demand Work Skills in 2024 - GlobeNewswire
8-hour intermittent fasting tied to 90% higher risk of cardiovascular death, early data hint – Livescience.com
Intermittent fasting a dieting strategy that limits when someone can eat each day has been linked to a 91% higher risk of heart-related death in a large study. This risk was tied to eating in an eight-hour or shorter window in the 24-hour day, compared with a more typical 12- to 16-hour window.
The new, preliminary research was presented March 18 at the American Heart Association (AHA) EPI Lifestyle Scientific Sessions 2024. It looked at deaths from cardiovascular diseases in more than 20,000 U.S. adults, who were followed for an average of eight years.
Experts told Live Science that the study highlights a need to exercise caution around the use of intermittent fasting. However, one noted that, until all the data from the study is published, it's difficult to say whether the time window or types of food consumed are more relevant to a person's risk of death.
"It's quite possible to eat a really low-quality diet while time-restricted eating," Christopher Gardner, professor of medicine at Stanford University, told Live Science. It may also be that some participants with restricted eating windows were facing food insecurity and not eating well or enough, he added. "We don't know everything yet. I'll wait for more," he said of the new research.
Related: 9 heart disease risk factors, according to experts
Intermittent fasting involves eating only during a specific window of time each day, often between four and 12 hours out of 24. Previous research suggests that intermittent fasting improves metrics tied to cardiovascular health in the short term, over a few months, including measures of insulin resistance.
Of the 20,000 people in the new study, 414 reported eating in time windows of eight hours or less each day. Participants were not assigned diets to follow, but rather, their typical diets were assessed through two surveys, in which participants recalled everything they'd eaten in two 24-hour time frames.
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The study uncovered a link between eight-hour eating windows and cardiovascular death, but due to its design, it cannot say whether this eating schedule caused the deaths it shows only a correlation.
"We were surprised to find that compared to people whose eating duration was 12-16 hours those who restricted their eating time within 8 hours per day had higher risk of cardiovascular death and did not live longer," lead study author Victor Wenze Zhong, professor of epidemiology and biostatistics at the Shanghai Jiao Tong University School of Medicine, told Live Science in an email.
Restricting eating to this short time window was tied to a higher cardiovascular death risk in the overall group and in people with either cancer or heart disease, who were singled out in separate analyses. In addition, participants with existing cardiovascular disease who ate during an eight- to 10-hour window also had a 66% higher risk of dying from heart disease or stroke, compared to those with longer eating windows.
Dr. Wendy Bennett, an associate professor of medicine at Johns Hopkins University School of Medicine, noted that this study suggests a need to scrutinize the "fad diet push that people should be doing time-restricted eating." Bennett and others published research in January 2023 that also raised doubts about the purported benefits of intermittent fasting, suggesting that it's not a successful weight-loss strategy in a six-year time window.
Zhong agreed that the new study calls for more caution regarding intermittent fasting, particularly for people with heart conditions or cancer. Overall, though, "it's too early to give a specific recommendation on [time-restricted eating] based on our study alone." He highlighted the need to explore exactly how eight-hour intermittent fasting might affect heart health and to assess additional populations around the world.
The study does have several limitations. For instance, participants provided dietary data through self-reporting, and some may have incorrectly recalled what they'd eaten. Only two diet surveys were used, so it's unknown whether these accurately represented people's long-term eating habits. The study also didn't consider the nutrient quality of participants' diets or reasons they practiced intermittent fasting.
"It looks tantalizing, but there is so much that isn't known," Gardner said, adding that there is a lot of interest in the study's full results.
Setting the new research aside, intermittent fasting can have other negative consequences, Cynthia Bulik, a psychiatry professor who studies eating disorders at the University of North Carolina School of Medicine, told Live Science in an email. For example, despite it being "hailed as the next great strategy for weight control," intermittent fasting can affect a person's familial and social relationships when their eating schedule doesn't align with that of their loved ones, she noted.
"In addition, for individuals who are genetically predisposed to some eating disorders, prolonged fasting periods could theoretically flip them into negative energy balance (expending more energy than they are consuming)," she said, "and trigger the onset of an eating disorder."
This article is for informational purposes only and is not meant to offer medical advice.
Ever wonder why some people build muscle more easily than others or why freckles come out in the sun? Send us your questions about how the human body works to community@livescience.com with the subject line "Health Desk Q," and you may see your question answered on the website!
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The Road to Biology 2.0 Will Pass Through Black-Box Data – Towards Data Science
AI-first Biotech This year marks perhaps the zenith of expectations for AI-based breakthroughs in biology, transforming it into an engineering discipline that is programmable, predictable, and replicable. Drawing insights from AI breakthroughs in perception, natural language, and protein structure prediction, we endeavour to pinpoint the characteristics of biological problems that are most conducive to being solved by AI techniques. Subsequently, we delineate three conceptual generations of bio AI approaches in the biotech industry and contend that the most significant future breakthrough will arise from the transition away from traditional white-box data, understandable by humans, to novel high-throughput, low-cost AI-specific black-box data modalities developed in tandem with appropriate computational methods. 46 min read
This post was co-authored with Luca Naef.
The release of ChatGPT by OpenAI in November 2022 has thrust Artificial Intelligence into the global public spotlight [1]. It likely marked the first instance where even people far from the field realised that AI is imminently and rapidly altering the very foundations of how humans will work in the near future [2]. A year down the road, once the limitations of ChatGPT and similar systems have become better understood [3], the initial doom predictions ranging from the more habitual panic about future massive job replacement by AI to declaring OpenAI as the bane of Google, have given place to impatience why is it so slow?, in the words of Sam Altman, the CEO of OpenAI [4]. Familiarity breeds contempt, as the saying goes.
We are now seeing the same frenetic optimism around AI in the biological sciences, with hopes that are probably best summarised by DeepMind
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The Road to Biology 2.0 Will Pass Through Black-Box Data - Towards Data Science
Introducing Seaborn Objects: One Ring to Rule Them All! – Towards Data Science
Quick Success Data Science One plotting ring to rule them all! One ring to Plot them all (by Dall-E2)
Have you started using the new Seaborn Objects System for plotting with Python? You definitely should; its a wonderful thing.
Introduced in late 2022, the new system is based on the Grammar of Graphics paradigm that powers Tableau and Rs ggplot2. This makes it more flexible, modular, and intuitive. Plotting with Python has never been better.
In this Quick Success Data Science project, youll get a quick start tutorial on the basics of the new system. Youll also get several useful cheat sheets compiled from the Seaborn Objects official docs.
Well use the following open-source libraries for this project: pandas, Matplotlib, and seaborn. You can find installation instructions in each of the previous hyperlinks. I recommend installing these in a virtual environment or, if youre an Anaconda user, in a conda environment dedicated to this project.
The goal of Seaborn has always been to make Matplotlib Pythons primary plotting library both easier to use and nicer to look at. As part of this, Seaborn has relied on declarative plotting, where much of the plotting code is abstracted away.
The new system is designed to be even more intuitive and to rely less on difficult Matplotlib syntax. Plots are built incrementally, using interchangeable marker types. This reduces the number of things you need to remember while allowing for a logical, repeatable workflow.
The use of a modular approach means you dont need to remember a dozen or more method names like barplot() or scatterplot() to build plots. Every plot is now initiated with a single Plot() class.
The Plot() class sets up the blank canvas for your graphic. Enter the following code to see an example (shown using JupyterLab):
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Introducing Seaborn Objects: One Ring to Rule Them All! - Towards Data Science
Largest-ever map of universes active superma – EurekAlert
image:
An infographic explaining the creation of a new map of around 1.3 million quasars from across the visible universe.
Credit: ESA/Gaia/DPAC; Lucy Reading-Ikkanda/Simons Foundation; K. Storey-Fisher et al. 2024
Astronomers have charted the largest-ever volume of the universe with a new map of active supermassive black holes living at the centers of galaxies. Called quasars, the gas-gobbling black holes are, ironically, some of the universes brightest objects.
The new map logs the location of about 1.3 million quasars in space and time, the furthest of which shone bright when the universe was only 1.5 billion years old. (For comparison, the universe is now 13.7 billion years old.)
This quasar catalog is different from all previous catalogs in that it gives us a three-dimensional map of the largest-ever volume of the universe, says map co-creator David Hogg, a senior research scientist at the Flatiron Institutes Center for Computational Astrophysics in New York City and a professor of physics and data science at New York University. It isnt the catalog withthe most quasars, and it isnt the catalog with the best-quality measurements of quasars, but it is the catalog with the largest total volume of the universe mapped.
Hogg and his colleagues present the map in a paper published March 18 in The Astrophysical Journal. The papers lead author, Kate Storey-Fisher, is a postdoctoral researcher at the Donostia International Physics Center in Spain.
The scientists built the new map using data from the European Space Agencys Gaia space telescope. While Gaias main objective is to map the stars in our galaxy, it also inadvertently spots objects outside the Milky Way, such as quasars and other galaxies, as it scans the sky.
We were able to make measurements of how matter clusters together in the early universe that are as precise as some of those from major international survey projects which is quite remarkable given that we got our data as a bonus from the Milky Wayfocused Gaia project, Storey-Fisher says.
Quasars are powered by supermassive black holes at the centers of galaxies and can be hundreds of times as bright as an entire galaxy. As the black holes gravitational pull spins up nearby gas, the process generates an extremely bright disk and sometimes jets of light that telescopes can observe.
The galaxies that quasars inhabit are surrounded by massive halos of invisible material called dark matter. By studying quasars, astronomers can learn more about dark matter, such as how much it clumps together.
Astronomers can also use the locations of distant quasars and their host galaxies to better understand how the cosmos expanded over time. For example, scientists have already compared the new quasar map with the oldest light in our cosmos, the cosmic microwave background. As this light travels to us, it is bent by the intervening web of dark matter the same web mapped out by the quasars. By comparing the two, scientists can measure how strongly matter clumps together.
It has been very exciting to see this catalog spurring so much new science, Storey-Fisher says. Researchers around the world are using the quasar map to measure everything from the initial density fluctuations that seeded the cosmic web to the distribution of cosmic voids to the motion of our solar system through the universe.
The team used data from Gaias third data release, which contained 6.6 million quasar candidates, and data from NASAs Wide-Field Infrared Survey Explorer and the Sloan Digital Sky Survey. By combining the datasets, the team removed contaminants such as stars and galaxies from Gaias original dataset and more precisely pinpointed the distances to the quasars. The team also created a map showing where dust, stars and other nuisances are expected to block our view of certain quasars, which is critical for interpreting the quasar map.
This quasar catalog is a great example of how productive astronomical projects are, says Hogg. Gaia was designed to measure stars in our own galaxy, but it also found millions of quasars at the same time, which give us a map of the entire universe.
ABOUT THE FLATIRON INSTITUTE
The Flatiron Institute is the research division of the Simons Foundation. The institute's mission is to advance scientific research through computational methods, including data analysis, theory, modeling and simulation. The institute's Center for Computational Astrophysics creates new computational frameworks that allow scientists to analyze big astronomical datasets and to understand complex, multi-scale physics in a cosmological context.
The Astrophysical Journal
Observational study
Not applicable
18-Mar-2024
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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Understanding Impact of Advanced Retrievers on RAG Behavior through Visualization – Towards Data Science
13 min read
LLMs have become adept at text generation and question-answering, including some smaller models such as Gemma 2B and TinyLlama 1.1B. Even with such performant pre-trained models, they may not perform well when queried about some documents not seen during training. In such a scenario, supplementing your question with relevant context from the documents is an effective approach. This approach termed Retrieval-Augmented Generation (RAG) has gained significant popularity, due to its simplicity and effectiveness.
Retriever is a key component of a RAG system, which involve obtaining relevant document chunks from a back end vector store. In a recent survey paper on the evolution of RAG systems, the authors have classified such systems into three categories, namely Naive, Advanced and Modular [1]. Within the advanced category, post-retrieval optimization techniques such summarizing as well as re-ranking retrieved documents have been identified as some key improvement techniques over the naive approach.
In this article, we will look at how a naive retriever as well as two advanced retrievers influence RAG behavior. To better represent and characterize their influence, we will be visualizing the document vector space along with the related documents in 2-D using visualization library, renumics-spotlight. This library boasts powerful features to visualize the intricacies of document embeddings, and yet it is easy to use. And for our LLM of choice, we will be using TinyLlama 1.1B Chat, a compact model, but without a proportional drop in accuracy [2]. It makes this LLM ideal for rapid experimentation.
Disclaimer: I dont have any affiliation with Renumics or its creators. This article provides an unbiased view of the library usage based on my personal experience with the intention to make its knowledge available to the masses.
Table of Contents1.0 Environment and Key Components 2.0 Design and Implementation 2.1 Module LoadVectorize 2.2 The main Module 3.0 Knobs on Spotlight UI 4.0 Comparison of Retrievers 5.0 Closing Remarks
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