Category Archives: Data Science
Freakonomics author: ‘Objections to data science in K-12 education … – Yahoo Finance
The three-year battle over Californias new math framework has produced calamity and confusion on all fronts. As fighting raged across op-ed pages and X (formerly known asTwitter), the fog of war obscured the inescapable truth: The data revolution is here and our kids are not prepared for it.
From ChatGPT to personal finance, nearly every decision we make in our daily lives is now dominated by data. Eight out of ten of the fastest-growing careers this year involve data science. A decade from now, it will be difficult to find any job that is not data-driven.
We need to equip our students for this new reality by teaching them basic data literacy in K-12. We can all see this, but somehow the politics of the moment have turned this idea into a raging debate.
The new critics of data science instruction seem to have three common objections. Their first claim is that data science programs are somehow watering down math. That is indeed possible, especially if districts treat data-related classes as a form of remediation, but this should not be the case. Data science is a very challenging subject, combining traditional math, statistics, computer programming, and complex datasets. In many ways, it demands more of students, requiring critical thinking, creativity, and a nuanced understanding of the context within which data have been generated.
A second objection is that learning data science in high school is somehow illegitimate because students wont yet have the mathematics skills required of professional data scientists. This is an odd argument. Can high school students never learn anything about physics because they dont understand differential calculus? Can they not find beauty in a Shakespearean sonnet if they dont know the rules of iambic pentameter?
The third claim is that data science coursework will crowd out calculus or some of the other math required for college STEM degrees. This is an important concern, but it assumes that every part of todays curriculum is absolutely critical to that path. Do we really think that is true? Having spent many nights at the kitchen table helping my kids with their homework, I suspect its not. And we (parents) shouldnt ignore the more than 130 college disciplines that now require data and statistics basics as the world changesincluding math and engineering.
Story continues
We adults can stand around and dither, but young people are not waiting for us to figure this out. In college, students are rushing toward data science courses with astonishing speed. The number of data science undergraduate degree programs has exploded nationallyandin every state. At the University of Wisconsin, Madison, it has quickly becomethe fastest-growing major. Not to be outdone, UC Berkeley recently launchedan entire collegededicated to the subject. Our own institution, the University of Chicago, has hired 25 faculty in data science to keep up with student demand.
Sixteen other states have already officially launched or recommended data science in K-12. Some are creating full-year courses, while others are completely redesigning their math pathways. Leading STEM high schools throughout the country are teaching their students the UC Berkeley Data8 program, one of the best collegiate data science courses in the country. Just recently, a group of AP Statistics teachersorganizeda national data science challenge that attracted more than 5,000 students.
Without leadership from policymakers and educators, this revolution will still happen, but the benefits will go disproportionately to the students who are already advantaged. Wealthy parents and tech employees will teach their kids these skills through summer and after-school programs. Is this what we want? Or do we want to ensure that every child gets at least a basic level of data literacy?
If this all rings true to you, if you believe that a modern K-12 education requires at least some data science instruction, then you can help move us toward action. Ask your local school to incorporate data across school subjects throughout K-12. Ask your teachers to bring modern data tools into the way they teach. And ask your school leaders to offer data-focused math coursesand support their educators with the right resources to do so.
Lets put down our weapons in this math war and start fighting again for our kids futures.
StevenLevitt is an economist, the founder of The Center for Radical Innovation for Social Change (RISC) at UChicago, and the author of Freakonomics.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs ofFortune.
This story was originally featured on Fortune.com
Follow this link:
Freakonomics author: 'Objections to data science in K-12 education ... - Yahoo Finance
Data Science Program and Department of Music Launch Industry … – University of Arkansas Newswire
Photo Submitted
The Department of Music, in partnership with theData Science Program, recently launched an innovative, first-of-its-kind music industry concentration.
This unique concentration is the only one of its kind nationally, and it incorporates music into the field of data science to create a multidisciplinary curriculum that allows students to explore both their artistry and technical literacy.
Jake Hertzog, assistant professor of guitar and jazz area coordinator, wantedDepartment of Musicstudents to have the opportunity to explore the business side of the music industry. He also saw the need to open creative programs up to non-performers.
"This new concentration broadens the scope of music education," Hertzog said. "It allows students interested in music to be industry leaders, growing alongside an increasingly technical field and supporting our mission of fostering 21st century leaders in the musical world."
Students of this program will graduate with a B.S. in data science with a concentration inMusic Industry Data Analytics (MIDA). While all data science students take the same core courses, each student is also asked to officially declare a concentration, like MIDA, which then functions like a built-in minor.
Several students have already shown interest in the MIDA concentration, including Breck Husong, a sophomore data science student from Cave Springs.
"The MIDA concentration is actually what drew me to the Data Science Program," Husong said. "As a lifelong musician who never had plans on making a career out of performance, the arrival of the MIDA concentration was a pleasant surprise.I really enjoy programming, so the chance of being able to still program while being connected to the music industry was very appealing to me."
Karl Schubert, associate director of the Data Science Program, was the one to take MIDA from concept to reality and said the vision for the concentration is perfectly suited for students with interests similar to Husong's.
"We are excited about the world of opportunities that this collaboration will bring," Schubert said. "It is the goal of our program to prepare students for all types of careers, including those within the high-tech field of music."
Additionally, the 21-credit hour MIDA concentration allows students to take a variety of courses related to music production, as well as introductory business and data-mining courses that help students to succeed in the modern job market.
The Data Science Program offers a B.S. in data science with a multitude of concentrations through the combined efforts of the UofA'sCollege of Engineering, theFulbright College of Arts and Sciences and theSam M. Walton College of Business. Instructors from across campus likewise collaborate to share their individual expertise through the programs many course offerings.
To learn more about the Data Science Program, visitdatascience.uark.edu.
Read the original here:
Data Science Program and Department of Music Launch Industry ... - University of Arkansas Newswire
LLNLs Data Science Summer Institute hosts student interns from … – Lawrence Livermore National Laboratory (.gov)
Lawrence Livermore National Laboratorys Data Science Summer Institute (DSSI) hosted summer student interns from Japan on-site for the first time, where the students worked with Lab mentors on real-world projects in artificial intelligence (AI)-assisted bio-surveillance and automated 3D printing.
From June to September, the three students Raiki Yoshimura, Shinnosuke Sawano and Taisei Saida lived in rental apartments near the Lab and worked at the Lab on different data science projects using electronic health records and neural networks trained on experimental data.
Sponsored by Japans Agency for Medical Research and Development (AMED) and the Japan Science and Technology Agency (JST), the relationship between the Data Science Institute (DSI) and the Ministry of Japan stems from a series of agreements that arose in the wake of the Fukushima nuclear accident, to conduct academic exchanges and expand scientific collaboration. The DSSI opportunity began in 2019 but had to go all-virtual due to the COVID-19 pandemic. This summer was the first time that students from Japan were brought on-site.
DSI Director Brian Giera said the relationship is part of a continuing trend of Livermore positioning itself to partner with Japan via scientific connections and one the Lab hopes to expand in the coming years.
The Data Science Institute has found itself an example of the U.S.s posture in using science and technology to partner with geopolitically relevant allies, Giera said. Livermore is establishing itself as a global leader in data science, and its very clear that Japan has oriented in the direction of producing candidates that are highly attractive to us. The collaboration is showing that we are helping the students steer their curricular or scientific focus and sharing the messaging to academia [in Japan] that indeed, data science is relevant. Livermore can help be a partner in realizing that vision, and students are the boots on the ground of having that occur.
Yoshimura, Sawano and Saida had never been to the U.S. before arriving in Livermore for their internships. The DSSI program helped the students find housing and matched them with mentors based on the students technical knowledge. Although the students had almost no knowledge of LLNL prior to their internships (outside of photos on the Internet) and were challenged with cultural and language barriers, they were able to acclimate to working in a national laboratory environment and had successful experiences.
While the students said it took some time to get used to the Labs security procedures and American food, they said they found working with their mentors rewarding and eye-opening.
Sawano, a Ph.D. student at the University of Tokyo with a clinical background in cardiology and treating patients with cardiovascular disease, said he thought his internship was a perfect opportunity to combine his knowledge of data science and medicine. His work is supported in Japan by AMED, and though he didnt know what national labs did prior to his internship, he considers LLNL a fascinating option for a career after he finishes his Ph.D.
Working with his lead mentor, LLNL computer scientist Priyadip Ray, and Lab researchers Andre Goncalves and Jose Cadena-Pico, Sawano applied machine learning to electronic health records obtained from Kaiser Permanente, for a bio-surveillance project funded by the Department of Homeland Security. The Lab is developing AI tools to perform faster diagnostics to allow scientists to detect biological threats earlier, thus providing more time to develop possible countermeasures, according to Ray.
As these tools advance, and if we are able to look at the clinical record of everybody in this country in real-time, then we can detect these kinds of anomalies much faster, Ray said. That would give us more time for developing countermeasures.
Sawano said he hopes the research can someday make it into clinical practice.
Collaborating with the professionals on the team is a really good experience for me because Im usually analyzing data by myself and I check my data by myself, but in this team, Priyadip, Jose and Andre can check my coding and my results. As a result, our output is bigger than I do myself, Sawano said. We were really fortunate to get great mentors; they gave me a great deal of advice and support. It will take time to deliver our results to society, but Im excited about the potential output of our research.
Ray, who also worked with Yoshimura on a project using neural networks to evaluate and predict the impact of gene interactions on the viral load of the HIV virus, said the students did a remarkable job in contributing to advancing the research, which could have concrete impacts to public health in the future.
Yoshimura, who attends Nagoya University and studies biology, said he recently began using machine learning to predict clinical outcomes and was drawn to the DSSI internship to expand his skill set and apply his knowledge to large-scale datasets.
This internship experience has been great because they have very big data sets here that I can apply deep learning to, Yoshimura said. In Japan, we have to pay for data and the data is very little so I cant apply a graph or something like that.
Saida, who studies civil engineering in Japan and wants to be a university professor, worked with his mentor LLNL postdoctoral researcher Aldair Gongora on a project on self-driving labs for additive manufacturing, where machine learning approaches are used to help decide which experiments to do next to speed up manufacturing processes with the goal of eventually deploying these approaches on fully automated robotic systems.
There has been a big push toward autonomous experimentation or self-driving labs, and I think that our work really puts us in a position to continue contributing to those fields in a way that really adds value to the modeling and decision-making components of these systems, said Gongora, who works in the Analytics for Advanced Manufacturing group in the Materials Engineering Division. With all the tools that we now have in data science, its blurring the lines between data scientists, chemists, physicists and engineers.
Gongora said he found Saidas background in programming, machine learning and data science a perfect fit for the project and marveled at how his mentee was able to pick up algorithms and implement them at an extremely fast pace.
Saida said he learned how to use and program robots and integrate the hardware with the software on 3D printing machines, and that he found his internship valuable in expanding his knowledge of Bayesian optimization and mechanical engineering.
Its the first time for me researching outside of Japan, so its been a great experience to research with people in the U.S., Saida said. I had a really positive experience working on these projects, especially 3D printing.
During their summer in Livermore, the students explored the Bay Area, enjoying local pizza and ramen restaurants, seeing tourist attractions and even taking a trip to Yosemite National Park. They also attended regular DSSI community events, meet-ups and ice cream socials, where they got to know their fellow intern cohort.
The mentors said they found the experience just as valuable as the students did. Gongora, who came to the Lab as a foreign national himself, said he resonated with the cultural challenges the students faced and said the opportunity epitomized the strength of diversity in science.
The benefit for me has really been being able work with someone from another country and learning more about Japan; learning about their lives, how their academic journey differs from education here in the U.S., and really finding the commonalities and differences, Gongora said. I'm taking away a lot of new perspective leveraging the expertise that [Saida] was able to bring to the project, both in terms of how very skilled he was at the data science concepts and the brainstorming of new ideas from the civil engineering perspective. Time and time again, the Lab teaches me that it's really through the diversity in thought that these interdisciplinary and multidisciplinary ideas emerge, and I think were stronger for it.
Fellow mentor Ray added that the experience was an incredible opportunity for the Lab to get some of the best students from Japan and that he looks forward to mentoring more students from that country.
At the Lab, we are trying to solve very impactful and challenging problems, and we want the best teams to work on these problems, Ray said. There are communication barriers and cultural barriers, because many of them are coming for the first time, but once they're on site, I see them working very hard to overcome all the challenges and really contribute to the mission and push things forward.
Continued here:
I Write About Science and Technology -Part 1: My Main Content on … – Medium
Photo by Scott Graham on Unsplash
Recently, Ive started having job requests on writing about science and technology, one client focused on biology, bioinformatics, and AI to assist these fields; and the other about AI language models and their applications. I thought it would be good to summarize what kinds of contents Ive so far written here at Medium, where the choice of topic and style is all mine -and then I can adapt to your needs. Heres a summary of my topics about data science, AI, numerical analysis and programming.
As a passionate technologist, scientist, writer, and technology integrator as I call myself, my articles on Medium span the fascinating intersection of data science, artificial intelligence, numerical analysis, and programming -oftentimes mixed with chemistry and biology, though not always, and sometimes touching on various technologies such as virtual reality or blockchains, just two mention two.
I try to write each piece as a journey into the heart of these rapidly evolving fields, offering insights into their latest advancements and applications. All making part of the LucianoSphere.
One of my key interests lies in the realm of AI and its transformative impact on various disciplines. Ive written extensively about DeepMinds AlphaFold 2 and its revolutionary role in protein research, as well as the potential of AI in discovering new antibiotics. The philosophical aspects of AI, especially of large language models, such as the Turing Test and the Chinese Room Argument, also feature prominently in my work, offering a deeper exploration of this technologys implications. Ive written about the disruptive potential of huge protein language models in, say, the classroom, and a lot also in biology.
Ive delved into numerical integration, fitting, Monte Carlo simulations, and much more, and their applications in natural sciences, engineering, and economics. My articles often discuss the practical use of these mathematical tools in simplifying equation modeling tasks, such as the Michaelis-Menten equation for enzymatic catalysis.
View post:
I Write About Science and Technology -Part 1: My Main Content on ... - Medium
Why SQL is THE Language to Learn for Data Science – KDnuggets
Python!No, R.Fools, its obviously Rust.
Many data science learners and experts alike are keen to pin down the very best language for data science. In my opinion, most people are wrong. Amidst the hunt for the newest, the sexiest, the most container-able data science language, people are looking for the wrong thing.
Its easy to overlook. Its easy to even discount it as a language. But the humble Structured Query Language, or SQL, is my pick for the language to learn for data science. All those other languages certainly have their place, but SQL is the one non-negotiable language that I consider a base requirement for anyone working in data science. Heres why.
Look, databases come hand in hand with data science. Its in the name. If youre working with data science, youre working with databases. And if youre working with databases, youre probably working with SQL.
Why? Because SQL is the universal database query language. There is no other. Imagine someone told you that if you just learned a specific language, youd be able to speak to and understand every single person on Earth. How valuable would that be? SQL is that language in data science, the language that everyone uses to manage and access databases.
Every data scientist needs to access and retrieve data, to explore data and build hypotheses, to filter, aggregate, and sort data. And hence, every data scientist will need SQL. As long as you know how to write a SQL query, youll go far.
Someone, reading this article right now, is piping up about the NoSQL movement. Indeed, certain data is now more commonly stored in non-relational databases, such as by key-value pairs or graph data. Its true that there are benefits to storing data like that you gain more scalability and flexibility. But theres no standard NoSQL query language. You might learn one for one job, and then need to learn an entirely new one for a new job.
Plus, you will very rarely find a business that works entirely with NoSQL databases, while many companies dont need non-relational databases.
Theres that famous (and debunked) stat about how data scientists spend 80% of their time cleaning. While its not true, I think if you ask any data scientist what they spend time on, data cleaning will rank in the top five tasks. Thats why this section is the longest.
You can clean and process data with other languages, but SQL in particular offers unique advantages for certain aspects of data cleaning and processing.
SQL's expressive query language allows data scientists to efficiently filter, sort, and aggregate data using concise statements. This level of flexibility is especially useful when dealing with large datasets where manual data manipulation would be time-consuming and error-prone. Compare that to a language like Python, where achieving similar data manipulation tasks might require writing more lines of code and dealing with loops, conditions, and external libraries. While Python is renowned for its versatility and rich ecosystem of data science libraries, SQL's focused syntax can expedite routine data cleaning operations, enabling data scientists to swiftly prepare data for analysis.
Plus, any data scientist will complain about the bane of their existence: missing values. SQL's functions and capabilities for handling missing valuessuch as using COALESCE, CASE, and NULL handlingprovide straightforward approaches to address gaps in data without the need for complex programming logic.
The other bane of a data scientists existence is duplicates. Happily, SQL offers efficient methods to identify and eliminate duplicate records from datasets, like the `DISTINCT` keyword and the `GROUP BY` clause.
Youve probably heard of ETL pipelines. Well, SQL can be used to create data transformation pipelines, which take raw or semi-processed data and convert it into a format suitable for analysis. This is particularly beneficial for automating and standardizing that repetitive data-cleaning processes we all know and hate.
SQL's ability to join tables from different databases or files streamlines the process of merging data for analysis is essential for projects involving data integration or aggregating data from diverse origins. Which, for a data scientist, comprises a majority of projects.
Finally, I like to remind people that data science does not happen in a vacuum. SQL queries are self-contained and can be easily shared with colleagues. This fosters collaboration and ensures that others can reproduce data cleaning steps without manual intervention.
Now, you wont get far in data science if you only know SQL. But happily, SQL integrates perfectly well with any other of the top data science languages like R, Python, Julia, or Rust. You get all the benefits of analysis, data viz, and machine learning while still retaining SQLs strength for data manipulation.
This is especially powerful when you think about all that data cleaning and processing I talked about earlier. You can use SQL to preprocess and clean data directly within databases, and then lean on Python, R, Julia, or Rust to perform more advanced data transformations or feature engineering, leveraging the extensive libraries available.
Many organizations rely on SQL or, more accurately, rely on data scientists who know how to use SQL to generate reports, dashboards, and visualizations that inform decision-making. Familiarity with SQL enables data scientists to produce meaningful reports directly from databases. And because SQL is so widespread, these reports are usually compatible and interoperable across almost any system.
Because of how interoperable it is with reporting tools and scripting languages like Python, R, and JavaScript, data scientists can actually automate the reporting processes, seamlessly combining SQL's data extraction and manipulation capabilities with the visualization and reporting features of these languages. The upshot is you get comprehensive and insightful reports that effectively communicate data-driven insights to stakeholders, all inside one place.
Theres a reason youll get asked a bunch of SQL interview questions at any data science interview. Almost every data science job requires at least a basic familiarity with SQL.
Heres an example of what I mean: the job listing says, Expertise in SQL, and R or Python for data analysis and platform development. In other words, SQL is a must. And then either R or Python, but one is as good as another to most employers. But thanks to SQL domination, theres no alternative to SQL. Every data science job will require you to work with SQL.
The really cool thing about it is that it makes SQL the ultimate transferable tool. One job may prefer Python, while a startup might require Rust due to personal preference or legacy infrastructure. But no matter where you go, or what you do, its SQL or bust. Take the time to learn it, and youll always be able to tick off a job requirement.
Ultimately, if you find a job as a data scientist that doesnt require SQL, youre probably not going to be doing a whole lot of data science.
It really comes down to the database. Data science requires the storage, manipulation, retrieval, and management of a lot of data. That data lives somewhere. It can only be accessed with one tool, normally, and that tool is SQL. SQL is the language to learn for data science and will be for as long as we rely on databases to do data science.
Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.
Go here to see the original:
Why SQL is THE Language to Learn for Data Science - KDnuggets
Rady School Leverages AI to Prepare Next Generation of Business … – University of California San Diego
Why has GenAI been integrated into the MSBA programs at Rady?
Nijs: GenAI is already transforming the ways people learn and work in many ways. Rather than fight the coming changes, we aim to teach our students skills that will complement what these tools already bring to the table.
We will be able to improve students math, programming and machine-learning skills in less time. As a result, our students will be able to develop a deeper understanding of the business problems they will solve using GenAI and machine learning during their capstone projects (in which they help an organization solve a real and pressing business challenge) and after graduation. Our part-time MSBA students, who are working professionals, will be able to have an immediate impact and bring value to their organizations as they learn to integrate GenAI into existing business processes or design new ones where different tasks can be maximally supported by GenAI.
In addition, GenAI will reduce the technical barriers to be successful in the program. For example, translating ideas into Python code with these tools will be much more efficient. Every incoming MSBA student along with program instructors and teaching assistants, will have access to GenAI to help in task completion. They basically have a personal AI tutor and can spend more time on valuable tasks like data exploration, applying advanced analytics and creating a tangible impact on the business problems they are trying to solve. Developing talent that has this experience, along with strong business acumen, will create business data scientists that are perfectly equipped to solve the business problems of the future and find innovative ways to help businesses grow.
Nijs: The Bureau of Labor Statistics projects data science job growth of 36% by 2031, which is much faster than the average for other roles. This is because businesses have access to huge amounts of data about their customers, supply chains, markets, devices, services, etc. So much so that developing creative ways to access, analyze and find patterns is particularly challenging but also necessary to stay competitive. Not only do business data scientists have to find meaningful insights, but they have to do it fast and at scale. GenAI can help business data scientists achieve these goals.
Business data scientists are integral to helping organizations make informed decisions, improve their business processes, design and develop new products, effectively market their products and more.
Nijs: We see GenAI as both a skill multiplier and a skill extender. That means students offered GenAI-enhanced learning will be stronger in terms of both their depth and breadth of understanding and abilities. By taking this big step forward we will have to work on new and better ways to continue challenging our students. GenAI-assisted learning will allow us to increase class expectations with respect to both the quantity and quality of student work. We are also creating customized GenAI policies for each MSBA class to ensure students are able to leverage these tools appropriately to maximize learning.
Students will also learn to use GenAI responsibly. Checking the work and using GenAI as a partner, but making sure that it's not hallucinating and that the work they submit is valid, credible and returns appropriate results. No matter if GenAI was or wasnt used for a particular task, we require that students can understand and explain every detail of their work. Students need to learn how to partner effectively with GenAI which takes practice.
Nemteanu: These technologies are still so new that the first major experimental studies are just starting to come out. They clearly show the massive impact these tools can have on individual and company performance.
For example, a paper published in Science demonstrated that ChatGPT was able to reduce the time required for a business writing task by 40% while at the same time increasing the quality of the work. After using AI, participants were also significantly happier. Why? Likely because it helped them complete tedious work at a much faster pace.
Our full-time students are going to have the real-world experience of using these technologies, building analytic solutions better and faster, something that will resonate with hiring managers. Students in our part-time MSBA program, who are working professionals, are going to be able to take what they learn and immediately apply it in their organization and have a lasting impact on near-term and long-term strategies and business goals.
Nemteanu: We believe that GenAI-assisted business analytics can add an extremely valuable layer of skills for many UC San Diego graduates. Studying data science for business (aka Business Analytics) is important because business is a science--whether its finance, marketing, supply chain--they require data and analysis to ensure every decision is data driven and credible.
If you have a technical undergraduate degree, we can bolster your job market outcomes (i.e., job title and salary) by helping you translate and enhance your skillset and set you on a GenAI-assisted path that solves business problems using data science tools. If you have strong domain expertise in a substantive area, e.g., biology, we can augment your abilities with a high-powered data and GenAI literate skillset. In short, a masters degree in Business Analytics can supplement any background and contribute to a very exciting future with amplified career opportunities.
Original post:
Data Science Platform Market to grow by USD 249.15 billion … – PR Newswire
NEW YORK, Oct. 13, 2023 /PRNewswire/ -- The Data Science Platform Marketreport has been added to Technavio's offering.With ISO 9001:2015 certification, Technavio has proudly partnered with more than 100 Fortune 500 companies for over 16 years. The potential growth difference for the data science platform market between 2022and 2027 is USD 249.145 billion.Get deeper insights into the market size, current market scenario, future growth opportunities, major growth driving factors, the latest trends, and much more. Buy the full report here
The majordriving factor forthe global data science platform market is thehigh generation of data volumes.Since 2014,data volumes have exploded, and more data has been created. Business applications are generating enormous volumes of data, and this will continue throughout the forecast period and beyond. Further, the growing volume of data generated in organizations through multiple channels and sources has forced organizations to implement big data analytics and save a significant amount of cost for organizations. However,data analysts or scientists need to thoroughly analyze large amounts of data and convert insights into real-time action.For instance, apopular data science application such asbig data analyticscan be used for retrieving and analyzing data to discover significant weaknesses, develop indicator patterns to identify opportunities and threats; and optimize business decisions. Therefore, as data volumes are rising, the demand for data analytics is also growing, which is anticipated to boost the growth of the market during the forecast period.
The data science platform market is segmented by Component (Platform and Services), Deployment (On-premise and Cloud), and Geography (North America, Europe, APAC, South America, and Middle East and Africa).
Key Companies in the Data Science Platform Market:
Alphabet Inc., Altair Engineering Inc., Alteryx Inc., Anaconda Inc., Cloudera Inc., Databricks Inc., Dataiku Inc., DataRobot Inc., Domino Data Lab Inc., International Business Machines Corp., Microsoft Corp., Oracle Corp., Rapid Insight Inc., RapidMiner Inc., Rexer Analytics, Rstudio PBC, SAS Institute Inc., The MathWorks Inc., Vista Equity Partners Management LLC, Wolfram
Related Reports:
The online data science training programs market share is expected to increase by USD3.76 million from 2021 to 2026,and the market's growth momentum will accelerate at a CAGR of 25.1%.
TheIndia -professional online courses marketsize is estimated to grow at aCAGR of 16.24%between 2022and 2027. Themarket size is forecast to increase byUSD 2,782.59 million.
ToC:
Executive Summary
Market Landscape
Market Sizing
Historic Market Sizes
Five Forces Analysis
Market Segmentation by Component
Market Segmentation by Deployment
Market Segmentation by Geography
Customer Landscape
Geographic Landscape
Drivers,Challenges, &Trends
Company Landscape
Company Analysis
Appendix
About Technavio
Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provideactionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.
With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.
Contacts
Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email:[emailprotected]Website:www.technavio.com
SOURCE Technavio
See the original post:
Data Science Platform Market to grow by USD 249.15 billion ... - PR Newswire
NOT-HL-23-118: Request for Information (RFI): National Heart Lung … – National Institutes of Health (.gov)
Request for Information (RFI): National Heart Lung and Blood Institute (NHLBI) Strategic Vision Refresh
The NHLBI provides global leadership for research and training to promote the prevention, diagnosis, and treatment of heart, lung, and blood diseases and sleep disorders and to enhance the health of all individuals so that they can live longer and more fulfilling lives. The NHLBI stimulates basic discoveries about the causes of disease, enables the translation of basic discoveries into clinical practice, leverages implementation science to take innovations toward application, and communicates research advances to the public.
By design, the NHLBI Strategic Vision is dynamic, reflecting input from partners who are at the leading edge of scientific exploration and from communities with a vested interest. As part of updating the Strategic Vision, the NHLBI wants to harness community-level participation. While the NHLBI Strategic Vision goals and objectives set in 2016 remain timely, the refresh aims to ensure such by keeping up with scientific needs and advances.
The NHLBIs Strategic Vision has four goals and eight objectives that were identified in 2016.
Strategic goals:
Strategic objectives:
For the refresh of the NHLBI Strategic Vision, this RFI invites input on the current relevance of the NHLBI strategic objectives and considering whether additional Compelling Questions (CQs) and Critical Challenges (CCs) are needed to address topics that have surfaced as priorities over the past five years to drive important scientific and health advances.
Compelling Questions are unanswered questions or poorly understood areas of research requiring NHLBI facilitation because their complexity exceeds the capacity of any single investigator-initiated program.
Critical Challenges are barriers or impediments to scientific progress, and overcoming these obstacles will result in significant impact.
The NHLBI Strategic Vision is inclusive of a broad portfolio of scientific ideas spanning basic through implementation sciences. This refresh will adhere to our commitment to scientific stewardship and accountability. This RFI has a particular interest in seeking input on novel research needs and approaches in the following focus areas:
Input sought includes the following:
Perspective on critical research needs or compelling research questions for any of the refresh focus areas
Perspective on challenges or barriers that need to be addressed to support progress in any of the refresh focus areas
The relevance of the 8 objectives for the NHLBI Strategic Vision and any critical questions or challenges that are not already incorporated into the NHLBI Strategic Vision
Comments must be submitted electronically on the submission website: https://rfi.grants.nih.gov/?s=65119386df04aef8db066042. Responses will be accepted through 11:59:59 pm (ET) on December 15, 2023.
Responses to this RFI are voluntary. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information shared or provide feedback to respondents with respect to any information submitted. No proprietary, classified, confidential, or sensitive information should be included in your response. This RFI is for information and planning purposes only and should not be construed as a solicitation or as an obligation on the part of the Federal Government in general, the NIH, or the NHLBI specifically.
Other than your name and contact information, the Government reserves the right to use any submitted information on public websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future funding opportunity announcements. Please note that the Government will not pay for the preparation of any information submitted or for use of that information.
We appreciate your input and invite you to share this RFI opportunity with your colleagues and others in your community.
See the rest here:
Robinson Receives New Mathematics Teacher of the Year Award – University of Arkansas Newswire
Bella Rose Robinson
Vice Chair and teaching associate professor Samantha Robinson
Samantha Robinson, vice chair and teaching associate professor in theDepartment of Mathematical Sciencesin theFulbright College of Arts and Sciences, will receive the 2023Arkansas Council of Teachers of MathematicsExcellence in Four-Year College/University Mathematics Teaching Awardfor making significant contributions to mathematics, statistics and data science education in the state.
The mission of the Arkansas Council of Teachers of Mathematics is to provide vision and leadership in improving the teaching and learning of mathematics so that every student is ensured of an equitable standards-based mathematics education, and every teacher of mathematics is ensured the opportunity to grow professionally.
This inaugural ACTM teaching award is one of six newly introduced Mathematics Teacher of the Year awards, each representing a different level of mathematics teaching e.g., Elementary School, Middle School, Secondary School, etc.
Robinson currently serves as vice chair in the Department of Mathematical Sciencesbut previously served as course coordinator for all sections of Principles of Statistics (STAT 2303) and Biostatistics (STAT 2823), which have an annual student enrollment of approximately 1,500-1,700 students. She drafted the Arkansas Course Transfer System learning objectives for Principles of Statistics, directly impacting all two-year and four-year institutions in the state of Arkansas, helped to align K-12 and college-level statistics and data science education objectives while serving on the Arkansas Mathematics Pathways Task Force Alignment Working Group and recently was elected to the American Statistical Association Section of Statistics and Data Science Education executive committee.
In recent years,Robinson has led groups of student researchersto conferences, presenting statistical research that has resulted in publications, oral presentations, poster presentations and numerous awards for the student researchers. Robinson has also been previously recognized for her distinguished university teaching and her contribution to mathematics, statistics and data science education at theuniversity,regionalandnationallevels.
Robinson has been teaching in the Department of Mathematical Sciences in a variety of different roles for approximately 10 years, with her first full-time teaching appointment in 2013.
Robinson will be the very first recipient of this newly established award at the Four-Year College/University level. She (along with other ACTM awardees) will be honored at the annual ACTM conference this fall in Little Rock.
Original post:
Robinson Receives New Mathematics Teacher of the Year Award - University of Arkansas Newswire
worldsteel Safety and Health Excellence Recognition 2023 … – World Steel Association
As part of its commitment to the highest safety and health standards, the World Steel Association (worldsteel) recognises excellence in six of its member companies for delivering demonstrable improvements in safety and health.
Andrew Purvis, Director, Sustainable Manufacturing, said, I am proud to recognise the commitment and effort of our members towards the wellbeing of their workforce and contractor community. The stories shared here are more than just examples; they highlight the remarkable progress made in safeguarding lives and promoting and preserving health.
The recognised companies this year are:
BlueScope Steel Limited Integrating HOP into foundational HSE processes
In 2019, BlueScope started its global HOP (Human and Organisational Performance) journey by proactively piloting HOP-based Leadership Workshops as top management was curious about evolved safety thinking. BlueScope is at the stage of systems and processes being simplified and updated to embed the HOP philosophy into everything it does, so that the practice is sustained.
Liberty Steel Transforming safety culture and performance through human performance principles
In 2019, a decision was made to initiate a transformative journey to reshape the safety culture across the organisation. To drive this transformation, a comprehensive roadmap was developed, known as The WRIB [We are InfraBuild] Safe Way. Underpinning this, are four strategic pillars, with the overarching goal of creating a world-class safety culture and safety performance.
ACERINOX S.A. Innovative roll cover solution enhances safety and operational efficiency in hot mill operations
In February 2022, a critical challenge arose at Columbus Stainless hot strip mill when a finishing mill backup roll (BUR) suffered a catastrophic failure, posing a risk to personnel and equipment. To address this, a cross-functional team embarked on a mission to create a preventive solution that prioritised safety without compromising operational efficiency.
JFE Steel Corporation Safe work support using safety monitoring system
At JFE Steel, the latest information and communications technology (ICT), artificial intelligence (AI) and data science technologies are being used to develop and commercialise more new technologies to ensure the safety of workers at manufacturing sites.
Tenaris Ergonomics programme
Confab, Tenaris production centre in Brazil, started evaluating the ergonomic conditions in its pipe manufacturing mills back in 2016. Before implementing its ergonomics programme, the production centre reported an average of 42 employees per year with work restrictions due to injuries associated with poor ergonomics. Following this assessment, a three-year ergonomics programme was introduced, including an annual review and evaluations by a cross-functional team to establish investment priorities.
Tata Steel Real-time visualisation of risk movement
All high-potential safety risk scenarios were identified at Tata Steel by implementing a Process Safety Management framework. To prevent and mitigate high-potential scenarios, a number of safety barriers were identified. However, in some instances, there was a fair probability of some early failure indications going unnoticed, which could cause the failure of barriers, leading to high potential incidents. Consequently, Tata Steel felt that tracking the health of the barriers on a real-time basis was needed. The companys innovative approach to real-time visualisation of risk movement aims to provide real-time insights and alerts on the level of risk.
More details on each of the initiatives can be found in worldsteels Safety and Excellence Recognition 2023 publication (click on the link on the right of this page).
#Ends#
Notes
The World Steel Association (worldsteel) is one of the largest and most dynamic industry associations in the world, with members in every major steel-producing country. worldsteel represents steel producers, national and regional steel industry associations, and steel research institutes. Members represent around 85% of global steel production.
See the article here:
worldsteel Safety and Health Excellence Recognition 2023 ... - World Steel Association