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Top 10 Predictive Analytics Software and Tools in 2023 – Analytics Insight

August 1, 2023 | Author: admin

Here are the top 10 tools and software for predictive analytics in 2023

1. Through its Knowledge Works portfolio, Altair (previously Datawatch) provides several products, with the Knowledge Studio sophisticated data mining and predictive analytics workbench taking center stage. The software has workflow, wizard-driven graphical user interfaces, and patented Decision Trees and Strategy Trees. It also can do in-database analytics, visual data profiling, sophisticated predictive modeling, and data preparation activities. Commonly used languages like R and Python and data formats including SAS, RDBMS, CSV, Excel, and SPSS may be used by users for import and export.

2. Alteryx provides data science and machine learning capability through various software solutions. The self-service platform has more than 260 drag-and-drop building components, with Alteryx Designer as its standout feature. Alteryx Designer automates data preparation, blending, reporting, predictive analytics, and data science. Users of Alteryx may easily choose and compare the performance of various algorithms and immediately see variable connections and distributions. The program may be set up in a hosted environment, in the cloud, behind your firewall, or without coding knowledge.

3. Anaconda provides a variety of product versions via which it may access its data science and machine learning capabilities. The companys primary offering is Anaconda Enterprise, an open-source platform emphasizing Python and R. You may use the Linux, Windows, and Mac OS programs to undertake data science and machine learning. Users can manage libraries, dependencies, and environments with Anaconda, download over 1,500 Python and R data science packages, and utilize Dask, NumPy, pandas, and Numba to analyze data. The Anaconda findings may be seen using Matplotlib, Bokeh, Datashader, and Holoviews.

4. A cloud-based, Apache Spark-based unified analytics platform from Databricks combines the features of data engineering with data science. For operationalization, performance, and real-time enablement on Amazon Web Services, the solution uses a variety of open-source languages and incorporates proprietary functionality. Users may collaborate to analyze data and develop models in a data science workspace. Additionally, it offers simple one-click access to predefined ML environments for machine learning supplemented with well-known frameworks.

5. With the help of Dataikus sophisticated analytics technology, businesses may build custom data tools. The companys flagship solution has a collaborative user interface for data scientists and analysts. The unified architecture for development and deployment offered by Dataiku gives users rapid access to all the functionality required to create custom data tools. Then, users may create and implement predicted data flows using machine learning and data science methodologies.

6. DataRobot is an enterprise AI platform streamlining AI creation, deployment, and upkeep. The solution may be used on-prem, in the cloud, or as a fully-managed AI service and is driven by open-source algorithms. Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps, and AI applications are just a few of the separate but fully connected technologies that make up DataRobot. These tools may be used to meet business goals and IT requirements.

7. Data scientists may create and use prediction models using the enterprise data science platform provided by Domino Data Lab. The solution utilizes infrastructure automation and cooperation to assist enterprises with creating and delivering these models. Users of Domino have access to a data science Workbench that offers both free and paid tools for batch experiments, as well as Model Delivery, which enables users to schedule reports or deploy APIs and web apps.

8. Many AI and data science solutions are available from H2O.ai, but its flagship product is the commercial platform H2O Driverless AI. A distributed in-memory machine learning platform with linear scalability, Driverless AI is completely open-source. H2O supports a variety of commonly used statistics and machine learning techniques, such as deep learning, generalized linear models, and gradient-boosted machines. Additionally, H2O has created AutoML capability, which automatically executes all algorithms and creates a scoreboard of the top models.

9. An open-source platform for data science development is KNIME Analytics. It offers a graphical drag-and-drop interface that enables the construction of visual processes without scripting. To design workflows, model each phase of analysis, regulate data flow, and guarantee work is current, users can select from more than 2000 nodes. To generate statistics, clean data, and extract and choose features, KNIME may combine data from any source. The software uses AI and machine learning to show data using conventional and cutting-edge charts.

10. A data science platform from RapidMiner enables business users of all skill levels to create and manage AI solutions. From data exploration and preparation through model construction, model deployment, and model operations, the package covers the whole lifetime of the AI production process. RapidMiners visual user interface makes it easier for everyone to develop and comprehend complicated models while still giving data scientists the required depth.

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Gartner Identifies Top Trends Shaping the Future of Data Science … – Gartner

August 1, 2023 | Author: admin

Gartner, Inc. today highlighted the top trends impacting the future of data science and machine learning (DSML) as the industry rapidly grows and evolves to meet the increasing significance of data in artificial intelligence (AI), particularly as the focus shifts towards generative AI investments.

Speaking at the Gartner Data & Analytics Summit in Sydney today, Peter Krensky, Director Analyst at Gartner said: As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline. This is now also fueled by the fervor around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organizations.

According to Gartner, the top trends shaping the future of DSML include:

Trend 1: Cloud Data EcosystemsData ecosystems are moving from self-contained software or blended deployments to full cloud-native solutions. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.

Gartner recommends organizations evaluate data ecosystems based on their ability to resolve distributed data challenges, as well as to access and integrate with data sources outside of their immediate environment.

Trend 2: Edge AIDemand for Edge AI is growing to enable the processing of data at the point of creation at the edge, helping organizations to gain real-time insights, detect new patterns and meet stringent data privacy requirements. Edge AI also helps organizations improve the development, orchestration, integration and deployment of AI.

Gartner predicts that more than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021. Organizations should identify the applications, AI training and inferencing required to move to edge environments near IoT endpoints.

Trend 3: Responsible AIResponsible AI makes AI a positive force, rather than a threat to society and to itself. It covers many aspects of making the right business and ethical choices when adopting AI that organizations often address independently, such as business and societal value, risk, trust, transparency and accountability. Gartner predicts the concentration of pretrained AI models among 1% of AI vendors by 2025 will make responsible AI a societal concern.

Gartner recommends organizations adopt a risk-proportional approach to deliver AI value and take caution when applying solutions and models. Seek assurances from vendors to ensure they are managing their risk and compliance obligations, protecting organizations from potential financial loss, legal action and reputational damage.

Trend 4: Data-Centric AIData-centric AI represents a shift from a model and code-centric approach to being more data focused to build better AI systems. Solutions such as AI-specific data management, synthetic data and data labeling technologies, aim to solve many data challenges, including accessibility, volume, privacy, security, complexity and scope.

The use of generative AI to create synthetic data is one area that is rapidly growing, relieving the burden of obtaining real-world data so machine learning models can be trained effectively. By 2024, Gartner predicts 60% of data for AI will be synthetic to simulate reality, future scenarios and derisk AI, up from 1% in 2021.

Trend 5: Accelerated AI InvestmentInvestment in AI will continue to accelerate by organizations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses. By the end of 2026, Gartner predicts that more than $10 billion will have been invested in AI startups that rely on foundation models large AI models trained on huge amounts of data.

A recent Gartner poll of more than 2,500 executive leaders found that 45% reported that recent hype around ChatGPT prompted them to increase AI investments. Seventy percent said their organization is in investigation and exploration mode with generative AI, while 19% are in pilot or production mode.

Gartner Data & Analytics SummitGartner analysts are presenting the latest research and advice for data and analytics leaders at the Gartner Data & Analytics Summit in Sydney, July 31-August 1. Follow news and updates from the conferences on Twitter using #GartnerDA.

About Gartner for Data & Analytics LeadersGartner for Data & Analytics Leaders provides actionable, objective insight to CDAOs and data & analytics leaders to help them accelerate their D&A strategy and operating model to increase business value. Additional information is available at https://www.gartner.com/en/data-analytics.

Follow news and updates from Gartner for D&A Leaders on Twitter and LinkedIn using #GartnerDA.Visit the Gartner Newsroom for more information and insights.

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What Is A Master’s In Data Science? Everything You Should Know – Forbes

August 1, 2023 | Author: admin

Editorial Note: We earn a commission from partner links on Forbes Advisor. Commissions do not affect our editors' opinions or evaluations.

Advances in technology have resulted in what seems like an endless amount of data. From personal wearable devices and laptops to large-scale manufacturing projects and government programs, our world relies on computer systems and technologies to manage and secure data.

Data science professionals work with large amounts of data, develop strategies to improve these systems and help businesses and organizations run more effectively and efficiently. If youre a computer technology or data science professional and youre ready for more job opportunities, higher earning potential, and a chance to use your analytical and leadership skills, consider earning a masters in data science.

If you ultimately plan to earn a doctoral degree, a masters in data science can push you toward that goal as well.

This article gives a detailed explanation of masters degrees in data science, including admission requirements, common courses, specializations and career options. Keep reading for everything you should know about a masters in data science.

A masters in data science provides you with advanced knowledge and skills essential for many data science careers. This degree takes you from gathering and analyzing raw data to using predictive modeling methods, machine learning, data mining, artificial intelligence and other technologies for extracting valuable insights from data. It also strengthens your leadership and communication skills.

A data science masters degree typically requires around 30 credits of coursework and takes two years to complete, though part-time students might need longer. Accelerated programs can take as little as a year to finish.

Masters in data science programs typically require a capstone or thesis project to graduate, allowing learners to demonstrate mastery of their data science knowledge under faculty guidance.

Admission requirements for data science masters degrees vary by program; below we list a few common standards for admission:

Data science masters programs typically allow you to choose an area of specialization, allowing you to focus your studies on an area that interests you. Specialization offerings differ among programs but may include the following:

Students in this specialization focus on applied statistics, using algorithms to develop marketing models, predictive modeling and analytics, and other applications. Learners understand how to test real-world projections based on data.

This specialization covers the foundations of data engineering and analytics application engineering, teaching students to use problem-solving skills, build software systems and choose hardware systems with the ultimate goal of scaling data analysts models in production systems.

An analytics management specialization covers communication strategies, management principles, and the use of statistical data and analyses to optimize business performance. The curriculum explores accounting and finance for technology managers, project management, business leadership and communications, ethics, legal issues and data governance.

This specialization teaches students how to pivot from traditional applied statistics models to data-adaptive models for machine learning, natural language processing, software robotics, artificial intelligence and deep learning.

Students specializing in technology entrepreneurship learn about innovative advances in technology and science, data science, artificial intelligence and machine learning, including how these advances provide opportunities for entrepreneurship. This specialization covers technology entrepreneurship and accounting and finance for technology managers.

A big data informatics specialization focuses on the use of cutting-edge tools and technologies to address big data issues in analytics, data processing and applications. It covers natural language processing, web information management, advanced data mining, interactive media, text mining, and information systems analysis and design.

Course titles and offerings are unique to each data science program, but you can expect to take a few standard courses as a data science masters student. Below we explore some common data science masters courses.

This course focuses on data organization methods, streaming algorithms for computing statistics, dynamic programming, numerical algorithm stability, gradient and stochastic descent, large-scale applications, collaborative filtering and basic graph models for searching.

A Python for data science course covers the fundamentals of the Python programming language in computer science. The curriculum includes data structure implementation, data analysis solutions, programming paradigms, data stream processing, object-oriented programming and an overview of the Python library and its packages.

This course introduces machine learning on the graduate level, focusing on the statistical concepts used in supervised machine learning, popular algorithmic paradigms, and representation and online learning.

Students in a statistical inference and modeling course learn the basics of statistical inference and testing. This course covers hypothesis testing, maximum likelihood estimates, generalized linear regression models, statistical computing and nonparametric regression.

In this course, you can expect to learn how to use reinforcement learning as a strategy for working with intelligent systems. You might study Markov decision processes, temporal difference learning, eligibility traces, dynamic programming, implementation of intelligent agents and function approximation.

No degree can guarantee any particular career outcome, but if you have a masters in data science, several roles should be within reach. Below are some well-paying careers you may qualify for with a masters in data science. We sourced salary data from the U.S. Bureau of Labor Statistics and Payscale.

Median Annual Salary: $103,500Minimum Required Education: Bachelors degree, masters or doctoral degree sometimes preferredJob Overview: Data scientists use various technologies and tools to identify, gather, categorize, analyze and extract insights and information from data. They leverage web-scraping tools to turn raw data into usable data. Data scientists also develop algorithms, classify data with machine learning and use data visualization software.

Median Annual Salary: $95,290Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Management analysts gather and analyze an organizations data to identify problems and recommend improvements. They typically work with financial data, such as revenue and expenditures; they also gather data from observing and interviewing personnel. Management analysts may specialize in a specific area or industry, such as inventory control, corporate reorganization or government agencies.

Median Annual Salary: $99,890Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Database administrators manage, implement and test databases. They identify performance issuessuch as those related to scalability, capacity and other potential problemsand find solutions. They may also plan and execute security measures to protect databases.

Median Annual Salary: $134,870Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Database architects develop strategies and procedures for data warehouse systems, enterprise databases and multidimensional networks. They design relational databases, establish database operations standards, develop data models for warehouse infrastructure and ensure systems are functional.

Average Annual Salary: Around $95,200Minimum Required Education: Bachelors degreeJob Overview: Data engineers create and translate algorithms into prototype code. They may also analyze and identify trends in data sets, create process documentation, develop tools and dashboards, recommend improvements for data usage and access, and provide advice on technological resources and tools.

If your desired career involves interpreting and analyzing data and using your findings to recommend solutions for businesses and organizations, then yes, earning a masters in data science is worth it. This degree opens up a large selection of career opportunities in the data science field.

A degrees difficulty level is subjective, but you can expect a masters in data science to challenge you. If youre interested in the data science field and enjoy using technologies, tools and other resources to work with data, you may be primed for success in this degree program. If youre not an analytical thinker, you might find it more difficult.

Program length varies by degree. Many data science masters programs take one and a half to two years to complete, but some accelerated online programs ready you for graduation in as little as one year. On the other hand, if you study part time, you may need longer to earn your degree.

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Data Science and Predictive Analytics Industry 2023-2031 Trends – Fagen wasanni

August 1, 2023 | Author: admin

The 2023 Global Data Science and Predictive Analytics Market research report provides key industry insights and data, as well as current and predicted market trends from 2020 to 2025. It covers pricing, geographical region, technology, and demand-supply evaluations.

The report begins by defining, categorizing, and characterizing the market, analyzing product specifications, company initiatives, and policies. It also includes cost structure examples, manufacturing methods, and other significant elements.

The study examines how these factors impact the market in different regions, helping organizations make informed investment decisions. It starts with a comprehensive market overview before delving into industry insights.

Key players in the Data Science and Predictive Analytics market include Salesforce, Teradata Corporation, SAS Institute, SAP, Oracle, BioSymetrics, Cyclica, IBM Corporation, and Microsoft Corporation.

The analysis provides an in-depth look at the industrys size, growth potential, competitive environment, and trends. It includes a SWOT analysis to identify strengths, weaknesses, opportunities, and threats. Revenue breakdown by location and product is also presented, helping businesses identify profitable markets and products.

The report profiles the top international market participants, offering a realistic projection of their trading performance over the next five years. Investors can gain insights into a specific firms market dynamics by investigating each sector.

The global Data Science and Predictive Analytics market is effectively segmented by top producers, geographic regions, product types, and applications. It conducts extensive analyses and forecasts for each category, as well as market size data. The research also investigates the market positioning of major competitors.

The study examines existing trends, new projections, and industry dynamics, with a focus on product positioning and market leaders. It includes financial assessments, successful market approaches, inventive discoveries, and items provided by top competitors.

The research looks at innovations that enhance market competitiveness, saving time, making better decisions, and improving productivity. It involves both secondary and primary research methodologies, utilizing massive volumes of data and primary data collection through questionnaires, interviews, and expert comments.

The study provides a concise assessment of market size, cost estimates, and forecasts from 2021 to 2030. It also offers insights into the competitive landscape of the sector, analyzing the contributions of key market players and their growth potential.

About Us:Orbis Research is a trusted source for market research, offering a vast database of reports from leading publishers and authors worldwide. We specialize in delivering customized reports tailored to our clients requirements, ensuring accuracy and relevance. Contact us for your market research needs.

Note: This is a rewritten version of the original article without author information, contact information, sources of information, and quotes.

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Podcast: All Things Data with Guest Dean Alderucci – Newsroom … – University of St. Thomas Newsroom

August 1, 2023 | Author: admin

In the ever-evolving technology landscape, data analytics and data strategy continues to play a larger role in economics and business models. Director of the Center for Applied Artificial Intelligence at the University of St. Thomas, Dr. Manjeet Rege, co-hosts the All Things Data podcast with adjunct professor and Innovation Fellow Dan Yarmoluk. The podcast provides insight into the significance of data science as it relates to business models, business economics, and delivery systems. Through informative conversation with leading data scientists, business model experts, technologists, and futurists, Rege and Yarmoluk discuss how to utilize, harness, and deploy data science, data-driven strategies, and enable digital transformations.

Rege and guest co-host Tom Marlow (chief technology officer of Black Hills IP) spoke with Dean Alderucci about patent analytics and the insight generated regarding innovation and momentum of advanced technologies. Alderucci is the director of research for the Center of AI and Patent Analysis at Carnegie Mellon University. He has a background in academia, having taught graduate courses in innovation strategy, natural language processing, and IP across several leading universities. In addition, Alderucci is a patent attorney and former COO and chief IP counsel for a global financial services company, so hes got a very interesting and diverse background. Here are some highlights from their conversation.

Q. Can you talk about some use cases in the IP world where AI can be utilized?

A. There are several common and well-developed use cases. Starting with the patent field, theres a lot of good work in classifying patents, and specifically the technology that the patent deals with. For example, if youre one of the worlds patent offices and you receive hundreds of thousands of patent applications a year, you want to sort them by technology so you can deliver the right kinds of patents to the right people, i.e., there are internal specialists for computer science, biotech, etc., and they will read a subset of the patents that relate to their field of expertise. Outside of the IP offices of the world, its also very important to classify patents by technology if you own a large patent portfolio and want to keep abreast of developments in the field, keep abreast of developments by your competitors, etc. So, this ability of AI to classify patent documents into a technical category is a very well trafficked use case.

Q. What are your thoughts on clustering type analyses and how well ML can do with patent documents just by being given a companys portfolio or maybe two companies portfolios?

A. In the patent field its certainly one of the most common use cases. You might hear it called patent clustering, patent segmentation, or patent landscaping, but essentially, youre putting together a figure or graph of what the set of patents might look like, e.g., clustered by the type of technology. Clustering is great, but it begs the question, What way are you clustering? Specifically, when you cluster, you (the human) are deciding which inputs to give the computer for each of these things you want to be clustered, and how do you tell it to measure similarity between these things?

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Practical Applications of NetworkX in Data Science | by Harshita … – Medium

August 1, 2023 | Author: admin

In the realm of data analysis and network science, NetworkX stands as a powerful Python library that provides a flexible framework for studying and analyzing complex networks. With its extensive range of functionalities, NetworkX has become a go-to tool for researchers, data scientists, and developers alike. In this blog, we will explore some practical applications of NetworkX and delve into real-world scenarios where it can be employed to gain valuable insights. Well also include code snippets to demonstrate how to leverage the capabilities of NetworkX effectively.

Social network analysis (SNA) has gained immense popularity across various domains, including sociology, marketing, and data-driven decision-making. NetworkX provides a comprehensive set of tools to analyze social networks and uncover hidden patterns. Lets consider an example where we want to identify key influencers in a social network.

# Create a social network graphsocial_graph = nx.Graph()

# Add nodes (users)social_graph.add_nodes_from(['Alice', 'Bob', 'Charlie', 'David', 'Eve'])

# Add edges (friendships)social_graph.add_edges_from([('Alice', 'Bob'), ('Alice', 'Charlie'), ('Bob', 'David'), ('Charlie', 'David'), ('Eve', 'David')])

# Calculate degree centralitydegree_centrality = nx.degree_centrality(social_graph)

# Print the most influential usersmost_influential_users = sorted(degree_centrality, key=degree_centrality.get, reverse=True)[:2]print("Most influential users:", most_influential_users)

NetworkX provides powerful tools for analyzing transportation networks, such as road networks, flight routes, or public transportation systems. We can leverage NetworkX to calculate optimal routes, identify critical nodes, and assess network robustness. Lets consider a scenario where we analyze a road network to find the shortest path between two locations.

# Create a road network graphroad_network = nx.Graph()

# Add nodes (locations)road_network.add_nodes_from(['A', 'B', 'C', 'D', 'E'])

# Add edges (roads) with their respective weights (distances)road_network.add_edge('A', 'B', weight=5)road_network.add_edge('A', 'C', weight=3)road_network.add_edge('B', 'D', weight=2)road_network.add_edge('C', 'D', weight=4)road_network.add_edge('D', 'E', weight=6)

# Calculate the shortest path between two locationsshortest_path = nx.shortest_path(road_network, 'A', 'E', weight='weight')print("Shortest path:", shortest_path)

NetworkX offers a wide range of algorithms and functions for analyzing biological networks, such as protein-protein interaction networks or gene regulatory networks. These analyses can provide insights into complex biological processes and help identify key components. Lets explore an example where we analyze a gene regulatory network.

# Create a gene regulatory network graphgene_network = nx.DiGraph()

# Add nodes (genes)gene_network.add_nodes_from(['GeneA', 'GeneB', 'GeneC', 'GeneD'])

# Add edges (regulations)gene_network.add_edges_from([('GeneA', 'GeneB'), ('GeneA', 'GeneC'), ('GeneB', 'GeneD'), ('GeneC', 'GeneD')])

# Check if GeneA regulates GeneDis_regulating = gene_network.has_edge('GeneA', 'GeneD')print("GeneA regulates GeneD:", is_regulating)

NetworkX can also be used to build recommendation systems based on collaborative filtering or similarity measures. By modeling user-item interactions as a network, we can leverage NetworkX algorithms to make personalized recommendations. Lets consider a scenario where we build a basic user-item recommendation system using the Jaccard similarity coefficient.

# Create a user-item network graphuser_item_network = nx.Graph()

# Add nodes (users and items)user_item_network.add_nodes_from(['UserA', 'UserB', 'UserC', 'Item1', 'Item2', 'Item3'])

# Add edges (user-item interactions)user_item_network.add_edges_from([('UserA', 'Item1'), ('UserA', 'Item2'), ('UserB', 'Item2'), ('UserC', 'Item3')])

# Calculate Jaccard similarity between usersdef jaccard_similarity(u, v):u_neighbors = set(user_item_network.neighbors(u))v_neighbors = set(user_item_network.neighbors(v))intersection = len(u_neighbors.intersection(v_neighbors))union = len(u_neighbors.union(v_neighbors))return intersection / union

# Calculate user-user similarityuser_sim = jaccard_similarity('UserA', 'UserB')print("UserA and UserB similarity:", user_sim)

NetworkX offers a powerful set of tools and algorithms for network analysis in various domains. From social network analysis to transportation networks, biological networks, and recommendation systems, NetworkX enables us to unlock valuable insights and make informed decisions. By combining its functionalities with other data analysis libraries, NetworkX can be a versatile asset in tackling real-world problems. So, dive into the world of network analysis with NetworkX and unlock the hidden connections within your data!

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Scientists Team Up to Protect Personal Data Used in Research – The University of Texas at El Paso

August 1, 2023 | Author: admin

EL PASO, Texas (July 31, 2023) Researchers are partnering to improve privacy and security of sensitive data that may contain personally identifiable information. Pacific Northwest National Laboratory (PNNL) Data Scientist Tony Chiang, DPhil, and University of Texas at El Paso Mathematical Sciences Professor Amy Wagler, Ph.D., are leading the project.

Researchers are partnering to improve privacy and security of sensitive data that may contain personally identifiable information. The University of Texas at El Paso Mathematical Sciences Professor Amy Wagler, Ph.D., and Pacific Northwest National Laboratory Data Scientist Tony Chiang, DPhil, are leading the project.

The project uses machine learning models to morph sensitive, real-world data into artificial data. The artificial data will resemble the original data in every statistical aspect and characteristic but will be shareable without compromising the privacy of those who contributed the data and researchers will still be able to gain valuable insights from it, according to the team.

Our model will make it highly improbable that someone could be identified, said Wagler.

This work will be particularly helpful in the health care research industry, she added, where providers may be concerned about sharing results and risking patient confidentiality.

The computer model the team is making will analyze the original data and generate data that resembles it using statistical models. Then, a separate adversary model will try to discriminate between the two data sets.

As soon as the model cannot discriminate between the two sets of data, we know we can work with it, Wagler said.

Wagler and Chiang maintain joint appointments with UTEP and PNNL. Joint appointments were established to help elevate the productivity of researchers at both institutions, providing strategic capabilities that accelerate scientific impact.

Wagler said, We are often siloed in academia, but working on shared projects with PNNL breaks down those silos and provides opportunities, professional support, and resources we need to solve these challenges.

Shared projects also open doors for students to work with scientists from national laboratories, she said. UTEP doctoral candidates Reagan Kesseku and Cesar Vazquez have both been able to contribute to the project thanks to the partnership.

The differential synthetic data generation project gave me the opportunity to collaborate with renowned researchers and gain exposure to cutting-edge research skills, said Kesseku. I learned the value of interdisciplinary collaboration and how it can drive innovation.

A core component to the partnership between UTEP and PNNL is the commitment to investing in the future science, technology, engineering and mathematics (STEM) workforce by creating opportunities for students to have hands-on research experience, mentorship and experience in a national lab setting. Every year, hundreds of students from around the nation, including UTEP, join PNNL for internship and research associate opportunities. Every student is connected with a dedicated mentor to champion their experience and growth during their time.

The most rewarding part of the collaboration is getting to work with Ph.D. students and helping them open their eyes to see the impact of their work, said Chiang. Often, Ph.D. students are solving problems for a thesis without a clear implication for the usefulness and utility of their work. As a Department of Energy National Laboratory, PNNLtacklessome of the worlds pressing science and technology challenges. Having students here at PNNL affordsthem the opportunity to understand howtheir workreally impacts the mission.

Kesseku added, The project allowed me to apply theoretical concepts to real-world data challenges, fostering critical thinking and problem-solving skills while contributing to meaningful research with practical implications.

Last Updated on July 31, 2023 at 12:00 AM | Originally published July 31, 2023

By MC Staff UTEP Marketing and Communications

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Data Science Platform Market 2022-2027: Analysis of Business … – University City Review

August 1, 2023 | Author: admin

The Data Science Platform Market Share, Analysis Future and Forecast 2022-2027 Research report provides in-depth research on the current market scenario as well as the development environment, market size and share, development trend, operating status, and future growth trend of the examined market. Information on the main market drivers, restraints, challenges, and opportunities is provided to stakeholders, which helps them comprehend the industrys pulse. Through 2027, the report provides in-depth assessments, financial information, and other critical insights concerning the market under consideration. The study stands out as a thorough and comprehensive assessment tool as well as a priceless instrument that will help establish a stronghold in the market.

The report provides a roadmap for how Data Science Platform secured their position in this fast chan Data Science Platform industry by covering in-depth examination of Covid-19 on the market under study and depicting various market scenarios. Participants in the industry can update their plans and procedures by analysing the forecast for market size that is presented in this report.

Ask for a free sample copy. (To Acquire a Complete Understanding of This Reports [Summary + TOC]] Structure) https://www.marknteladvisors.com/query/request-sample/data-science-platform-market.html

Highlights of the report:

Market Forces and Barriers:

The research report examines a number of elements that foster market expansion. It consists of trends, roadblocks, and forces that change the market either positively or negatively. This section also discusses a wide variety of market segments that may influence the industry in the future. The information is based on significant historical occurrences and contemporary trends.

Market competition analysis:

In order to give the necessary information and benchmark data regarding the worldwide Data Science Platform market, Porters Five Forces and PESTLE analysis are provided. The study provides in-depth analysis of the major players in order to provide the full picture of the markets competitive environment. For each player examined in this analysis, this research gives revenue and market value for the years 2022 through 2027. The main regional competitors and their relative market shares based on global revenue are examined in the report. It also discusses their most recent strategy choices, product innovation investments, and leadership adjustments taken to stay ahead of the competition. By empowering the stakeholders to make an informed choice while taking the market as a whole into account, this gives them a competitive edge. Major players included in the report are:

-ActionIQ

-Alphabet Inc. (Google)

-Alteryx Inc.

-Amazon Web Services, Inc.

-Anaconda, Inc.

-Cloudera, Inc.

-Domino Data Lab, Inc.

-IBM Corporation

-H2O.ai

-MathWorks

-Microsoft Corporation

-SAP SE

-SAS Institute, Inc.

-Snowflake Inc.

-Teradata Corporation

-Others

View the Table of Content, Research Methodology, and Full Description of the Report- https://www.marknteladvisors.com/research-library/data-science-platform-market.html

Analysis of market segments:

Specific segments are included in the research paper. Each market category is in-depthly examined in the research report. The studys segmental analysis reveals the key market opportunities through the dominant segments. Readers may gain a complete grasp of how various geographic markets have progressed recently and will continue to do so with the help of the regional research, which is also included in the study. Complete market dynamics analysis, including market variables, drivers, challenges, limitations, trends, and prospects, is included in the report.

Market Divided, by Application

-Marketing & Sales

-Logistics

-Finance & Accounting

-Customer Support

Market Divided, by Deployment

-On-Premise

-Cloud

Market Divided, by Organization Size

-Small & Medium Enterprises

-Large Enterprises

Market Divided, by End-User

-Banking, Financial Services, & Insurance (BFSI)

-Telecom & IT

-Retail & E-commerce

-Healthcare & Life Sciences

-Manufacturing

-Government & Defense

-Energy & Utilities

-Others (Media & Entertainment, Transportation & Logistics, etc.)

Market Divided, by Region

-North America

-South America

-Europe

-Middle East & Africa

-Asia-Pacific

For Any Query Talk to our Consultant https://www.marknteladvisors.com/query/talk-to-our-consultant/data-science-platform-market.html

About MarkNtel Advisors:

MarkNtel Advisors is a leading research, consulting, & data analytics firm that provides an extensive range of strategic reports on diverse industry verticals. We deliver data to a substantial & varied client base, including multinational corporations, financial institutions, governments, & individuals, among others.

Our specialization in niche industries & emerging geographies allows our clients to formulate their strategies in a much more informed way and entail parameters like Go-to-Market (GTM), product development, feasibility analysis, project scoping, market segmentation, competitive benchmarking, market sizing & forecasting, & trend analysis, among others, for 15 diverse industrial verticals. Market watch Link:

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Data Science Platform Market 2022-2027: Analysis of Business ... - University City Review

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Science enabling heat and air conditioning for long-term space … – Purdue University

August 1, 2023 | Author: admin

WEST LAFAYETTE, Ind. To live on the moon or Mars, humans will need heat and air conditioning that can operate long term in reduced gravity and temperatures hundreds of degrees above or below what we experience on Earth.

Building these systems requires knowing how reduced gravity affects boiling and condensation, which all heating, ventilation and air conditioning systems use to operate in Earths gravity.

A Purdue University experiment launching Aug. 1 on Northrop Grummans 19th commercial resupply services mission (NG-19) to the International Space Station for NASA aims to collect data scientists need to answer decades-old questions about how boiling and condensation work in reduced gravity.

We have developed over a hundred years worth of understanding of how heat and cooling systems work in Earths gravity, but we havent known how they work in weightlessness, saidIssam Mudawar,Purdues Betty Ruth and Milton B. Hollander Family Professor ofMechanical Engineering.

The NG-19 spacecraft is expected to launch at 8:31 p.m. on Aug. 1 from the Mid-Atlantic Regional Spaceport at NASAs Wallops Flight Facility in Virginia and arrive at the space station Aug. 4. A livestream of the launch is available via NASA Live.

Onboard this flight is a module for conducting the second experiment of a facility called the Flow Boiling and Condensation Experiment (FBCE), which has been collecting data on the space station since August 2021.

Last July, Mudawar and his students finished their first experiment gathering data from a module of FBCE on the space station that measures the effects of reduced gravity on boiling. When the facilitys additional components arrive with the NG-19 spacecraft, the researchers will be able to conduct the second experiment, which will investigate how condensation works in a reduced-gravity environment.

Both experiments modules for FBCE will remain in orbit through 2025, allowing the fluid physics community at large to take advantage of this hardware.

We are ready to literally close the book on the whole science of flow and boiling in reduced gravity, Mudawar said.

To develop FBCE, Mudawars lab worked with NASAs Glenn Research Center in Cleveland, which engineered and built the flight hardware funded by the agencys Biological and Physical Sciences Division at NASA Headquarters. The team spent 11 years developing FBCE hardware to fit into the Fluids Integrated Rack on the orbiting laboratory.

FBCEs answers on boiling and condensation will not only support exploration on the moon or Mars but also help spacecraft to travel longer distances. The farther missions are from Earth, the more likely that the spacecraft for those missions will need innovative power and propulsion systems, such as ones that are nuclear thermal or electric. Compared to other types of processes that enable heating and cooling in space, boiling and condensation would be much more effective at transferring heat for spacecraft with these systems.

In addition, FBCE data could help enable spacecraft to refuel in orbit by providing scientific understanding of how reduced gravity affects the flow boiling behavior of the cryogenic liquids spacecraft use as propellant.

FBCE is among NASAs largest and most complex experiments for fluid physics research. Mudawars team is preparing a series of research papers unpacking data the FBCE has collected on the space station, adding tomore than 60 papersthey have published on reduced gravity and fluid flow since the projects inception.

The papers we have published over the duration of this project are really almost like a textbook for how to use boiling and condensation in space, Mudawar said.

With more than30,000 citations, Mudawar is one of the most highly cited researchers in the field of heat transfer. Google Scholar ranks him No. 1 in flow boiling, spray cooling, microchannels, and microgravity boiling. He also is the most cited author in theInternational Journal of Heat and Mass Transfer.

For more than a decade, Mudawar and his students have been developing three sets of predictive tools to be validated using FBCE data. One set of tools puts the data into the form of equations that engineers can use to design space systems. Another set identifies fundamental information about fluid physics from the data, and the third set is computational models of the fluid dynamics.

All together, these models would make it possible to predict which equipment designs could operate in lunar and Martian gravity.

The amount of data coming out of the FBCE is just absolutely enormous, and thats exactly what we want, Mudawar said.

About Purdue University

Purdue University is a public research institution with excellence at scale. Ranked among top 10 public universities (Times Higher Education/Wall Street Journal and QS), with two colleges in the top 4 in the United States (U.S. News & World Report), Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, with 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdues main campus has frozen tuition 12 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap, including its first comprehensive urban campus in Indianapolis, the new Mitchell E. Daniels, Jr. School of Business, and Purdue Innovates, athttps://stories.purdue.edu.

Writer/Media contact: Kayla Albert, 765-494-2432, wiles5@purdue.edu

Source: Issam Mudawar,mudawar@ecn.purdue.edu

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Science enabling heat and air conditioning for long-term space ... - Purdue University

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Increase Efficiency of Manufacturing Operations with IoT Solutions – Data Science Central

August 1, 2023 | Author: admin

In an age where efficiency is king, manufacturing firms are in a constant race to outshine their competition. Imagine if you could boost productivity, slash downtime, and cut costs all at once. Sounds like a dream, right? The good news is, this isnt a fantasy. Its achievable through Internet of Things (IoT) solutions.

IoT solutions enable manufacturers to monitor operations in real-time, predict machine failures, and automate processes. You get a high-octane, streamlined operation that fires on all cylinders, day in and day out. The result? Increased efficiency, lower costs, and a significant edge over the competition.

But wait, theres more! IoT isnt just transforming the manufacturing landscape. Its reshaping the world of logistics and supply chain management too. Uncover the myriad ways our IoT solutions for logistics and supply chain management can give your business the kickstart it needs, and step into a future of unprecedented efficiency.

Internet of Things (IoT) is a system of interconnected devices, machines, objects, or people that share data over a network. When applied to manufacturing, it allows for real-time monitoring, automation, and optimization of production processes. The global IoT in manufacturing market size is expected to reach $736.5 billion by 2027, up from $190 billion in 2019 (Statista, 2021). This dramatic growth underlines the immense value and potential that the industry sees in IoT.

So, how does IoT in manufacturing bolster efficiency? Here are five key ways:

Embracing IoT is not an overnight process, it requires careful planning and execution. Here are a few steps to consider:

Identify the Need: Understanding where IoT can be most beneficial in your operations is the first step. This could be in maintenance, automation, energy efficiency, or data analysis.

Choose the Right IoT Solutions: There are various IoT devices and solutions available, each with its strengths and limitations. Choosing the right ones that align with your needs is crucial.

Start Small and Scale: Implement IoT solutions in a small area of your operations first, then scale up based on results and learning.

Invest in Training: The success of it is highly dependent on the employees using it. Invest in training to ensure they understand and can effectively use the technology.

Evaluate and Adapt: Post-implementation, its essential to measure the impact of IoT solutions and adapt accordingly. Using key performance indicators (KPIs), businesses can track whether the implementation is achieving its objectives and make necessary adjustments to maximize efficiency.

As we continue to innovate, IoT will only grow more prominent. We can expect advancements in AI and machine learning to further enhance the capabilities of IoT, providing even greater efficiency gains. As per Statista, by 2025, the number of connected devices worldwide is forecast to reach 75.44 billion, reinforcing the immense potential for IoT in various industries, including manufacturing.

The future is looking bright for IoT, with several trends on the horizon promising to bring significant changes and improvements. Lets take a look at 5 key predictions:

The embrace of IoT in manufacturing has a transformative impact on operational efficiency. When properly implemented, it can bring significant benefits and changes to the production line, supply chain, and overall operations. Here are some ways how:

Enhanced Productivity: IoT devices can monitor equipment and processes in real-time, providing valuable data that can be used to streamline operations, reduce waste, and increase output. According to a study by the MPI Group, factories implementing IoT solutions have seen a 72% increase in productivity.

Reduced Downtime: The predictive maintenance capabilities of IoT solutions can identify potential machinery faults before they lead to downtime. The result is smoother, uninterrupted operations and significant cost savings.

Improved Quality Control: IoT sensors can monitor production quality in real-time, quickly identifying and correcting defects. This not only reduces waste but also improves product quality and customer satisfaction.

Optimized Supply Chain: IoT in manufacturing can track materials and products throughout the supply chain, enhancing visibility and allowing for more accurate demand forecasting and inventory management.

Better Safety Measures: IoT devices can monitor the manufacturing environment for safety hazards and ensure compliance with safety standards, protecting employees and reducing the risk of costly accidents or violations.

The ability to constantly monitor, analyze, and optimize operations leads to more efficient and profitable manufacturing processes, setting the industry on an exciting trajectory toward increased productivity and sustainability.

IoT in manufacturing offers incredible potential to enhance efficiency and productivity. From real-time monitoring to automation, the benefits are clear. However, to fully reap these rewards, businesses must be willing to embrace change, invest in the right technologies, and train their employees. By doing so, the future of manufacturing looks bright, efficient, and highly connected. The power of IoT in each industry could very well be your golden ticket to staying competitive in the manufacturing industry of tomorrow.

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Increase Efficiency of Manufacturing Operations with IoT Solutions - Data Science Central

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