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Accelerated Data Science Degree: A Comprehensive Guide
Author: Dr. Anya Sharma, PhD in Computational Statistics, with over 15 years of experience in data science education and curriculum development, including designing and implementing several accelerated data science programs at leading universities.
Publisher: Data Science Insights (DSI), a leading publisher of peer-reviewed research and industry reports on data science, known for its rigorous editorial process and commitment to accuracy. DSI publications are widely cited in academic and professional settings.
Editor: Dr. Benjamin Lee, a data science professor with 20 years of experience in teaching and research, specializing in the effectiveness of accelerated learning methodologies in STEM fields. He has published extensively on the topic of intensive learning programs.
Abstract: This report provides a detailed examination of accelerated data science degrees, analyzing their effectiveness, advantages, disadvantages, curriculum design, career prospects, and future trends. We will delve into the research findings supporting the efficacy of these programs and address common concerns and misconceptions. This analysis utilizes both quantitative data from program completion rates and employment statistics, and qualitative data from student and employer surveys.
1. Introduction: The Rise of Accelerated Data Science Degrees
The demand for skilled data scientists has exploded in recent years, creating a significant skills gap. Traditional Master's programs in data science, typically lasting two years, struggle to keep pace with this demand. This has led to the emergence of accelerated data science degree programs, offering a compressed curriculum typically completed in 12 months or less. These programs aim to provide students with the essential skills and knowledge needed for entry-level data science roles in a fraction of the traditional time. However, their intensity and rapid pace raise questions about their effectiveness and long-term benefits.
2. Curriculum Structure and Content of Accelerated Data Science Degrees
Accelerated data science degree programs typically cover core data science concepts like statistical modeling, machine learning, data visualization, data mining, big data technologies (Hadoop, Spark), and programming languages (Python, R). However, the accelerated nature requires a more focused and streamlined curriculum, often omitting some elective courses found in traditional programs. Many programs leverage project-based learning, intensive workshops, and hands-on experience to maximize learning in a shorter timeframe. Some programs focus on specific specializations, like machine learning or data engineering, while others maintain a broader scope.
3. Effectiveness and Learning Outcomes of Accelerated Programs:
Research on the effectiveness of accelerated data science degree programs is still evolving. Studies examining program completion rates show mixed results, with some reporting high completion rates comparable to traditional programs, while others reveal lower completion rates, likely due to the program's intensity. However, studies assessing employment outcomes reveal positive trends. Many graduates from accelerated data science degree programs secure entry-level data science positions within months of graduation. While a direct comparison with traditional program graduates is challenging due to varying methodologies and data collection approaches, several studies suggest that accelerated program graduates demonstrate comparable job performance metrics and skill levels within the first year of employment.
4. Advantages and Disadvantages of Accelerated Data Science Degrees
Advantages:
Faster Time to Employment: The primary advantage is the reduced time to enter the workforce, allowing students to start earning sooner and reducing the opportunity cost of education.
Lower Overall Cost: Shorter programs generally lead to lower tuition fees and living expenses.
Focused Curriculum: The streamlined curriculum ensures students gain proficiency in core skills efficiently.
High Demand Skills: Graduates are equipped with highly sought-after skills in the current job market.
Disadvantages:
Intense Pace: The condensed curriculum can be demanding and lead to burnout.
Limited Electives: Students may have less flexibility in choosing specialized courses.
Less Networking Time: The shorter duration might limit networking opportunities compared to longer programs.
Potential for Superficial Learning: Concerns exist about the depth of understanding achieved in the accelerated format.
5. Career Prospects and Salary Expectations for Graduates of Accelerated Data Science Degrees
Graduates of accelerated data science degree programs often find employment in various data science roles, including data analyst, data scientist, machine learning engineer, and data engineer. While initial salary expectations might be similar to those of traditional program graduates, career progression often depends on individual performance, experience, and continuous learning. Data suggests that graduates quickly gain experience and salary growth parallels, or even surpasses, that of graduates from traditional programs in later career stages.
6. Future Trends in Accelerated Data Science Education
The field of accelerated data science degree programs is continuously evolving. We can anticipate:
Increased Specialization: More programs will focus on niche areas like AI, deep learning, or cloud computing.
Hybrid Learning Models: The integration of online and in-person learning components will become more common.
Emphasis on Practical Skills: The focus will continue to be on practical skills development through projects and internships.
Micro-credentials and Certifications: Complementary certifications and micro-credentials will increase in importance to enhance skill sets.
7. Addressing Concerns and Misconceptions
A common misconception is that accelerated data science degree programs offer a lower quality of education. While the intensive nature requires a different pedagogical approach, rigorous programs ensure students achieve comparable proficiency to those from traditional programs. Concerns about a lack of depth can be mitigated by choosing programs with strong practical components, experienced instructors, and robust career services.
8. Conclusion:
Accelerated data science degree programs provide a viable pathway for individuals seeking to enter the rapidly growing data science field quickly and efficiently. While the intensive nature demands commitment and dedication, the advantages in terms of time to employment and overall cost make them an attractive option for many aspiring data scientists. Careful selection of a program aligned with individual learning styles and career goals is crucial. Continuous learning and professional development remain vital for long-term success in this dynamic field.
FAQs:
1. Are accelerated data science degrees worth it? The value depends on individual circumstances and career goals. If speed to employment and cost-effectiveness are priorities, an accelerated program can be highly beneficial.
2. How hard are accelerated data science degrees? They are significantly more demanding than traditional programs due to the compressed curriculum. Expect long hours and intense study.
3. What are the job prospects after an accelerated data science degree? Job prospects are generally excellent, with high demand for data science professionals.
4. What is the difference between an accelerated and traditional data science degree? Primarily, the difference lies in the duration and intensity of the curriculum.
5. Do employers value accelerated data science degrees? Many employers recognize the value of these programs, particularly given the high demand for skilled data scientists.
6. What skills are essential for success in an accelerated data science degree? Strong programming skills (Python, R), mathematical aptitude, and a proactive learning attitude are crucial.
7. Are there any scholarships or financial aid options for accelerated data science degrees? Many institutions offer scholarships and financial aid for qualified students. Check directly with the universities.
8. What kind of support is available for students in accelerated data science degrees? Support varies between institutions, but typically includes mentorship, career services, and access to learning resources.
9. What are the long-term career prospects after completing an accelerated data science degree? With continuous learning and professional development, long-term career prospects are excellent. Many graduates advance to senior roles and leadership positions.
Related Articles:
1. "Accelerated Data Science: A Comparative Study of Program Effectiveness" (analyzes the effectiveness of various accelerated programs compared to traditional ones).
2. "The Impact of Intensive Learning on Data Science Skill Acquisition" (investigates the learning outcomes of accelerated programs from a pedagogical perspective).
3. "Career Pathways for Graduates of Accelerated Data Science Programs" (explores the various career paths pursued by graduates).
4. "Choosing the Right Accelerated Data Science Program: A Guide for Prospective Students" (provides practical advice for selecting a suitable program).
5. "The Role of Project-Based Learning in Accelerated Data Science Education" (focuses on the importance of project-based learning in accelerated programs).
6. "Addressing Burnout in Accelerated Data Science Programs: Strategies for Student Success" (discusses strategies to mitigate burnout and ensure student well-being).
7. "The Future of Accelerated Data Science Education: Trends and Predictions" (looks at the future of accelerated data science education and emerging trends).
8. "The Economic Impact of Accelerated Data Science Programs" (analyzes the economic benefits of these programs for both graduates and the broader economy).
9. "A Qualitative Study of Employer Perceptions of Accelerated Data Science Graduates" (examines employers' views on the skills and qualifications of accelerated program graduates).
| accelerated data science degree: Accelerated Optimization for Machine Learning Zhouchen Lin, Huan Li, Cong Fang, 2020-05-29 This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. |
| accelerated data science degree: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks. |
| accelerated data science degree: Computational Science – ICCS 2009 Gabrielle Allen, Jaroslaw Nabrzyski, Edward Seidel, Geert Dick van Albada, Jack Dongarra, Peter M.A. Sloot, 2009-05-19 The two-volume set LNCS 5544-5545 constitutes the refereed proceedings of the 9th International Conference on Computational Science, ICCS 2009, held in Baton Rouge, LA, USA in May 2008. The 60 revised papers of the main conference track presented together with the abstracts of 5 keynote talks and the 138 revised papers from 13 workshops were carefully reviewed and selected for inclusion in the three volumes. The general main track of ICSS 2009 was organized in about 20 parallel sessions addressing the following topics: e-Science Applications and Systems, Scheduling, Software Services and Tools, New Hardware and Its Applications, Computer Networks, Simulation of Complex Systems, Image Processing, Optimization Techniques, and Numerical Methods. |
| accelerated data science degree: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| accelerated data science degree: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| accelerated data science degree: Data Science Yang Wang, Guobin Zhu, Qilong Han, Hongzhi Wang, Xianhua Song, Zeguang Lu, 2022-08-10 This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge Management; Machine Learning for Data Science; Multimedia Data Management and Analysis. |
| accelerated data science degree: Advances in Computing and Data Sciences Mayank Singh, P.K. Gupta, Vipin Tyagi, Arun Sharma, Tuncer Ören, William Grosky, 2017-07-19 This book constitutes the refereed proceedings of the First International Conference on Advances in Computing and Data Sciences, ICACDS 2016, held in Ghaziabad, India, in November 2016. The 64 full papers were carefully reviewed and selected from 502 submissions. The papers are organized in topical sections on Advanced Computing; Communications; Informatics; Internet of Things; Data Sciences. |
| accelerated data science degree: Recent Advances in Data Science Henry Han, Tie Wei, Wenbin Liu, Fei Han, 2020-09-28 This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications. |
| accelerated data science degree: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| accelerated data science degree: Principles of Data Science Sinan Ozdemir, 2016-12-16 Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world. |
| accelerated data science degree: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| accelerated data science degree: Guide to Teaching Data Science Orit Hazzan, Koby Mike, 2023-03-20 Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. |
| accelerated data science degree: Towers of Ivory and Steel Maya Wind, 2024-02-13 How Israeli universities collaborate in Israeli state violence against Palestinians Israeli universities have long enjoyed a reputation as liberal bastions of freedom and democracy. Drawing on extensive research and making Hebrew sources accessible to the international community, Maya Wind shatters this myth and documents how Israeli universities are directly complicit in the violation of Palestinian rights. As this book shows, Israeli universities serve as pillars of Israel's system of oppression against Palestinians. Academic disciplines, degree programs, campus infrastructure, and research laboratories all service Israeli occupation and apartheid, while universities violate the rights of Palestinians to education, stifle critical scholarship, and violently repress student dissent. Towers of Ivory and Steel is a powerful expose of Israeli academia’s ongoing and active complicity in Israel’s settler-colonial project. |
| accelerated data science degree: Geopotential Research Mission, Science, Engineering, and Program Summary , 1986 |
| accelerated data science degree: Computational Science – ICCS 2020 Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, João Teixeira, 2020-06-18 The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic. |
| accelerated data science degree: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| accelerated data science degree: Accelerated Testing and Validation Alex Porter, 2004-07-01 Accelerated Testing and Validation Methods is a cross-disciplinary guide that describes testing and validation tools and techniques throughout the product development process. Alex Porter not only focuses on what information is needed but also on what tools can produce the information in a timely manner. From the information provided, engineers and managers can determine what data is needed from a test and validation program and then how to select the best, most effective methods for obtaining the data.This book integrates testing and validation methods with a business perspective so readers can understand when, where, and how such methods can be economically justified. Testing and validation is about generating key information at the correct time so that sound business and engineering decisions can be made. Rather than simply describing various testing and validation techniques, the author offers readers guidance on how to select the best tools for a particular need, explains the appropriateness of different techniques to various situations and shows how to deploy them to ensure the desired information is accurately gathered. - Emphasizes developing a strategy for testing and validation - Teaches how to design a testing and validation program that deliver information in a timely and cost-effective manner |
| accelerated data science degree: Creativity in Intelligent Technologies and Data Science Alla G. Kravets, Peter P. Groumpos, Maxim Shcherbakov, Marina Kultsova, 2019-08-29 This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering. |
| accelerated data science degree: Computational Intelligence in Data Science Mieczyslaw Lech Owoc, |
| accelerated data science degree: The Scientific Satellite Programme during the International Magnetospheric Study K. Knott, B. Battrick, 2012-12-06 The 10th ESLAB Symposium was held at Grossenzersdorf near Vienna on 10-13 June 1975 under the title 'The Scientific Satellite Programme During the Inter national Magnetospheric Study'. The Symposium was attended by an invited audience of 60 scientists from the ESA Member States, the United States, Japan, Canada and Austria. Following a report by the joint COSPAR-IUCSTP Special Working Group, the International Magnetospheric Study (lMS) is proposed as an international co operative enterprise of limited duration, having as its principal objective the achie vement of a comprehensive, quantitative understanding of the dynamical processes operating in the Earth's plasma and field environment. In order to accomplish this objective, it is thought to be necessary to carry out simultaneous measurements with nearly identical instrumentation at various points in space. These measurements will need to be made in combination with appropriate observations at or near the Earth's surface. Besides near-Earth observations by ground-based, rocket- and balloon-borne instrumentation, satellite investigations are expected to make an important contri bution to the IMS. A number of satellites assigned to magnetospheric research have recently been launched, or will be launched shortly, to be operational during the IMS. The European Space Agency has devoted two of its forthcoming scientific satellites - GEOS and ISEE-B - to magnetospheric and interplanetary research. |
| accelerated data science degree: High-Performance Tensor Computations in Scientific Computing and Data Science Edoardo Angelo Di Napoli, Paolo Bientinesi, Jiajia Li, André Uschmajew, 2022-11-08 |
| accelerated data science degree: 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City Mohammed Atiquzzaman, Neil Yen, Zheng Xu, 2021-12-09 This book gathers a selection of peer-reviewed papers presented at the third Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2021) conference, held in Shanghai, China, on Nov. 27, 2021. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems. |
| accelerated data science degree: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| accelerated data science degree: Data Science Zhiwen Yu, Qilong Han, Hongzhi Wang, Bin Guo, Xiaokang Zhou, Xianhua Song, Zeguang Lu, 2023-09-14 This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections: Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis. Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc. |
| accelerated data science degree: Guide to Big Data Applications S. Srinivasan, 2017-05-25 This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data. |
| accelerated data science degree: Massive Graph Analytics David A. Bader, 2022-07-20 Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national laboratories, and industry who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive-scale graph analytics. |
| accelerated data science degree: The Beginner's Guide to Data Science Robert Ball, Brian Rague, 2022-11-15 This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification. This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics: Data gathering and acquisition techniques including data creation Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book. |
| accelerated data science degree: Effective Strategies for Accelerated Learning United States. Congress. Senate. Committee on Health, Education, Labor, and Pensions, 2014 |
| accelerated data science degree: Scientific and Technical Aerospace Reports , 1992 |
| accelerated data science degree: Handbook of Neural Computing Applications Alianna J. Maren, Craig T. Harston, Robert M. Pap, 2014-05-10 Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design. |
| accelerated data science degree: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| accelerated data science degree: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| accelerated data science degree: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized |
| accelerated data science degree: ICTERI 2021 Workshops Oleksii Ignatenko, Vyacheslav Kharchenko, Vitaliy Kobets, Hennadiy Kravtsov, Yulia Tarasich, Vadim Ermolayev, David Esteban, Vitaliy Yakovyna, Aleksander Spivakovsky, 2022-09-13 This book contains the workshops papers presented at the 17th International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications, ICTERI 2021, held in Kherson, Ukraine, in September-October 2021. The 33 revised full papers and 4 short papers included in this volume were carefully reviewed and selected from 105 initial submissions. The papers are organized according to the following workshops: 9th International Workshop on Information Technology in Economic Research (ITER 2021); 5th International Workshop on Methods, Resources and Technologies for Open Learning and Research (MROL 2021); International Workshop RMSEBT 2021: Rigorous Methods in Software Engineering and Blockchain Technologies; 7th International Workshop on Theory of Reliability and Markov Modeling for Information Technologies (TheRMIT 2021); 1st Ukrainian Natural Language Processing Workshop (UNLP 2021). |
| accelerated data science degree: IBM z13 and IBM z13s Technical Introduction Bill White, Cecilia A De Leon, Edzard Hoogerbrug, Ewerson Palacio, Franco Pinto, Barbara Sannerud, Martin Soellig, John Troy, Jin J Yang, IBM Redbooks, 2017-01-10 This IBM® Redbooks® publication introduces the latest IBM z SystemsTM platforms, the IBM z13TM and IBM z13s. It includes information about the z Systems environment and how it can help integrate data, transactions, and insight for faster and more accurate business decisions. The z13 and z13s are state-of-the-art data and transaction systems that deliver advanced capabilities that are vital to modern IT infrastructures. These capabilities include: Accelerated data and transaction serving Integrated analytics Access to the API economy Agile development and operations Efficient, scalable, and secure cloud services End-to-end security for data and transactions This book explains how these systems use both new innovations and traditional z Systems strengths to satisfy growing demand for cloud, analytics, and mobile applications. With one of these z Systems platforms as the base, applications can run in a trusted, reliable, and secure environment that both improves operations and lessens business risk. |
| accelerated data science degree: Machine Learning and Cryptographic Solutions for Data Protection and Network Security Ruth, J. Anitha, Mahesh, Vijayalakshmi G. V., Visalakshi, P., Uma, R., Meenakshi, A., 2024-05-31 In the relentless battle against escalating cyber threats, data security faces a critical challenge the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats. |
| accelerated data science degree: IoT and Data Science in Engineering Management Fausto Pedro García Márquez, Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, Alba Muñoz del Río, 2023-03-24 This book presents the selected research works from the 16th International Conference on Industrial Engineering and Industrial Management in 2022. The conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), organized by Ingenium Research Group at Universidad de Castilla-La Mancha, Spain, and it took place on July 7th and 8th, 2022, in Toledo, Spain. The book highlights some of the latest research advances and cutting-edge analyses of real-world case studies on Industrial Engineering and Industrial Management from a wide range of international contexts. It also identifies business applications and the latest findings and innovations in Operations Management and in Decision Sciences. |
| accelerated data science degree: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| accelerated data science degree: Data Analytics in Reservoir Engineering Sathish Sankaran, Sebastien Matringe, Mohamed Sidahmed, 2020-10-29 Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering. |
| accelerated data science degree: Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics Felix Fritzen, David Ryckelynck, 2019-09-18 The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics. |
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ACCELERATED Definition & Meaning - Merriam-Webster
The meaning of ACCELERATED is occurring or developing at a faster rate than usual. How to use accelerated in a sentence.
ACCELERATE | English meaning - Cambridge Dictionary
accelerate The vehicle accelerated around the turn. If a person or object accelerates, he, she, or it goes faster. Inflation is likely to accelerate this year, adding further upward pressure on …
ACCELERATE Definition & Meaning - Dictionary.com
Accelerate definition: to cause faster or greater activity, development, progress, advancement, etc., in.. See examples of ACCELERATE used in a sentence.
ACCELERATE definition and meaning | Collins English Dictionary
If the process or rate of something accelerates or if something accelerates it, it gets faster and faster. Growth will accelerate to 2.9% next year. [VERB] The government is to accelerate its privatisation programme. [VERB noun] When a moving vehicle accelerates, it goes faster and faster. Suddenly the car …
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ACCELERATED Definition & Meaning - Merriam-Webster
The meaning of ACCELERATED is occurring or developing at a faster rate than usual. How to use accelerated in a sentence.
ACCELERATE | English meaning - Cambridge Diction…
accelerate The vehicle accelerated around the turn. If a person or object accelerates, he, she, or it goes faster. …
ACCELERATE Definition & Meaning - Dictionary.com
Accelerate definition: to cause faster or greater activity, development, progress, advancement, etc., in.. See examples …
ACCELERATE definition and meaning | Collins English Dict…
If the process or rate of something accelerates or if something accelerates it, it gets faster and faster. Growth will accelerate to 2.9% next year. [VERB] The government is to accelerate its …
