Capitalizing Data Science

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Capitalizing Data Science

Author : Mathangi Sri Ramachandran
Publisher : BPB Publications
ISBN 13 : 9355511582
Total Pages : 295 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Capitalizing Data Science by : Mathangi Sri Ramachandran

Download or read book Capitalizing Data Science written by Mathangi Sri Ramachandran and published by BPB Publications. This book was released on 2022-12-03 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the Potential of Data Science and Machine Learning to Your Business and Organization KEY FEATURES ● Includes today's most popular applications powered by data science and machine learning technology. ● A solid primer on the entire data science lifecycle, detailed with examples. ● An integrated approach to demonstrating the use of Image Processing, Natural Language Processing, and Neural Networks in business. DESCRIPTION Can you foresee how your company and its products will benefit from data science? How can the results of using AI and ML in business be tracked and questioned? Do questions like ‘how do you build a data science team?’ keep popping into your head? All these strategic concerns and challenges are addressed in this book. Firstly, the book explores the evolution of decision-making based on empirical evidence. The book then helps compare the data-supported era with the current data-led era. It also discusses how to successfully run a data science project, the lifecycle of a data science project, and what it looks like. The book dives fairly in-depth into various today's data-led applications, highlights example datasets, discusses obstacles, and explains machine learning models and algorithms intuitively. This book covers structural and organizational considerations for making a data science team. The book helps recommend the use of optimal data science organization structure based on the company's level of development. Finally, the book explains data science's effects on businesses by assisting technological leaders. WHAT YOU WILL LEARN ● Learn the entire data science lifecycle and become fluent in each phase. ● Discover the world of supervised and unsupervised learning applications and structured and unstructured datasets. ● Discuss NLP's function, its potential, and the application of well-known methods like BERT and GPT3. ● Explain practical applications like automatic captioning, machine translation, and emotion recognition. ● Provide a framework for evaluating your team's data science skills and resources. WHO THIS BOOK IS FOR Startups, investors, small businesses, product management teams, CxO and all developing businesses desiring to leverage a data science team to gain the most from this book. The book also discusses the potential of practical applications of machine learning and AI for the future of businesses in banking and e-commerce. TABLE OF CONTENTS 1. Data-Driven Decisions from Beginning to Now 2. Data Science Life Cycle —Part 1 3. Data Science Life Cycle —Part 2 4. Deep Dive into AI 5. Applying AI with Structured Data—Banking 6. Applying AI with Structured Data 7. Applying AI with Structured Data—On-Demand Deliveries 8. AI in Natural Language Processing 9. Bringing It All Together

The Secret to Capitalizing on Analytics

Author : Tarek Riman
Publisher : Cap.TaiM Marketing Inc.
ISBN 13 : 1796616192
Total Pages : 245 pages
Book Rating : 4.7/5 (966 download)

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Book Synopsis The Secret to Capitalizing on Analytics by : Tarek Riman

Download or read book The Secret to Capitalizing on Analytics written by Tarek Riman and published by Cap.TaiM Marketing Inc.. This book was released on 2019-09-06 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Secret to Capitalizing on Analytics' purpose is to help start-ups, students, beginners and entrepreneurs understand how to use data to optimize and improve their business and marketing strategy. All businesses today, no matter what their size, need to know how their website is performing. Without analytics, there is no way for a company to know how their website is performing in terms of attracting, informing and converting visitors.In this book, you will learn how to get started with Google Analytics and how to set it up for optimal tracking. You will also learn to assess which marketing campaigns bring the best traffic to your website, which pages on your website are the most popular and how to extract information about your visitors. Information such as location, interests, age, behaviours and more so you can better understand your web traffic and capitalize on your marketing. You will also learn how to capitalize on the different trends and tools that are available.

Capitalizing Knowledge

Author : Henry Etzkowitz
Publisher : SUNY Press
ISBN 13 : 9780791439470
Total Pages : 304 pages
Book Rating : 4.4/5 (394 download)

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Book Synopsis Capitalizing Knowledge by : Henry Etzkowitz

Download or read book Capitalizing Knowledge written by Henry Etzkowitz and published by SUNY Press. This book was released on 1998-01-01 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines current trends toward increasing links between industry and academia and the resulting commercialization of universities as they seek to capitalize their research.

Data Science for Financial Econometrics

Author : Nguyen Ngoc Thach
Publisher : Springer Nature
ISBN 13 : 3030488535
Total Pages : 633 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Data Science for Financial Econometrics by : Nguyen Ngoc Thach

Download or read book Data Science for Financial Econometrics written by Nguyen Ngoc Thach and published by Springer Nature. This book was released on 2020-11-13 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.

Capitalizing from Data

Author : Shriram Girishkumar Gajjar
Publisher :
ISBN 13 : 9780355763805
Total Pages : pages
Book Rating : 4.7/5 (638 download)

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Book Synopsis Capitalizing from Data by : Shriram Girishkumar Gajjar

Download or read book Capitalizing from Data written by Shriram Girishkumar Gajjar and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: With the advances in communication technologies and the emergence of smart factories, large volumes of data are routinely collected and stored at high sampling rates. The data generation and collection are so fast-paced that humans have to rely on computers for consuming as well as processing the data. This necessitates development of algorithms and methods that can be used to improve process performance and facilitate process monitoring. My research interests are in application of multi-dimensional visualizations, advanced statistical and mathematical algorithms tailored to extract meaningful information from large data-sets collected in industrial plant operations and scientific experiments. These algorithms will at first, be able to unlock significant information from the large datasets. Second, they will provide accurate means to reduce process variability and boost performance. Third, they will allow discovery of the underlying process dynamics that can substantially improve decision-making. Finally, they will provide recommendations for steps that can be taken proactively to avoid sub-optimal and abnormal operations. Methods: 1. Visualization: A control chart is one of the primary techniques for statistical process monitoring of real-time data. However, monitoring hundreds of variables simultaneously using univariate control charts is difficult. An approach based on parallel coordinates is developed to address this challenge. 2. Data dimension reduction: Principal component analysis (PCA), a widely used multivariate technique with various applications ranging from facial recognition to clustering is implemented. One of the drawbacks of using PCA for dimension reduction is that most variable loadings are typically non-zero. Such non-zero variable loadings (NZL) make it difficult to interpret the derived principal components and may confound subsequent analyses. To address this challenge, Sparse Principal Component Analysis (SPCA) is used but specifying number of NZL for each sparse principal component is a numerically hard combinatorial problem. Evolutionary algorithms in conjunction with SPCA are implemented to tackle such combinatorial optimization problems and gain insights about the underlying dynamics of the data. Least Squares SPCA (LS SPCA) is then introduced wherein uncorrelatedness constraints on the components are imposed to obtain uncorrelated sparse loadings 3. Real-time analytics: A novel real-time fault detection, machine learning based diagnosis is developed. Summary: This dissertation, at first, will focus on determination of number of non-zero loadings in SPCA. Second, it will compare the performance of SPCA with PCA for process monitoring that also enables process knowledge discovery. Lastly, parallel coordinates and LS SPCA are introduced for process monitoring and validity of the proposed techniques will be demonstrated through a benchmark process simulator. Keywords: Fault detection, Fault diagnosis, Random Forests, Singular value decomposition, Principal component analysis (PCA), Sparse principal component analysis (SPCA), Least Squares Sparse principal component analysis (LS SPCA), Multivariate statistical process monitoring, Multidimensional visualization, Genetic Algorithms, Random Forests, Big Data, Historical data analysis, Tennessee Eastman process.

Data Smart

Author : John W. Foreman
Publisher : John Wiley & Sons
ISBN 13 : 1118839862
Total Pages : 432 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Data Smart by : John W. Foreman

Download or read book Data Smart written by John W. Foreman and published by John Wiley & Sons. This book was released on 2013-10-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.

Capitalizing on big data

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (961 download)

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Book Synopsis Capitalizing on big data by :

Download or read book Capitalizing on big data written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Intelligence And Data Analytics

Author : Dr. A. Vijayalakshmi
Publisher : Academic Guru Publishing House
ISBN 13 : 8119843711
Total Pages : 250 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Artificial Intelligence And Data Analytics by : Dr. A. Vijayalakshmi

Download or read book Artificial Intelligence And Data Analytics written by Dr. A. Vijayalakshmi and published by Academic Guru Publishing House. This book was released on 2024-02-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Artificial Intelligence and Data Analytics" is an essential manual that clarifies the intricate yet enthralling domains of AI and Data Analytics, providing readers with an all-encompassing examination of the revolutionary potential that these technologies possess in the present-day environment. An indispensable resource for professionals, academicians, and enthusiasts desiring a profound comprehension of the interrelationships among artificial intelligence and data analytics, this book has been painstakingly crafted. The book commences with a meticulously organized structure that establishes a strong groundwork, exploring the fundamental principles of data analytics, machine learning, and artificial intelligence. The narrative proceeds with case studies and real-world applications that shed light on the pragmatic ramifications of these technologies in various sectors, including healthcare, finance, and e-commerce. This book is distinguished by its nuanced treatment of ethical considerations, which addresses the conscientious and responsible application of artificial intelligence and data-driven insights. By delving into sophisticated algorithms and addressing the complexities of big data, the book provides readers with a comprehensive understanding of these ever-evolving domains through the application of both theoretical and practical expertise. Irrespective of one's level of expertise, "Artificial Intelligence and Data Analytics" provides an engaging exploration of the latest advancements and prospective prospects, assisting individuals in maximizing the capabilities of AI and Data Analytics within their specific fields.

Big Data Analytics for the Prediction of Tourist Preferences Worldwide

Author : N. Padmaja
Publisher : Emerald Group Publishing
ISBN 13 : 1835493408
Total Pages : 116 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Big Data Analytics for the Prediction of Tourist Preferences Worldwide by : N. Padmaja

Download or read book Big Data Analytics for the Prediction of Tourist Preferences Worldwide written by N. Padmaja and published by Emerald Group Publishing. This book was released on 2024-02-22 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics for the Prediction of Tourist Preferences Worldwide explores the benefits, importance and demonstrates how Big Data can be applied in predicting tourist preferences and delivering tourism services in a customer friendly manner.

Python for Data Science

Author : A. Lakshmi Muddana
Publisher : Springer Nature
ISBN 13 : 303152473X
Total Pages : 398 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Python for Data Science by : A. Lakshmi Muddana

Download or read book Python for Data Science written by A. Lakshmi Muddana and published by Springer Nature. This book was released on with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Rise of the Data Cloud

Author : Frank Slootman
Publisher : AuthorHouse
ISBN 13 : 1728373069
Total Pages : 200 pages
Book Rating : 4.7/5 (283 download)

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Book Synopsis Rise of the Data Cloud by : Frank Slootman

Download or read book Rise of the Data Cloud written by Frank Slootman and published by AuthorHouse. This book was released on 2020-12-18 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise of the Data Cloud is ushering in a new era of computing. The world’s digital data is mass migrating to the cloud, where it can be more effectively integrated, managed, and mobilized. The data cloud eliminates data siloes and enables data sharing with business partners, capitalizing on data network effects. It democratizes data analytics, making the most sophisticated data science tools accessible to organizations of all sizes. Data exchanges enable businesses to discover, explore, and easily purchase or sell data—opening up new revenue streams. Business leaders have long dreamed of data driving their organizations. Now, thanks to the Data Cloud, nothing stands in their way.

It's All Analytics, Part III

Author : Scott Burk
Publisher : CRC Press
ISBN 13 : 1000928446
Total Pages : 248 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis It's All Analytics, Part III by : Scott Burk

Download or read book It's All Analytics, Part III written by Scott Burk and published by CRC Press. This book was released on 2023-09-15 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years, there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term, called "analytics," is an overarching description of a compilation of methodologies. But artificial intelligence (AI), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? Extending on the foundations introduced in the first book, this book illustrates how professionals in healthcare, business, and government are applying these disciplines, methods, and technologies. The goal of this book is to get leaders and practitioners to start thinking about how they may deploy techniques outside their function or industry into their domain. Application of modern technology into new areas is one of the fastest, most effective ways to improve results. By providing a rich set of examples, this book fosters creativity in the application and use of AI and analytics in innovative ways.

Oxford A-Z of Grammar and Punctuation

Author : John Seely
Publisher : Oxford University Press, USA
ISBN 13 : 019966918X
Total Pages : 209 pages
Book Rating : 4.1/5 (996 download)

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Book Synopsis Oxford A-Z of Grammar and Punctuation by : John Seely

Download or read book Oxford A-Z of Grammar and Punctuation written by John Seely and published by Oxford University Press, USA. This book was released on 2013-08-15 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides the basic information about grammar and punctuation that people need on a day-to-day basis. Arranged A to Z, it contains entries for standard grammatical terms as well as dealing with specific questions of usage.

Capitalizing on Crisis

Author : Greta R. Krippner
Publisher : Harvard University Press
ISBN 13 : 0674735315
Total Pages : 241 pages
Book Rating : 4.6/5 (747 download)

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Book Synopsis Capitalizing on Crisis by : Greta R. Krippner

Download or read book Capitalizing on Crisis written by Greta R. Krippner and published by Harvard University Press. This book was released on 2012-09-10 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the context of the recent financial crisis, the extent to which the U.S. economy has become dependent on financial activities has been made abundantly clear. In Capitalizing on Crisis, Greta Krippner traces the longer-term historical evolution that made the rise of finance possible, arguing that this development rested on a broader transformation of the U.S. economy than is suggested by the current preoccupation with financial speculation. Krippner argues that state policies that created conditions conducive to financialization allowed the state to avoid a series of economic, social, and political dilemmas that confronted policymakers as postwar prosperity stalled beginning in the late 1960s and 1970s. In this regard, the financialization of the economy was not a deliberate outcome sought by policymakers, but rather an inadvertent result of the state’s attempts to solve other problems. The book focuses on deregulation of financial markets during the 1970s and 1980s, encouragement of foreign capital into the U.S. economy in the context of large fiscal imbalances in the early 1980s, and changes in monetary policy following the shift to high interest rates in 1979. Exhaustively researched, the book brings extensive new empirical evidence to bear on debates regarding recent developments in financial markets and the broader turn to the market that has characterized U.S. society over the last several decades.

Data Science Thinking

Author : Longbing Cao
Publisher : Springer
ISBN 13 : 3319950924
Total Pages : 404 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Data Science Thinking by : Longbing Cao

Download or read book Data Science Thinking written by Longbing Cao and published by Springer. This book was released on 2018-08-17 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

What's the Big Idea?

Author : Thomas H. Davenport
Publisher : Harvard Business Press
ISBN 13 : 9781578519316
Total Pages : 264 pages
Book Rating : 4.5/5 (193 download)

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Book Synopsis What's the Big Idea? by : Thomas H. Davenport

Download or read book What's the Big Idea? written by Thomas H. Davenport and published by Harvard Business Press. This book was released on 2003 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores where new ideas come from, how to evaluate which ideas are worth pursuing, and how to customize ideas to suit and organization's unique needs.

Data Science

Author : Ivo D. Dinov
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110697823
Total Pages : 489 pages
Book Rating : 4.1/5 (16 download)

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Book Synopsis Data Science by : Ivo D. Dinov

Download or read book Data Science written by Ivo D. Dinov and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-12-06 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.