[PDF] Capitalizing Data Science eBook

Author : Mathangi Sri Ramachandran
Publisher : BPB Publications
Page : 295 pages
File Size : 30,63 MB
Release : 2022-12-03
Category : Computers
ISBN : 9355511582

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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

Author : Tarek Riman
Publisher : Cap.TaiM Marketing Inc.
Page : 245 pages
File Size : 49,7 MB
Release : 2019-09-06
Category : Business & Economics
ISBN : 1796616192

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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.

Author : Henry Etzkowitz
Publisher : SUNY Press
Page : 304 pages
File Size : 16,13 MB
Release : 1998-01-01
Category : Education
ISBN : 9780791439470

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Examines current trends toward increasing links between industry and academia and the resulting commercialization of universities as they seek to capitalize their research.

Author : Shriram Girishkumar Gajjar
Publisher :
Page : pages
File Size : 17,27 MB
Release : 2017
Category :
ISBN : 9780355763805

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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.

Author : Nguyen Ngoc Thach
Publisher : Springer Nature
Page : 633 pages
File Size : 25,34 MB
Release : 2020-11-13
Category : Computers
ISBN : 3030488535

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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.

Author : John W. Foreman
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 47,92 MB
Release : 2013-10-31
Category : Business & Economics
ISBN : 1118839862

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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.

Author : Dr. A. Vijayalakshmi
Publisher : Academic Guru Publishing House
Page : 250 pages
File Size : 35,44 MB
Release : 2024-02-06
Category : Study Aids
ISBN : 8119843711

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"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.

Author : Greta R. Krippner
Publisher : Harvard University Press
Page : 241 pages
File Size : 12,92 MB
Release : 2012-09-10
Category : Social Science
ISBN : 0674735315

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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.