Capitalizing Data Science

Author : Mathangi Sri Ramachandran
Publisher : BPB Publications
Page : 295 pages
File Size : 55,7 Mb
Release : 2022-12-03
Category : Computers
ISBN : 9789355511584

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Capitalizing Data Science by Mathangi Sri Ramachandran Pdf

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 : Dr. A. Vijayalakshmi,Dr. Jayasudha M
Publisher : Academic Guru Publishing House
Page : 250 pages
File Size : 55,9 Mb
Release : 2024-02-06
Category : Study Aids
ISBN : 9788119843718

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Artificial Intelligence And Data Analytics by Dr. A. Vijayalakshmi,Dr. Jayasudha M Pdf

"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 : A. Lakshmi Muddana
Publisher : Springer Nature
Page : 398 pages
File Size : 50,7 Mb
Release : 2024-06-18
Category : Electronic
ISBN : 9783031524738

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Python for Data Science by A. Lakshmi Muddana Pdf

Author : Longbing Cao
Publisher : Springer
Page : 390 pages
File Size : 44,5 Mb
Release : 2018-08-17
Category : Computers
ISBN : 9783319950921

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Data Science Thinking by Longbing Cao Pdf

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.

Author : VK Jain
Publisher : KHANNA PUBLISHING
Page : 600 pages
File Size : 51,5 Mb
Release : 2017-01-01
Category : Education
ISBN : 9789382609131

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Big Data and Hadoop by VK Jain Pdf

This book introduces you to the Big Data processing techniques addressing but not limited to various BI (business intelligence) requirements, such as reporting, batch analytics, online analytical processing (OLAP), data mining and Warehousing, and predictive analytics. The book has been written on IBMs Platform of Hadoop framework. IBM Infosphere BigInsight has the highest amount of tutorial matter available free of cost on Internet which makes it easy to acquire proficiency in this technique. This therefore becomes highly vunerable coaching materials in easy to learn steps. The book optimally provides the courseware as per MCA and M. Tech Level Syllabi of most of the Universities. All components of big Data Platform like Jaql, Hive Pig, Sqoop, Flume , Hadoop Streaming, Oozie: HBase, HDFS, FlumeNG, Whirr, Cloudera, Fuse , Zookeeper and Mahout: Machine learning for Hadoop has been discussed in sufficient Detail with hands on Exercises on each.

Author : Tarek Riman
Publisher : Cap.TaiM Marketing Inc.
Page : 245 pages
File Size : 48,8 Mb
Release : 2019-09-06
Category : Business & Economics
ISBN : 9781796616194

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The Secret to Capitalizing on Analytics by Tarek Riman Pdf

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 : Thomas H. Davenport,Jeanne G. Harris
Publisher : Harvard Business Review Press
Page : 961 pages
File Size : 51,8 Mb
Release : 2014-08-12
Category : Business & Economics
ISBN : 9781625277749

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Analytics and Big Data: The Davenport Collection (6 Items) by Thomas H. Davenport,Jeanne G. Harris Pdf

The Analytics and Big Data collection offers a “greatest hits” digital compilation of ideas from world-renowned thought leader Thomas Davenport, who helped popularize the terms analytics and big data in the workplace. An agile and prolific thinker, Davenport has written or coauthored more than a dozen bestselling books. Several of these titles are offered together for the first time in this curated digital bundle, including: Big Data at Work, Competing on Analytics, Analytics at Work, and Keeping Up with the Quants. The collection also includes Davenport’s popular Harvard Business Review articles, “Data Scientist: The Sexiest Job of the 21st Century” (2012) and “Analytics 3.0” (2013). Combined, these works cover all the bases on analytics and big data: what each term means; the ramifications of each from a technical, consumer, and management perspective; and where each can have the biggest impact on your business. Whether you’re an executive, a manager, or a student wanting to learn more, Analytics and Big Data is the most comprehensive collection you’ll find on the ever-growing phenomenon of digital data and analysis—and how you can make this rising business trend work for you. Named one of the ten “Masters of the New Economy” by CIO magazine, Thomas Davenport has helped hundreds of companies revitalize their management practices. He combines his interests in research, teaching, and business management as the President’s Distinguished Professor of Information Technology & Management at Babson College. Davenport has also taught at Harvard Business School, the University of Chicago, Dartmouth’s Tuck School of Business, and the University of Texas at Austin and has directed research centers at Accenture, McKinsey & Company, Ernst & Young, and CSC. He is also an independent Senior Advisor to Deloitte Analytics.

Author : National Academy of Engineering,National Academy of Sciences,Institute of Medicine,National Academy of Sciences, National Academy of Engineering, Institute of Medicine,Committee on Science, Engineering, and Public Policy
Publisher : National Academies Press
Page : 129 pages
File Size : 47,8 Mb
Release : 1999-04-18
Category : Political Science
ISBN : 9780309062916

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Capitalizing on Investments in Science and Technology by National Academy of Engineering,National Academy of Sciences,Institute of Medicine,National Academy of Sciences, National Academy of Engineering, Institute of Medicine,Committee on Science, Engineering, and Public Policy Pdf

Although the United States is currently capitalizing on its investment in science and technology effectively, there remains much room for improvement. This volume identifies the ingredients for success in capitalizing on such investments to produce national benefits, assesses current U.S. performance, and identifies future challenges. The book cites specific examples and examines several cross-cutting issues. It explores the possibility that the national research portfolio is losing diversity as a result of less long-term research in critical fields such as networking and materials. It also examines the implications of imbalances in the supply of and demand for science and engineering talent in emerging interdisciplinary fields such as bioinformatics.

Author : Committee on Science, Engineering, and Public Policy,National Academy of Sciences, National Academy of Engineering, Institute of Medicine,Institute of Medicine,National Academy of Sciences,National Academy of Engineering
Publisher : National Academies Press
Page : 129 pages
File Size : 50,9 Mb
Release : 1999-04-01
Category : Political Science
ISBN : 9780309524773

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Capitalizing on Investments in Science and Technology by Committee on Science, Engineering, and Public Policy,National Academy of Sciences, National Academy of Engineering, Institute of Medicine,Institute of Medicine,National Academy of Sciences,National Academy of Engineering Pdf

Although the United States is currently capitalizing on its investment in science and technology effectively, there remains much room for improvement. This volume identifies the ingredients for success in capitalizing on such investments to produce national benefits, assesses current U.S. performance, and identifies future challenges. The book cites specific examples and examines several cross-cutting issues. It explores the possibility that the national research portfolio is losing diversity as a result of less long-term research in critical fields such as networking and materials. It also examines the implications of imbalances in the supply of and demand for science and engineering talent in emerging interdisciplinary fields such as bioinformatics.

Author : Henry Etzkowitz,Andrew Webster,Peter Healey
Publisher : SUNY Press
Page : 304 pages
File Size : 48,9 Mb
Release : 1998-01-01
Category : Education
ISBN : 079143947X

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Capitalizing Knowledge by Henry Etzkowitz,Andrew Webster,Peter Healey Pdf

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

Author : Chikio Hayashi,Keiji Yajima,Hans H. Bock,Noboru Ohsumi,Yutaka Tanaka,Yasumasa Baba
Publisher : Springer Science & Business Media
Page : 780 pages
File Size : 44,5 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9784431659501

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Data Science, Classification, and Related Methods by Chikio Hayashi,Keiji Yajima,Hans H. Bock,Noboru Ohsumi,Yutaka Tanaka,Yasumasa Baba Pdf

This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.

Author : Mrs. Ch. V. Naga Sowjanya
Publisher : Academic Guru Publishing House
Page : 219 pages
File Size : 42,5 Mb
Release : 2024-04-04
Category : Study Aids
ISBN : 9788197195990

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Data Science: Exploring Future Trends by Mrs. Ch. V. Naga Sowjanya Pdf

"Data Science: Exploring Future Trends" is a forward-thinking look at the constantly changing area of data science and its future directions. This book, written by specialists in the field, provides a thorough overview of the developing trends, cutting-edge technology, and transformational applications that are driving the future of data science. "Data Science: Exploring Future Trends" guides readers through the ever-changing data science ecosystem. This book covers a broad variety of issues at the vanguard of data science innovation, including the fundamental concepts of artificial intelligence and machine learning, the transformative possibilities of quantum computing, and the ethical implications surrounding data-driven decision-making. Readers will understand the key trends shaping data science, including automation and AutoML, explainable AI and interpretability, data science's integration with healthcare, finance, and environmental sustainability, and edge computing and IoT integration's transformative impact. Whether you're a seasoned data scientist looking to stay ahead of the curve, a student or researcher interested in exploring the frontiers of data science, or a business leader looking to use data-driven insights for strategic decision-making, "Data Science: Exploring Future Trends" offers valuable insights and perspectives to navigate the ever-changing landscape of data science and unlock its full.

Author : Ivo D. Dinov,Milen Velchev Velev
Publisher : Walter de Gruyter GmbH & Co KG
Page : 489 pages
File Size : 52,7 Mb
Release : 2021-12-06
Category : Computers
ISBN : 9783110697827

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Data Science by Ivo D. Dinov,Milen Velchev Velev Pdf

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.

Author : Harvard Business Review
Publisher : Harvard Business Press
Page : 256 pages
File Size : 48,8 Mb
Release : 2018-03-13
Category : Business & Economics
ISBN : 9781633694293

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HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) by Harvard Business Review Pdf

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes

Author : Leondra R. Gonzalez,Aaren Stubberfield
Publisher : Packt Publishing Ltd
Page : 404 pages
File Size : 45,6 Mb
Release : 2024-02-29
Category : Computers
ISBN : 9781805120193

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Cracking the Data Science Interview by Leondra R. Gonzalez,Aaren Stubberfield Pdf

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.