If you want to learn about data science and big data, then keep reading... Two manuscripts in one book:
Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning
Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining
This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in.
There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today.
Some of the topics covered in part 1 of this book include:
What is Data Science?
What Exactly Does a Data Scientist Do?
A Look at What Data Analytics Is All About
What is Data Mining and How Does It Fit in with Data Science?
Regression Analysis
Why is Data Visualization So Important When It Comes to Understanding Your Data?
How to work with Database Querying
A Look at Artificial Intelligence
What is Machine Learning and How Is It Different from Artificial Intelligence?
What is the Future of Artificial Intelligence and Machine Learning?
And much more
Some of the topics covered in part 2 of this book include:
What is big data, and why is it important?
The five V's behind big data
How big data is already impacting your life, and where big data may be headed
How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things
How companies and governments are using predictive analytics to get ahead of the competition or improve service
How big data is used for fraud detection
How big data can train intelligent computer systems
The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics
Upcoming trends in big data that are sure to have a large impact on your future
Artificial intelligence, and how big data drives its development
What machine learning is and how it is tied to big data
The relationship between big data, data analytics, and business intelligence
Insights into how big data impacts privacy issues
The pros and cons regarding big data
And much, much more
So if you want to learn more about data science and big data, click the "add to cart" button
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This guidebook is going to provide you with all of the information that you need to learn more about data science, what this process is all about, and how you can use the Python language to put it all to work for you Even if you have no idea how to program or any idea of what to do with all of that data you have been collecting, this guidebook will give you all of the tools you need to be successful
There are a lot of different parts that come with data science and being able to put them all together can really help us to do better with helping our customers, finding new products to bring to market, and more. And with the help of this guidebook, we can hopefully find the best ways to beat out the competition and see the results that will work for us. It takes some time, and a good data analysis with the right algorithms from Python, but it can be one of the best ways to make some smart and sound decisions for your business.
Working with data science is becoming even more prevalent as the years go on, and businesses all over the world, and in many different industries, are using this to help them see more success. Whether you want to make predictions, provide better customer service, or learn other valuable insights about your business, data science with the help of Python, can make this happen. When you are ready to see what Python data science can do for your business, make sure to check out this guidebook to get started.
The process of Python data science is not an easy one and learning how to make this work for your needs, and to put all of the parts together can make a big difference in the way that you run your business, and how much success you will see when it comes to your business growing in the future. When you are ready to learn more about working with Python data science and how to make this work for your business, make sure to check out this guidebook to get started.
There are so many parts that come with a data science project, and we are going to take some time to discuss them all in this guidebook. We are going to look at some of the basics that come with this data science project, and why it is so beneficial to so many companies to at least check it out and see what it has to offer them. At the same time, we are also going to explore how to set up your own environment to get started with data science, and some of the best libraries that are out there to help us succeed with the use of data science and Python put together.
This book covers:
What Is Data Science?
How Can I Use Data Science?
The Best Python Libraries for Data Science
Setting Up Your Virtual Environments for Data Science
The Importance of the NumPy Arrays
Gathering and Collecting Your Data
Loading and Preparing Your Dataset
Data Mining
Completing the Data Analysis
How Machine Learning Can Help
How to Work with Data Visualization
Many businesses are able to benefit when they work with data analysis for some of their own needs. It will help them to learn more about their customers, their industry, and so much more. When you are ready to learn more about what data science can do for you and to figure out whether this is a process your business should spend some time on, make sure to check out this guidebook to help you get started.
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This Handbook will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option.
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Have a Clear and Exhaustive Explanation About Data Analysis and Why It Is So Important Today in The Business World; organizations of all sizes rely on the insights they extract from the data they have to measure progress, make informed decisions, plan for the future, and so on. Data scientists are the people who process and organize the data with scientific methods, algorithms, and other techniques.
Understand Why Python is Preferred to Use For Data Analysis Over Other Tools and the reasons why all the benefits of using Python made it the best tool to learn data science.
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Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 46470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Kalita Название: Recent Developments in Machine Learning and Data Analytics ISBN: 9811312796 ISBN-13(EAN): 9789811312793 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents high-quality papers from an international forum for research on computational approaches to learning. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted.
Автор: Ulf Brefeld; Jesse Davis; Jan Van Haaren; Albrecht Название: Machine Learning and Data Mining for Sports Analytics ISBN: 3030172732 ISBN-13(EAN): 9783030172732 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018, colocated with ECML/PKDD 2018, in Dublin, Ireland, in September 2018.
The 12 full papers presented together with 4 challenge papers were carefully reviewed and selected from 24 submissions. The papers present a variety of topics, covering the team sports American football, basketball, ice hockey, and soccer, as well as the individual sports cycling and martial arts. In addition, four challenge papers are included, reporting on how to predict pass receivers in soccer.
Автор: V. Sathiyamoorthi, Atilla Elci Название: Challenges and Applications of Data Analytics in Social Perspectives ISBN: 1799825671 ISBN-13(EAN): 9781799825678 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 196810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The publication examines topics that include collaborative filtering, data visualization, and edge computing.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 180180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 239310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: O`Neil Bonnie K., Fryman Lowell Название: The Data Catalog: Sherlock Holmes Data Sleuthing for Analytics ISBN: 1634627873 ISBN-13(EAN): 9781634627870 Издательство: Gazelle Book Services Рейтинг: Цена: 57190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Apply this definitive guide to data catalogs and select the feature set needed to empower your data citizens in their quest for faster time to insight. The data catalog may be the most important breakthrough in data management in the last decade, ranking alongside the advent of the data warehouse. The latter enabled business consumers to conduct their own analyses to obtain insights themselves. The data catalog is the next wave of this, empowering business users even further to drastically reduce time to insight, despite the rising tide of data flooding the enterprise. Use this book as a guide to provide a broad overview of the most popular Machine Learning (ML) data catalog products, and perform due diligence using the extensive features list. Consider graphical user interface (GUI) design issues such as layout and navigation, as well as scalability in terms of how the catalog will handle your current and anticipated data and metadata needs. ONeil & Frymanpresent a typology which ranges from products that focus on data lineage, curation and search, data governance, data preparation, and of course, the core capability of finding and understanding the data. The authors emphasize that machine learning is being adopted in many of these products, enabling a more elegant data democratization solution in the face of the burgeoning mountain of data that is engulfing organizations. Derek Strauss, Chairman/CEO, Gavroshe, and Former CDO, TD Ameritrade. This book is organized into three sections: Chapters 1 and 2 reveal the rationale for a data catalog and share how data scientists, data administrators, and curators fare with and without a data catalog; Chapters 3-10 present the many different types of data catalogs; Chapters 11 and 12 provide an extensive features list, current trends, and visions for the future.
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