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Databases Data Science And Data Analytics Fundamentals
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Book Synopsis Databases, Data Science, and Data Analytics Fundamentals by : Johnathan Mun
Download or read book Databases, Data Science, and Data Analytics Fundamentals written by Johnathan Mun and published by . This book was released on 2020-04-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Slides for the course covering the fundamentals of databases. The material covered includes Basic SQL, IBM Cloud DB2, Microsoft Access, Microsoft Excel, Python, QDM SQL, Risk Simulator, ROV BizStats.
Book Synopsis Fundamentals of Data Science by : Sanjeev J. Wagh
Download or read book Fundamentals of Data Science written by Sanjeev J. Wagh and published by CRC Press. This book was released on 2021-09-26 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.
Book Synopsis Data Science Fundamentals and Practical Approaches by : Dr. Gypsy Nandi
Download or read book Data Science Fundamentals and Practical Approaches written by Dr. Gypsy Nandi and published by BPB Publications. This book was released on 2020-06-02 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics
Book Synopsis R for Data Science by : Hadley Wickham
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Book Synopsis Data Science from Scratch by : Joel Grus
Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Book Synopsis SQL for Data Science by : Antonio Badia
Download or read book SQL for Data Science written by Antonio Badia and published by Springer Nature. This book was released on 2020-11-09 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
Book Synopsis Data Analytics Basics by : Simplilearn
Download or read book Data Analytics Basics written by Simplilearn and published by IndraStra Whitepapers. This book was released on 2020-12-14 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics is increasingly becoming a key element in shaping a company’s business strategy. Today, data influences every decision made by an organization, and this is driving the wide-scale adoption of data analytics, including machine learning technologies and artificial intelligence solutions. The heightened focus is propelling a surge in data analytics spending, reflected in various studies conducted by leading market research firms. The field of data analytics offers some amazing salaries and is not only the hottest IT job, but it is also one of the best-paying jobs in the world. This guide aims at providing the readers with everything they need to know about the data analytics field, basic terminologies, key concepts, real-life use cases, skills you must master in order to scale up your career, and training and certifications you might need to reach your dream job.
Book Synopsis Data Science Fundamentals Part 1 by : Jonathan Dinu
Download or read book Data Science Fundamentals Part 1 written by Jonathan Dinu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air...
Book Synopsis Data Analytics by : Juan J. Cuadrado-Gallego
Download or read book Data Analytics written by Juan J. Cuadrado-Gallego and published by Springer Nature. This book was released on 2023-11-30 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools. Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.
Book Synopsis Fundamentals of Data Analytics by : Rudolf Mathar
Download or read book Fundamentals of Data Analytics written by Rudolf Mathar and published by Springer Nature. This book was released on 2020-09-15 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
Book Synopsis Databases for Small Business by : Anna Manning
Download or read book Databases for Small Business written by Anna Manning and published by Apress. This book was released on 2015-11-21 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the practical aspects of database design, data cleansing, data analysis, and data protection, among others. The focus is on what you really need to know to create the right database for your small business and to leverage it most effectively to spur growth and revenue. Databases for Small Business is a practical handbook for entrepreneurs, managers, staff, and professionals in small organizations who are not IT specialists but who recognize the need to ramp up their small organizations’ use of data and to round out their own business expertise and office skills with basic database proficiency. Anna Manning—a data scientist who has worked on database design and data analysis in a computer science university research lab, her own small business, and a nonprofit—walks you through the progression of steps that will enable you to extract actionable intelligence and maximum value from your business data in terms of marketing, sales, customer relations, decision making, and business strategy. Dr. Manning illustrates the steps in the book with four running case studies of a small online business, an engineering startup, a small legal firm, and a nonprofit organization. Databases for Small Business teaches non-techie entrepreneurs and professionals how to: Design a small business database from scratch Extract the maximum profit from your data Follow guidance on data protection law Effectively use data collection and data cleansing techniques Train staff to leverage your data
Download or read book Data Analytics written by Scott Harvey and published by Createspace Independent Publishing Platform. This book was released on 2017-04-04 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Read for FREE with Kindle Unlimited! Data Analytics: Essentials to master Data Analytics Choose to understand Data Analytics! If you want to learn about data analytics, statistical informations, and how to leverage your business get your copy today. Take it now! The information in the world doubles every 20 months. Important data sources are business and industrial processes, text and structured databases, images and videos, and physical and biomedical data. Data analytics allows finding relevant information, structures, and patterns, to gain new insights, to identify causes and effects, to predict future developments, or to suggest optimal decisions. We need models and algorithms to collect, preprocess, analyze, and evaluate data, from various fields such as statistics, machine learning, pattern recognition, system theory, operations research, or artificial intelligence. With this book, you will learn about the most important methods and algorithms for data analytics. You will be able to choose appropriate methods for specific tasks and apply these in your own data analytics projects. You will understand the basic concepts of the growing field of data analytics, which will allow you to keep pace and to actively contribute to the advancement of the field. Here Is A Preview Of What You'll Learn... What are data analytics How important is big data in your business The advantage getting a better understanding of data management Examples and practical methods to implement Much, much more! Check Out What Others Are Saying... "I'm passionate about business and understanding analytics and how deep things can operate.This book really helped in my search of understanding! Thank you! " Conor Grunion
Book Synopsis Fundamental Of Data Science And Big Data Analytics by : N. Narayanan Prasanth
Download or read book Fundamental Of Data Science And Big Data Analytics written by N. Narayanan Prasanth and published by Academic Guru Publishing House. This book was released on 2023-11-29 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a thorough, accessible, and current comprehension of Big Data for both business people and engineers. This book presents essential ideas, theories, terminology, and technologies related to Big Data. It also covers important analysis and analytics approaches. The information is rationally organized, given in clear and simple language, and backed with easily comprehensible examples. The objective of “Fundamentals of Data Science and Big Data Science” is to enhance decision-making by analyzing data. Currently, data science plays a crucial role in determining the advertisements that appear on the internet, the recommendations you get for books and films, the classification of emails into your spam folders, as well as the pricing of health insurance. This book provides a brief description of the developing discipline of data science, elucidating its progression, present applications, data infrastructure concerns, and legal issues. The text adopts a conversational tone and stays clear of complex mathematical ideas often associated with data science, instead focusing on straightforward explanations and real-world use cases. Upon concluding the book, readers will have acquired proficiency in controlling data, using data in the context of business challenges, and implementing optimal methodologies for data analysis. This book functions as a practical guide for Science/Engineering/MBA students, including both undergraduate and graduate students, who have an interest in the field of Data Science.
Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Book Synopsis Introduction to Data Science by : Rafael A. Irizarry
Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Book Synopsis Foundations of Data Science by : Avrim Blum
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Book Synopsis SQL for Data Scientists by : Renee M. P. Teate
Download or read book SQL for Data Scientists written by Renee M. P. Teate and published by John Wiley & Sons. This book was released on 2021-08-17 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!
