Search results for: learn-data-mining-through-excel

Learn Data Mining Through Excel

Author : Hong Zhou
File Size : 32.48 MB
Format : PDF, Docs
Download : 396
Read : 1187
Download »
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

Learning Excel Data Mining

Author :
File Size : 32.8 MB
Format : PDF, ePub
Download : 747
Read : 520
Download »
Learn how to use Excel and Excel SQL Server Analysis Services to perform basic data mining and analysis.

Learn By Examples A Quick Guide to Data Mining with RapidMiner and Weka

Author : Eric Goh
File Size : 30.32 MB
Format : PDF, ePub
Download : 280
Read : 1204
Download »
This book aim to equip the reader with RaidMiner and Weka and Data Mining basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) using Weka and RapidMiner. Content Covered: - Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) - Getting Started (INstall Weka and RapidMiner) - Prediction and Classification (Prediction and Classification) - Machine Learning Basics (Kmeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) - Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) - Data Mining with RapidMiner (Data Understanding using RapidMiner, Data Preparation using RapidMiner, Model Building and Evaluation using RapidMiner) - Conclusion We will be using opensource tools, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into Weka and RapidMiner, with a touch on data mining. This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/data-mining-with-rapidminer/?couponCode=EBOOKSPECIAL https://www.udemy.com/learn-machine-learning-with-weka/?couponCode=EBOOKSPECIAL

Neuronale Netze Selbst Programmieren

Author : Tariq Rashid
File Size : 84.10 MB
Format : PDF, ePub, Docs
Download : 823
Read : 1242
Download »
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Data Mining for Business Intelligence

Author : Galit Shmueli
File Size : 90.82 MB
Format : PDF, Mobi
Download : 778
Read : 803
Download »
Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models. Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding. Data Mining for Business Intelligence: Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis Features a business decision-making context for these key methods Illustrates the application and interpretation of these methods using real business cases and data This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

A Quick Guide to Data Mining with Weka and Java using Weka

Author : Eric Goh
File Size : 78.26 MB
Format : PDF, ePub, Docs
Download : 390
Read : 1112
Download »
This technical book aim to equip the reader with Weka, Data Mining in a fast and practical way. There will be many examples and explanations that are straight to the point. Contents 1. Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) 2. Getting Started (INstall Weka) 3. Prediction and Classification (Prediction and Classification) 4. Machine Learning Basics (KMeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) 5. Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) 6. Java interact Weka (Use Java to use Weka, in order to develop your own prediction or classification system) 7. Conclusion This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/machine-learning-with-java-and-weka/?couponCode=SPECIALCOUPON

Hands On Machine Learning with Microsoft Excel 2019

Author : Julio Cesar Rodriguez Martino
File Size : 60.67 MB
Format : PDF, ePub
Download : 975
Read : 1119
Download »
A practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis. Key Features Use Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book Description We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning. What you will learn Use Excel to preview and cleanse datasets Understand correlations between variables and optimize the input to machine learning models Use and evaluate different machine learning models from Excel Understand the use of different visualizations Learn the basic concepts and calculations to understand how artificial neural networks work Learn how to connect Excel to the Microsoft Azure cloud Get beyond proof of concepts and build fully functional data analysis flows Who this book is for This book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.

Data Analysis Using SQL and Excel

Author : Gordon S. Linoff
File Size : 54.5 MB
Format : PDF, ePub, Docs
Download : 346
Read : 290
Download »
A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. Understand core analytic techniques that work with SQL and Excel Ensure your analytic approach gets you the results you need Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

Data Warehousing and Mining

Author : ITLESL
File Size : 33.87 MB
Format : PDF, ePub, Docs
Download : 866
Read : 1122
Download »
Data Warehousing and Data Mining is presented in a question-and-answer format following the examination pattern and covers all key topics in the syllabus. The book is designed to make learning fast and effective and is precise, up-to-date and will help students excel in their examinations. The book is part of the Express Learning is a series of books designed as quick reference guides to important undergraduate courses. The organized and accessible format of these books allows students to learn important concepts in an easy-to-understand, question-and-answer format. These portable learning tools have been designed as one-stop references for students to understand and master the subjects by themselves.

Data Mining for Business Analytics

Author : Galit Shmueli
File Size : 49.9 MB
Format : PDF, ePub, Mobi
Download : 334
Read : 222
Download »
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

Learning Data Mining with Python

Author : Robert Layton
File Size : 74.91 MB
Format : PDF
Download : 511
Read : 565
Download »
Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner.

Data Mining with Microsoft SQL Server 2008

Author : Jamie MacLennan
File Size : 82.83 MB
Format : PDF, ePub
Download : 965
Read : 859
Download »
Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.

Data Mining with SQL Server 2005

Author : ZhaoHui Tang
File Size : 50.43 MB
Format : PDF, Docs
Download : 176
Read : 1061
Download »
Your in-depth guide to using the new Microsoft data mining standard to solve today's business problems Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects. You'll learn: The principal concepts of data mining How to work with the data mining algorithms included in SQL Server data mining How to use DMX-the data mining query language The XML for Analysis API The architecture of the SQL Server 2005 data mining component How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms How to implement a data mining project using SQL Server Integration Services How to mine an OLAP cube How to build an online retail site with cross-selling features How to access SQL Server 2005 data mining features programmatically

Data Mining

Author : Richard J. Roiger
File Size : 23.11 MB
Format : PDF, ePub
Download : 548
Read : 924
Download »
Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

Data Mining Techniques

Author : Gordon S. Linoff
File Size : 32.45 MB
Format : PDF, Mobi
Download : 774
Read : 232
Download »
Berry's name appears first on the 2nd ed.

Data Mining

Author : Robert Groth
File Size : 63.16 MB
Format : PDF
Download : 871
Read : 1149
Download »
This book contains all the practical information, hands-on demos and software you need to understand data mining.This book doesn't just explain data mining concepts: it shows you exactly how to make the most of them. If you're in marketing, you'll learn how data mining can help you rank your customers by the likelihood they'll respond to your mailings. If you're in MIS, you'll learn exactly how to prepare relational data for data mining. You'll learn how to use each of three powerful data mining tools; demos for all three are included on CD-ROM. The book also includes detailed case studies for several of the industries that can benefit most from data mining, including banking, finance, retail, healthcare, direct marketing, and telecommunications. The book is replete with shortcuts and techniques that have never been published before.For all business and marketing professionals, systems analysts, database administrators, students and others who want to leverage the power of data mining.

Learn By Examples Introduction to Data and Text Mining using DSTK3

Author : Eric M. H. Goh
File Size : 45.81 MB
Format : PDF, ePub, Docs
Download : 351
Read : 1227
Download »
DSTK - Data Science Toolkit 3 is a set of data and text mining softwares, following the CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK 3 contains DSTK Engine as interpreter, DSTK ScriptWriter as a simple IDE, DSTK Studio providing a SPSS Statistics like easy to use interface with DSTK Engine, and DSTK Text Explorer provides a GUI for Text Mining. DSTK Studio and DSTK Text Explorer, however, need a small payment of 59 usd to support our development. DSTK Engine and DSTK ScriptWriter are free. This book is going to be an easy guide to get started using DSTK 3 softwares for data and text mining. IntroductionGetting StartedDSTK ScriptWriter EssentialsDSTK Studio EssentialsDSTK Text Explorer EssentialsConclusion

INFORMS Conference Program

Author : Institute for Operations Research and the Management Sciences. National Meeting
File Size : 73.89 MB
Format : PDF, Mobi
Download : 678
Read : 944
Download »

Journal of Database Management

Author :
File Size : 36.31 MB
Format : PDF, Kindle
Download : 205
Read : 350
Download »

Data Science and Machine Learning Interview Questions Using R

Author : Vishwanathan Narayanan
File Size : 28.32 MB
Format : PDF, Docs
Download : 229
Read : 922
Download »
Get answers to frequently asked questions on Data Science and Machine Learning using R KEY FEATURES - Understand the capabilities of the R programming language - Most of the machine learning algorithms and their R implementation covered in depth - Answers on conceptual data science concepts are also covered DESCRIPTION This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language. The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. WHAT WILL YOU LEARN - Get answers to the basics, intermediate and advanced questions on R programming - Understand the transformation and filtering capabilities of R - Know how to perform visualization using R WHO THIS BOOK IS FOR This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. TABLE OF CONTENTS 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet