Access Data Analysis Cookbook

Author: Ken Bluttman
Publisher: "O'Reilly Media, Inc."
ISBN: 9780596555214
Size: 60.18 MB
Format: PDF, Kindle
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If you have large quantities of data in a Microsoft Access database, and need to study that data in depth, this book is a data cruncher's dream. Access Data Analysis Cookbook offers practical recipes to solve a variety of common problems that users have with extracting Access data and performing calculations on it. Each recipe includes a discussion on how and why the solution works. Whether you use Access 2007 or an earlier version, this book will teach you new methods to query data, different ways to move data in and out of Access, how to calculate answers to financial and investment issues, and more. Learn how to apply statistics to summarize business information, how to jump beyond SQL by manipulating data with VBA, how to process dates and times, and even how to reach into the Excel data analysis toolkit. Recipes demonstrate ways to: Develop basic and sophisticated queries Apply aggregate functions, custom functions, regular expressions, and crosstabs Apply queries to perform non-passive activities such as inserting, updating, and deleting data Create and manipulate tables and queries programmatically Manage text-based data, including methods to isolate parts of a string and ways to work with numbers that are stored as text Use arrays, read and write to the Windows registry, encrypt data, and use transaction processing Use the FileSystemObject, use XML with XSLT, communicate with SQL Server, and exchange data with other Office products Find answers from time-based data, such as how to add time, count elapsed time, work with leap years, and how to manage time zones in your calculations Deal with business and finance problems, including methods for calculating depreciation, loan paybacks, and Return on Investment (ROI) Explore statistical techniques, such as frequency, variance, kurtosis, linear regression, combinations and permutations Access Data Analysis Cookbook is a one-stop-shop for extracting nuggets of valuable information from your database, and anyone with Access experience will benefit from these tips and techniques, including seasoned developers. If you want to use your data, and not just store it, you'll find this guide indispensable.

Microsoft Access 2007 Data Analysis

Author: Michael Alexander
Publisher: John Wiley & Sons
ISBN: 1118079183
Size: 50.15 MB
Format: PDF, ePub, Mobi
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Chart a course for more effective data analysis with Access 2007. With this resource, you’ll learn how Access 2007 offers powerful functionality that may be better suited to your data analysis needs. Learn to analyze large amounts of data in meaningful ways, quickly and easily slice it into various views, automate redundant analysis, and save time—all using Access. If you know a bit about table structures and formulas as well as data analysis, start thinking outside the chart.

Data Analysis With Microsoft Access 2010 From Simple Queries To Business Intelligence

Author: Larry Rockoff
Publisher: Cengage Learning
ISBN: 9781435460102
Size: 24.49 MB
Format: PDF, ePub
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DATA ANALYSIS WITH MICROSOFT ACCESS 2010 is an introduction to Access with an emphasis on topics relevant to data analysis. The goal is to help the analyst gain a true understanding of data and the information it contains. Access queries are covered in detail, both in terms of the mechanics of their design, and how they can be used for typical data analysis tasks. The book is written in an easy-to-understand tutorial style, with new topics introduced in a logical and intuitive sequence. Numerous screenshots are included, so you won’t need to sit with a computer as you read the book. The author also broadens the concept of data analysis to encompass business intelligence (BI) topics, including valuable material on how to use Access and Excel pivot tables. Additional features include See the SQL sidebars that allow interested readers to learn SQL as they are learning Access, and Focus on Analysis sidebars that provide details on a number of useful quantitative topics. A companion website has a sample database that correlates with the BI material in the book. In short, this is the only book you’ll need to gain a working knowledge of Access, and how it can be used for data analysis. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Sql For Microsoft Access

Author: Cecelia L. Allison
Publisher: Jones & Bartlett Learning
ISBN: 144963155X
Size: 16.30 MB
Format: PDF, Kindle
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SQL for Microsoft Access (2nd Edition) provides a guide to getting the most out of Microsoft Access through the use of Structured Query Language. Step-by-step examples demonstrate how to use SQL script to create tables, add records to tables, and retrieve and manage records. Readers will also learn about calculated fields, Access projects, and the integration of SQL script in VBA and ASP code. Explore the relational database structure and the basics of SQL. Understand how table joins, unions, and subqueries are used to retrieve records from multiple tables simultaneously. Learn how to filter records and group data. Discover how to create parameter queries that prompt users for data. Test your knowledge and comprehension with the end-of-chapter quizzes and projects.

Python For Data Analysis

Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Size: 62.70 MB
Format: PDF, Docs
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Big Data For Dummies

Author: Judith Hurwitz
Publisher: John Wiley & Sons
ISBN: 1118644174
Size: 35.30 MB
Format: PDF, Docs
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Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Marketing Analytics

Author: Wayne L. Winston
Publisher: John Wiley & Sons
ISBN: 1118417305
Size: 21.82 MB
Format: PDF, Mobi
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Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.

Predictive Analytics And Data Mining

Author: Vijay Kotu
Publisher: Morgan Kaufmann
ISBN: 0128016507
Size: 10.53 MB
Format: PDF, ePub
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Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You’ll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples

R Cookbook

Author: Paul Teetor
Publisher: "O'Reilly Media, Inc."
ISBN: 1449307264
Size: 71.32 MB
Format: PDF, ePub, Docs
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With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

Bioinformatics Data Skills

Author: Vince Buffalo
Publisher: "O'Reilly Media, Inc."
ISBN: 1449367518
Size: 41.27 MB
Format: PDF, ePub, Docs
View: 5921
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Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles