Practical Data Analysis
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Practical Data Analysis
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Author : Peter G. Bryant
language : en
Publisher: McGraw-Hill/Irwin
Release Date : 1995
Practical Data Analysis written by Peter G. Bryant and has been published by McGraw-Hill/Irwin this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Business & Economics categories.
Practical Data Analysis
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Author : Hector Cuesta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2013-10-22
Practical Data Analysis written by Hector Cuesta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-22 with Computers categories.
Each chapter of the book quickly introduces a key ‘theme’ of Data Analysis, before immersing you in the practical aspects of each theme. You’ll learn quickly how to perform all aspects of Data Analysis.Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.
Practical Data Analysis
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Author : Hector Cuesta
language : en
Publisher: CreateSpace
Release Date : 2015-05-09
Practical Data Analysis written by Hector Cuesta and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-09 with categories.
For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that. Overview Explore how to analyze your data in various innovative ways and turn them into insight Learn to use the D3.js visualization tool for exploratory data analysis Understand how to work with graphs and social data analysis Discover how to perform advanced query techniques and run MapReduce on MongoDB In Detail Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle. Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered. Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight. What you will learn from this book Work with data to get meaningful results from your data analysis projects Visualize your data to find trends and correlations Build your own image similarity search engine Learn how to forecast numerical values from time series data Create an interactive visualization for your social media graph Explore the MapReduce framework in MongoDB Create interactive simulations with D3js Approach Practical Data Analysis is a practical, step-by-step guide to empower small businesses to manage and analyze your data and extract valuable information from the data Who this book is written for This book is for developers, small business users, and analysts who want to implement data analysis and visualization for their company in a practical way. You need no prior experience with data analysis or data processing; however, basic knowledge of programming, statistics, and linear algebra is assumed.
Practical Data Analysis
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Author : Dhiraj Bhuyan
language : en
Publisher: Dhiraj Bhuyan
Release Date : 2019-11-30
Practical Data Analysis written by Dhiraj Bhuyan and has been published by Dhiraj Bhuyan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Computers categories.
“Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com
Practical Data Analysis With Jmp Third Edition
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Author : Robert Carver
language : en
Publisher:
Release Date : 2019-10-14
Practical Data Analysis With Jmp Third Edition written by Robert Carver and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
Master the concepts and techniques of statistical analysis using JMP(R) Practical Data Analysis with JMP(R), Third Edition, highlights the powerful interactive and visual approach of JMP to introduce readers to statistical thinking and data analysis. It helps you choose the best technique for the problem at hand by using real-world cases. It also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings. The book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. It includes varied examples and problems using real sets of data. Each chapter typically starts with an important or interesting research question that an investigator has pursued. Reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples. Application Scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples. New in the third edition, chapters have been updated to demonstrate the enhanced capabilities of JMP, including projects, Graph Builder, Query Builder, and Formula Depot.
Practical Data Analysis With Jmp
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Author : Robert Carver
language : en
Publisher: SAS Press
Release Date : 2010
Practical Data Analysis With Jmp written by Robert Carver and has been published by SAS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with JMP (Computer file) categories.
"Practical Data Analysis with JMP" uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. The book can stand on its own or be used to supplement a standard introduction-to-statistics textbook.
Data Analysis For Social Science
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Author : Elena Llaudet
language : en
Publisher: Princeton University Press
Release Date : 2022-09-13
Data Analysis For Social Science written by Elena Llaudet and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-13 with Social Science categories.
An ideal textbook for complete beginners—teaches from scratch R, statistics, and the fundamentals of quantitative social science Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses’ strengths and limitations. Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population. Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters. Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer. Assumes no prior knowledge of statistics or coding. Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose. Provides cheatsheets of statistical concepts and R code. Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Practical Data Analysis Using Jupyter Notebook
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Author : Marc Wintjen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-19
Practical Data Analysis Using Jupyter Notebook written by Marc Wintjen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-19 with Computers categories.
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Practical Data Analysis In Chemistry
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Author : Marcel Maeder
language : en
Publisher: Elsevier
Release Date : 2007-08-10
Practical Data Analysis In Chemistry written by Marcel Maeder and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-10 with Mathematics categories.
The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses.* Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered
Data Analytics
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Author : Owen Kriev
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-08-11
Data Analytics written by Owen Kriev and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-11 with Big data categories.
Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data Analytics The Comprehensive Beginner's Guide to Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business using the power of Data Analytics Is a book targeted for anyone interested in wanting a Comprehensive learning experience with data analytics, Furthermore, for people who want to improve the sales and revenue for their business, but needs a guide regarding Data Analytics, Then this book is perfect for you! Discover everything you need to know about with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data Analytics and let it a skill that you have With this book, you will improve your Date analytics skills: Overview of Date analytics. Application of Date analytics. Foundation of Date analytics. Mathematics and Analytics. Analysis and Analytics. Communicating Data Insights. Automated Data Services. Gathering data. Charts and Graphs. Measures of Central Tendency. Applying Data Analytics to Business and Industry. Importance of Business Intelligence. The Goals of Business Intelligence. How data analyst benefits from BI. And much more! Buy this book NOW to Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data Analytics