Extending Excel With Python And R

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Extending Excel with Python and R

Author : Steven Sanderson
Publisher : Packt Publishing Ltd
ISBN 13 : 1804615544
Total Pages : 345 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Extending Excel with Python and R by : Steven Sanderson

Download or read book Extending Excel with Python and R written by Steven Sanderson and published by Packt Publishing Ltd. This book was released on 2024-04-30 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionFor businesses, data analysis and visualization are crucial for informed decision-making; however, Excel’s limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. Whether you're a beginner or an expert, this book has everything you need to unlock Excel's full potential and take your data analysis skills to the next level. By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed.What you will learn Read and write Excel files with R and Python libraries Automate Excel tasks with R and Python scripts Use R and Python to execute Excel VBA macros Format Excel sheets using R and Python packages Create graphs with ggplot2 and Matplotlib in Excel Analyze Excel data with statistical methods and time series analysis Explore various methods to call R and Python functions from Excel Who this book is for If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. It provides a comprehensive introduction to the topics covered, making it suitable for both beginners and intermediate learners. A basic understanding of Excel, Python, and R is all you need to get started.

Extending Power BI with Python and R

Author : Luca Zavarella
Publisher : Packt Publishing Ltd
ISBN 13 : 1801076677
Total Pages : 559 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis Extending Power BI with Python and R by : Luca Zavarella

Download or read book Extending Power BI with Python and R written by Luca Zavarella and published by Packt Publishing Ltd. This book was released on 2021-11-26 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform more advanced analysis and manipulation of your data beyond what Power BI can do to unlock valuable insights using Python and R Key FeaturesGet the most out of Python and R with Power BI by implementing non-trivial codeLeverage the toolset of Python and R chunks to inject scripts into your Power BI dashboardsImplement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BIBook Description Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you'll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R. What you will learnDiscover best practices for using Python and R in Power BI productsUse Python and R to perform complex data manipulations in Power BIApply data anonymization and data pseudonymization in Power BILog data and load large datasets in Power BI using Python and REnrich your Power BI dashboards using external APIs and machine learning modelsExtract insights from your data using linear optimization and other algorithmsHandle outliers and missing values for multivariate and time-series dataCreate any visualization, as complex as you want, using R scriptsWho this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

Extending Power BI with Python and R

Author : Luca Zavarella
Publisher : Packt Publishing Ltd
ISBN 13 : 1837635862
Total Pages : 815 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Extending Power BI with Python and R by : Luca Zavarella

Download or read book Extending Power BI with Python and R written by Luca Zavarella and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Discover best practices for using Python and R in Power BI by implementing non-trivial code Enrich your Power BI dashboards using external APIs and machine learning models Create any visualization, as complex as you want, using Python and R scripts Book DescriptionThe latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.What you will learn Configure optimal integration of Python and R with Power BI Perform complex data manipulations not possible by default in Power BI Boost Power BI logging and loading large datasets Extract insights from your data using algorithms like linear optimization Calculate string distances and learn how to use them for probabilistic fuzzy matching Handle outliers and missing values for multivariate and time-series data Apply Exploratory Data Analysis in Power BI with R Learn to use Grammar of Graphics in Python Who this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

Essentials of Excel VBA, Python, and R

Author : John Lee
Publisher : Springer Nature
ISBN 13 : 3031142365
Total Pages : 698 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Essentials of Excel VBA, Python, and R by : John Lee

Download or read book Essentials of Excel VBA, Python, and R written by John Lee and published by Springer Nature. This book was released on 2023-01-02 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the second volume for dedicated content on financial derivatives, risk management, and machine learning.

Advancing Into Analytics

Author : George Mount
Publisher : O'Reilly Media
ISBN 13 : 9781492094340
Total Pages : 250 pages
Book Rating : 4.0/5 (943 download)

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Book Synopsis Advancing Into Analytics by : George Mount

Download or read book Advancing Into Analytics written by George Mount and published by O'Reilly Media. This book was released on 2021-04-30 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics may seem daunting, but if you're familiar with Excel, you have a head start that can help you make the leap into analytics. Advancing into Analytics will lower your learning curve. Author George Mount, founder and CEO of Stringfest Analytics, clearly and gently guides intermediate Excel users to a solid understanding of analytics and the data stack. This book demonstrates key statistical concepts from spreadsheets and pivots your existing knowledge about data manipulation into R and Python programming. With this practical book at your side, you'll learn how to: Explore a dataset for potential research questions to check assumptions and to build hypotheses Make compelling business recommendations using inferential statistics Load, view, and write datasets using R and Python Perform common data wrangling tasks such as sorting, filtering, and aggregating using R and Python Navigate and execute code in Jupyter notebooks Identify, install, and implement the most useful open source packages for your needs And more

Essentials of Excel VBA, Python, and R

Author : John Lee
Publisher : Springer Nature
ISBN 13 : 3031142837
Total Pages : 521 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Essentials of Excel VBA, Python, and R by : John Lee

Download or read book Essentials of Excel VBA, Python, and R written by John Lee and published by Springer Nature. This book was released on 2023-03-23 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

Advanced Analytics in Power BI with R and Python

Author : Ryan Wade
Publisher : Apress
ISBN 13 : 9781484258286
Total Pages : 330 pages
Book Rating : 4.2/5 (582 download)

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Book Synopsis Advanced Analytics in Power BI with R and Python by : Ryan Wade

Download or read book Advanced Analytics in Power BI with R and Python written by Ryan Wade and published by Apress. This book was released on 2020-09-05 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. What You Will Learn Create advanced data visualizations through R using the ggplot2 package Ingest data using R and Python to overcome the limitations of Power Query Apply machine learning models to your data using R and Python Incorporate advanced AI in Power BI via Microsoft Cognitive Services, IBM Watson, and pre-trained models in SQL Server Machine Learning Services Perform string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more succinct way

Extending R

Author : John M. Chambers
Publisher : CRC Press
ISBN 13 : 1315349876
Total Pages : 428 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Extending R by : John M. Chambers

Download or read book Extending R written by John M. Chambers and published by CRC Press. This book was released on 2017-12-19 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Up-to-Date Guidance from One of the Foremost Members of the R Core Team Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R. The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R’s data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages. A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. A 2017 Choice Outstanding Academic Title

Python Lab1 Excel Openpyxl

Author : R. Zimmerman
Publisher :
ISBN 13 : 9781711078229
Total Pages : 84 pages
Book Rating : 4.0/5 (782 download)

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Book Synopsis Python Lab1 Excel Openpyxl by : R. Zimmerman

Download or read book Python Lab1 Excel Openpyxl written by R. Zimmerman and published by . This book was released on 2019-11-24 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: #1 New Release Black and White Edition. Are you curious about the Python language and wondering how to read and write Excel files? This book is a hands-on lab with simple code examples that perform one basic task: compare two Excel files and output an Excel file of differences. At the end of the lab, you will know enough about Python to work with your own Excel files, even if you're new to Python or programming. My examples use the free Anaconda data science platform Python 3.7, running on a Windows computer, utilizing the Spyder application. The step-by-step examples walk through each line of code, with screenshots of the corresponding Excel files so you can follow along as the program moves through the code. In the course of the lab, you'll learn these Python concepts. 1. What is a Library? 2. Comments 3. Strings, Types, and Variables 4. If...else statements for comparing data 5. While loops for working with rows of Excel data 6. Working with the file system (files/directories) 7. Creating functions and importing them into your main code file 8. Working with Excel files using openpyxlThe lab has two parts. Part 1 accomplishes the basic tasks to compare the two Excel files. I think of this as the core code that gets the job done. Part 2 adds some nice-to-have features. * Format headings and column widths in the output Excel file* Search for strings and substrings * Find New Items or Retired Items * Compare Dates * Delete Rows * Delete Worksheets * Check if the output Excel file already exists in your filesystem, and delete it if it does * Create functions and call them from your main code file. Please note, I don't attempt to cover all aspects of Python, only those concepts needed to complete this lab. If you said, "Show me what I need to start using Python with Excel files" this lab answers that simple question. After you complete the lab, you'll definitely be able to say you can program in Python. Python is really powerful, and I hope you enjoy the lab and want to continue to expand your Python skills in the future.In my opinion, a working code example takes all the guesswork out of programming, leaving just the fun of learning something new. You don't have to wonder if you have the correct indentation, your counter is in the right place, or if you forgot the colon at the end of the line when you defined your function. Are you ready? Let's get started!

Essentials of Excel VBA, Python, and R

Author : John Lee
Publisher : Springer
ISBN 13 : 9783031528866
Total Pages : 0 pages
Book Rating : 4.5/5 (288 download)

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Book Synopsis Essentials of Excel VBA, Python, and R by : John Lee

Download or read book Essentials of Excel VBA, Python, and R written by John Lee and published by Springer. This book was released on 2023-12-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

The Ultimate Power Query Cookbook for Power BI and Excel

Author : Dominick Raimato
Publisher : BPB Publications
ISBN 13 : 9355517394
Total Pages : 522 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis The Ultimate Power Query Cookbook for Power BI and Excel by : Dominick Raimato

Download or read book The Ultimate Power Query Cookbook for Power BI and Excel written by Dominick Raimato and published by BPB Publications. This book was released on 2024-01-22 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novice or expert, learn to simplify and optimize data transformations KEY FEATURES ● Practical approaches to cleansing, connecting and transforming data in Power Query. ● Real-life examples that readers can apply to their own work. ● Master Power Query for Excel and Power BI with step-by-step recipes. DESCRIPTION “The Ultimate Power Query Cookbook for Power BI and Excel” serves up easy-to-follow recipes that transform data into meaningful insights. You will learn to clean messy files, combine datasets, and even use AI magic to Power BI and Excel. This book will walk you through the basics of getting connected to data with Power Query. You will understand how to ingest data from files, folders, databases, websites, APIs, and other third party sources. Once connected, you will learn how to transform the data so it is ready for your use. We will clean up columns, filter, replace, extract, and classify data in Power Query to meet your needs. The book offers over 100 practical recipes, ensuring you understand each step with clear explanations and examples. Lastly, we will go over advanced techniques to help optimize and simplify your transformations allowing fast refreshes all while helping you manage them in the future. This book will help you know how to apply these techniques and recipes to your data all while understanding the implications of making certain decisions. This will enable you to have better conversations with other data professionals who are providing data for your use. WHAT YOU WILL LEARN ● Learn to connect to files, databases, and third-party services. ● Manage data types and formats to optimize storage. ● Transform, create, and manipulate queries. ● Combine, merge, filter, and cleanse queries. ● Integrate artificial intelligence to accelerate insights. ● Perform complex and scalable transformations. WHO THIS BOOK IS FOR Novice or expert, this book is designed for all Excel users, data analysts, Power BI power users, business professionals and data enthusiasts to get the most out of your data solutions when transforming your data in Power Query. TABLE OF CONTENTS 1. Introduction to Power Query 2. Connect to File-Based Data Sources 3. Connect to Web-Based Data Sources 4. Connect to Database Sources 5. Connect to Third-Party Data Sources 6. Managing Data Types 7. Transforming Columns 8. Cleansing Columns 9. Creating New Columns 10. Combining and Manipulating Queries 11. Using Python, R, and AI 12. Indexing 13. Parameters 14. Functions 15. Advanced Web Connections 16. Manipulating Supporting Queries

Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 676 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python for Data Analysis by : Wes McKinney

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Football Analytics with Python & R

Author : Eric A. Eager
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492099589
Total Pages : 361 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Football Analytics with Python & R by : Eric A. Eager

Download or read book Football Analytics with Python & R written by Eric A. Eager and published by "O'Reilly Media, Inc.". This book was released on 2023-08-15 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data. Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to: Apply basic statistical concepts to football datasets Describe football data with quantitative methods Create efficient workflows that offer reproducible results Use data science skills such as web scraping, manipulating data, and plotting data Implement statistical models for football data Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny And more

Learn R Programming in 24 Hours

Author : Alex Nordeen
Publisher : Guru99
ISBN 13 :
Total Pages : 349 pages
Book Rating : 4./5 ( download)

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Book Synopsis Learn R Programming in 24 Hours by : Alex Nordeen

Download or read book Learn R Programming in 24 Hours written by Alex Nordeen and published by Guru99. This book was released on 2020-10-31 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?

R for Business Analytics

Author : A Ohri
Publisher : Springer Science & Business Media
ISBN 13 : 1461443423
Total Pages : 322 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis R for Business Analytics by : A Ohri

Download or read book R for Business Analytics written by A Ohri and published by Springer Science & Business Media. This book was released on 2012-09-14 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples.

Python for Excel

Author : Felix Zumstein
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492080950
Total Pages : 366 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Python for Excel by : Felix Zumstein

Download or read book Python for Excel written by Felix Zumstein and published by "O'Reilly Media, Inc.". This book was released on 2021-03-04 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot

Analysis and Presentation of Experimental Results

Author : Costas Christodoulides
Publisher : Springer
ISBN 13 : 3319533452
Total Pages : 526 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Analysis and Presentation of Experimental Results by : Costas Christodoulides

Download or read book Analysis and Presentation of Experimental Results written by Costas Christodoulides and published by Springer. This book was released on 2017-06-14 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical methods and the theory of errors. After presenting basic theoretical concepts, the book describes the methods by which the results can be presented, both numerically and graphically. The book is divided into three parts, of roughly equal length, addressing the theory, the analysis of data, and the presentation of results. Examples are given and problems are solved using the Excel, Origin, Python and R software packages. In addition, programs in all four languages are made available to readers, allowing them to use them in analyzing and presenting the results of their own experiments. Subjects are treated at a level appropriate for undergraduate students in the natural sciences, but this book should also appeal to anyone whose work involves dealing with experimental results.