Why program Excel? For solving complex calculations, automating tasks and presenting results. With VBA you can create custom modifications to transform Excel into a task-specific piece of software that will quickly and precisely serve your needs.

The best part is, you can program Excel with no additional tools. A variant of the Visual Basic programming language, VB for Applications (VBA) is built into Excel to facilitate its use as a platform. With VBA, you can create macros and templates, manipulate user interface features such as menus and toolbars, and work with custom user forms or dialog boxes. VBA is relatively easy to use, but if you've never programmed before, Programming Excel with VBA and .NET is a great way to learn a lot very quickly. If you're an experienced Excel user or a Visual Basic programmer, you'll pick up a lot of valuable new tricks. Developers looking forward to .NET development will also find discussion of how the Excel object model works with .NET tools, including Visual Studio Tools for Office (VSTO).

This book teaches you how to use Excel VBA by explaining concepts clearly and concisely in plain English, and provides plenty of downloadable samples so you can learn by doing. You'll be exposed to a wide range of tasks most commonly performed with Excel, arranged into chapters according to subject, with those subjects corresponding to one or more Excel objects. With both the samples and important reference information for each object included right in the chapters, instead of tucked away in separate sections, Programming Excel with VBA and .NET covers the entire Excel object library. For those just starting out, it also lays down the basic rules common to all programming languages.

With this single-source reference and how-to guide, you'll learn to use the complete range of Excel programming tasks to solve problems, no matter what you're experience level.

In a corporate setting, the Microsoft Office Suite is an invaluable set of applications. One of Offices' biggest advantages is that its applications can work together to share information, produce reports, and so on. The problem is, there isn't much documentation on their cross-usage. Until now.

Introducing Integrating Excel and Access, the unique reference that shows you how to combine the strengths of Microsoft Excel with those of Microsoft Access. In particular, the book explains how the powerful analysis tools of Excel can work in concert with the structured storage and more powerful querying of Access. The results that these two applications can produce together are virtually impossible to achieve with one program separately.

But the book isn't just limited to Excel and Access. There's also a chapter on SQL Server, as well as one dedicated to integrating with other Microsoft Office applications. In no time, you'll discover how to:
•Utilize the built in features of Access and Excel to access data
•Use VBA within Access or Excel to access data
•Build connection strings using ADO and DAO
•Automate Excel reports including formatting, functions, and page setup
•Write complex functions and queries with VBA
•Write simple and advanced queries with the Access GUI
•Produce pivot tables and charts with your data

With Integrating Excel and Access, you can crunch and visualize data like never before. It's the ideal guide for anyone who uses Microsoft Office to handle data.

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

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

If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.

Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.

Topics include:
•Data Structures and Algorithms
•Strings and Text
•Numbers, Dates, and Times
•Iterators and Generators
•Files and I/O
•Data Encoding and Processing
•Functions
•Classes and Objects
•Metaprogramming
•Modules and Packages
•Network and Web Programming
•Concurrency
•Utility Scripting and System Administration
•Testing, Debugging, and Exceptions
•C Extensions

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