Chapter 1: Transitioning from Data Developer to Data Scientist, sets the stage for the transition from data developer to data scientist. You will understand the difference between a developer mindset versus a data scientist mindset, the important difference between the two, and how to transition into thinking like a data scientist.

Chapter 2: Declaring the Objectives, introduces and explains (from a developer’s perspective) the basic objectives behind statistics for data science and introduces you to the important terms and keys that are used in the field of data science.

Chapter 3: A Developer's  Approach to Data Cleaning, discusses how a developer might understand and approach the topic of data cleaning using common statistical methods.

Chapter 4: Data Mining and the Database Developer, introduces the developer to mining data using R. You will understand what data mining is, why it is important, and feel comfortable using R for the most common statistical data mining methods: dimensional reduction, frequent patterns, and sequences.

Chapter 5: Statistical Analysis for the Database Developer, discusses the difference between data analysis or summarization and statistical data analysis and will follow the steps for successful statistical analysis of data, describe the nature of data, explore the relationships presented in data, create a summarization model from data, prove the validity of a model, and employ predictive analytics on a developed model.

Chapter 6: Database Progression to Database Regression, sets out to define statistical regression concepts and outline how a developer might use regression for simple forecasting and prediction within a typical data development project.

Chapter 7: Regularization for Database Improvement, introduces the developer to the idea of statistical regularization to improve data models. You will review what statistical regularization is, why it is important, and various statistical regularization methods.

Chapter 8: Data Development and Assessment, covers the idea of data model assessment and using statistics for assessment. You will understand what statistical assessment is, why it is important, and use R for statistical assessment.

Chapter 9: Databases and Neural Networks, defines the neural network model and draws from a developer’s knowledge of data models to help understand the purpose and use of neural networks in data science.

Chapter 10: Boosting and your Database, introduces the idea of using statistical boosting to better understand data in a database.

Chapter 11: Database Classification using Support Vector Machines, uses developer terminologies to define an SVM, identify various applications for its use and walks through an example of using a simple SVM to classify data in a database

Chapter 12: Database Structures and Machine Learning, aims to provide an explanation of the types of machine learning and shows the developer how to use machine learning processes to understand database mappings and identify patterns within the data.

This book is intended for those with a data development background who are interested in possibly entering the field of data science and are looking for concise information on the topic of statistics with the help of insightful programs and simple explanation. Just bring your data development experience and an open mind!

This book is intended for those developers who are interested in entering the field of data science and are looking for concise information on the topic of statistics with the help of insightful programs and simple explanation.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: In statistics, a boxplot is a simple way to gain information regarding the shape, variability, and center (or median) of a statistical data set, so we'll use the boxplot with our data to see if we can identify both the median Coin-in and if there are any outliers.

A block of code is set as follows:

MyFile <-"C:/GammingData/SlotsResults.csv" 
MyData <- read.csv(file=MyFile, header=TRUE, sep=",") 

New terms and important words are shown in bold. 

Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply email [email protected], and mention the book's title in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.