James D. Miller [James D. Miller] - Statistics for Data Science
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- Book:Statistics for Data Science
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Statistics for Data Science: summary, description and annotation
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Get your statistics basics right before diving into the world of data science
About This Book
- No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;
- Implement statistics in data science tasks such as data cleaning, mining, and analysis
- Learn all about probability, statistics, numerical computations, and more with the help of R programs
Who This Book Is For
This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.
What You Will Learn
- Analyze the transition from a data developer to a data scientist mindset
- Get acquainted with the R programs and the logic used for statistical computations
- Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more
- Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis
- Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks
- Get comfortable with performing various statistical computations for data science programmatically
In Detail
Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.
This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.
By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Style and approach
Step by step comprehensive guide with real world examples
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Yes, you will find that there is plenty of respectable, easy-to-understand, and valuable information and examples on artificial neural networks generously obtainable on the internet.
One such resource is offered by Gekko Quant (http://gekkoquant.com/author/gekkoquant/) and is worth taking some time to locate and read.
This information offers a very nice tutorial that produces an artificial neural network that takes a single input (a number that you want to calculate a square root for) and produces a single output (the square root of the number input).
The output of this artificial neural network example model is nicely displayed in an easy-to-understand format and I'll show it here as a great example of how the results of an ANN model should look:
From the output information shown here, you can see (as the author declares) that the neural network does a reasonable job at finding the square root (of each input number).
Before a data scientist can begin the process of fitting an artificial neural network, some important time-saving groundwork needs to be accomplished, which we'll discuss in the next few sections.
To be sure, artificial neural networks are not easy to understand, train, and tune; some preparatory or preprocessing first is strongly recommended.
Suppose there are concerns about the quality of the data to be, or being, consumed by the organization. As we eluded to earlier in this chapter, there are different types of data quality concerns such as what we called mechanical issues as well as statistical issues (and there are others).
If management is questioning the validity of the total sales listed on a daily report or perhaps doesn't trust it because the majority of your customers are not legally able to drive in the United States, the number of the organizations repeat customers are declining, you have a quality issue:
Quality is a concern to both the data developer and the data scientist. A data developer focuses more on timing and formatting (the mechanics of the data), while the data scientist is more interested in the data's statistical quality (with priority given to issues with the data that may potentially impact the reliability of a particular study).
This chapter discusses how a developer might understand and approach the topic of data cleaning using several common statistical methods.
In this chapter, we've broken things into the following topics:
- Understanding basic data cleaning
- Using R to detect and diagnose common data issues, such as missing values, special values, outliers, inconsistencies, and localization
- Using R to address advanced statistical situations, such as transformation, deductive correction, and deterministic imputation
If your statistical objective is to look for and learn about relationships in your data, you first examine your data for a form or, in other words, ask the question: does your data revolve around frequencies or measurements? From there, the number of predictor variables will dictate to you what regression (or other) approach you should use in your project.
Back to our previous mention of a package named AdaBoost, which is short for adaptive boosting. AdaBoost is a boosting approach referred to as an ensemble learning algorithm. Ensemble learning is when multiple learners are used in conjunction with each other to build a stronger learning algorithm.
AdaBoost works by selecting a base algorithm and then iteratively improving it by accounting for the incorrectly classified examples in the training dataset.
The aforementioned article describes how AdaBoost works:
- AdaBoost trains a model on a data subset
- Weak learners (based upon performance) are weighted
- The process is repeated
In narrative form, the AdaBoost boosting logic can be explained in the following way:
- The process works by building a model on training data and then measuring the results' accuracy on that training data, then:
- The individual results that were erroneous in the model are assigned a larger weight (or weighted more) than those that were correct, and then the model is retrained again using these new weights. This logic is then repeated multiple times, adjusting the weights of individual observations each time based on whether they were correctly classified or not in the last iteration!
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.
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In a manner of speaking, boosting is a process generally accepted in data science for improving the accuracy of a weak learning data science process.
Specifically, boosting is aimed at reducing bias and variance in supervised learning.
What do we mean by bias and variance? Before going on further about boosting, let's take note of what we mean by bias and variance.
Data scientists describe bias as a level of favoritism that is present in the data collection process, resulting in uneven, disingenuous results and can occur in a variety of different ways. A sampling method is called
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