Data mining and statistical analysis using SQL / by Trueblood, Robert P., Lovett, John N.

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R data science essentials : learn the essence of data science and visualization using R in no time at all / by Koushik, Raja B., Ravindran, Sharan Kumar, PALCI EBSCO books

Table of Contents: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while…”
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Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / by Mukhiya, Suresh Kumar, Ahmed, Usman, PALCI EBSCO books

Table of Contents: “…Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness…”
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Fraud and fraud detection : a data analytics approach / by Gee, Sunder, Safari Books Online

Table of Contents: “…Determine Whether IDEA Is AppropriateData Requirements; Performing the Audit; Obtain Test Files; IDEA Import; After Import; Cleaning Up the Data; Documenting the Results; File Format Types; Preparation for Data Analysis; Data Familiarization; Arranging and Organizing Data; Conclusion; Notes; Chapter 4: Statistics and Sampling; Descriptive Statistics; Inferential Statistics; Measures of Center; Measure of Dispersion; Measure of Variability; Deviations from the Mean; The Mean Deviation; The Variance; The Standard Deviation; Sampling; Statistical Sampling; Nonstatistical Sampling; Sampling Risk…”
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Machine Learning with Python Cookbook : practical solutions from preprocessing to deep learning / by Gallatin, Kyle, Safari Books Online

Table of Contents: “…Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem…”
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Trends of data science and applications : theory and practices / by SpringerLink (Online service)

Table of Contents: “…5 Measures of Central Tendency -- 5.1 Mean -- 5.2 Median -- 5.3 Mode -- 5.4 Variance -- 5.5 Standard Deviation -- 6 Distributions in Statistics -- 6.1 Probability Distributions -- 6.2 PMF Versus PDF -- 6.3 Common Probability Distributions -- 6.4 Kurtosis -- 6.5 Skewness in Distributions -- 6.6 Scaling and Transformations -- 7 Outlier Treatment -- 7.1 Understanding Outliers -- 7.2 Detecting Outliers -- 8 Correlation Analysis -- 8.1 Steps for Correlation Analysis -- 8.2 Autocorrelation Versus Partial Correlation -- 9 Variance and Covariance Analysis -- 9.1 Analysis of Variance (ANOVA)…”
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Data Reduction and Analysis by Raghavender, U. S., PALCI EBSCO books

Table of Contents: “…3.5 Estimates Of Location3.6 Mean; 3.7 Median And Robust Estimates; 3.8 Estimates Of Variability; 3.9 Standard Deviation And Related Estimates; 3.10 Conclusions; Chapter 4 Data Science With Python And R; 4.1 Dataframe; 4.2 Reading The Files; 4.3 Indexing And Slicing; 4.4 Data Selection; 4.5 Function Mapping And Grouping; 4.6 Aggregate; 4.7 Conclusions; Chapter 5 Error Analysis; 5.1 Uncertainties In Data; 5.2 Propagation of Errors; 5.3 Conclusions; Chapter 6 Principal Component Analysis; 6.1 Preparing Our TB Data; 6.2 Using R For PCA; 6.3 Exploring Data Structure With K-Means Clustering…”
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Project management analytics : a data-driven approach to making rational and effective project decisions / by Singh, Harjit, Safari Books Online

Table of Contents: “…-- Project versus Program versus Portfolio -- Project Management Office (PMO) -- Project Life Cycle (PLC) -- Project Management Life Cycle (PMLC) -- A Process within the PMLC -- Work Breakdown Structure (WBS) -- Systems Development Life Cycle (SDLC) -- Summary -- Key Terms -- Case Study: Life Cycle of a Construction Project -- Case Study Questions -- Chapter Review and Discussion Questions -- Bibliography -- ch. 4 Chapter Statistical Fundamentals I: Basics and Probability Distributions -- Statistics Basics -- Probability Distribution -- Mean, Variance, and Standard Deviation of a Binomial Distribution -- Poisson Distribution -- Normal Distribution -- Confidence Intervals -- Summary -- Key Terms -- Solutions to Example Problems -- Chapter Review and Discussion Questions -- Bibliography -- ch. 5 Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression -- What Is a Hypothesis?…”
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Intelligent data analysis : from data gathering to data comprehension / by Safari Books Online.

Table of Contents: “…3.1 Introduction -- 3.2 Probability -- 3.2.1 Definitions -- 3.2.1.1 Random Experiments -- 3.2.1.2 Probability -- 3.2.1.3 Probability Axioms -- 3.2.1.4 Conditional Probability -- 3.2.1.5 Independence -- 3.2.1.6 Random Variable -- 3.2.1.7 Probability Distribution -- 3.2.1.8 Expectation -- 3.2.1.9 Variance and Standard Deviation -- 3.2.2 Bayes' Rule -- 3.3 Descriptive Statistics -- 3.3.1 Picture Representation -- 3.3.1.1 Frequency Distribution -- 3.3.1.2 Simple Frequency Distribution -- 3.3.1.3 Grouped Frequency Distribution -- 3.3.1.4 Stem and Leaf Display -- 3.3.1.5 Histogram and Bar Chart…”
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Data Wrangling with JavaScript / by Davis, Ashley, Safari, an O'Reilly Media Company, Safari Books Online

Table of Contents: “…8.7.7 Filtering using queries -- 8.7.8 Discarding data with projection -- 8.7.9 Sorting large data sets -- 8.8 Achieving better data throughput -- 8.8.1 Optimize your code -- 8.8.2 Optimize your algorithm -- 8.8.3 Processing data in parallel -- Summary -- Chapter 9: Practical data analysis -- 9.1 Expanding your toolkit -- 9.2 Analyzing the weather data -- 9.3 Getting the code and data -- 9.4 Basic data summarization -- 9.4.1 Sum -- 9.4.2 Average -- 9.4.3 Standard deviation -- 9.5 Group and summarize -- 9.6 The frequency distribution of temperatures -- 9.7 Time series -- 9.7.1 Yearly average temperature -- 9.7.2 Rolling average -- 9.7.3 Rolling standard deviation -- 9.7.4 Linear regression -- 9.7.5 Comparing time series -- 9.7.6 Stacking time series operations -- 9.8 Understanding relationships -- 9.8.1 Detecting correlation with a scatter plot -- 9.8.2 Types of correlation -- 9.8.3 Determining the strength of the correlation -- 9.8.4 Computing the correlation coefficient -- Summary -- Chapter 10: Browser-based visualization -- 10.1 Expanding your toolkit -- 10.2 Getting the code and data -- 10.3 Choosing a chart type -- 10.4 Line chart for New York City temperature -- 10.4.1 The most basic C3 line chart -- 10.4.2 Adding real data -- 10.4.3 Parsing the static CSV file -- 10.4.4 Adding years as the X axis -- 10.4.5 Creating a custom Node.js web server -- 10.4.6 Adding another series to the chart -- 10.4.7 Adding a second Y axis to the chart -- 10.4.8 Rendering a time series chart -- 10.5 Other chart types with C3 -- 10.5.1 Bar chart -- 10.5.2 Horizontal bar chart -- 10.5.3 Pie chart -- 10.5.4 Stacked bar chart -- 10.5.5 Scatter plot chart -- 10.6 Improving the look of our charts -- 10.7 Moving forward with your own projects -- Summary -- Chapter 11: Server-side visualization -- 11.1 Expanding your toolkit -- 11.2 Getting the code and data.…”
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Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks / by Mund, Sumit, Safari Books Online

Table of Contents: “…Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- Introduction to predictive analytics -- Problem definition and scoping -- Data collection -- Data exploration and preparation -- Model development -- Model deployment -- Machine learning -- Kinds of machine learning problems -- Classification -- Regression -- Clustering -- Common machine learning techniques/algorithms -- Linear regression -- Logistic regression -- Decision tree-based ensemble models -- Neural networks and deep learning -- Introduction to Azure Machine Learning -- ML Studio -- Summary -- Chapter 2: ML Studio Inside Out -- Introduction to ML Studio -- Getting started with Microsoft Azure -- Microsoft account and subscription -- Creating and managing ML workspaces -- Inside ML Studio -- Experiments -- Creating and editing an experiment -- Running an experiment -- Creating and running an experiment -- do it yourself -- Workspace as a collaborative environment -- Summary -- Chapter 3: Data Exploration and Visualization -- The basic concepts -- The mean -- The median -- Standard deviation and variance -- Understanding a histogram -- The box and whiskers plot -- The outliers -- A scatter plot -- Data exploration in ML Studio -- Visualizing an automobile price dataset -- A histogram -- The box and whiskers plot -- Comparing features -- A snapshot -- Do it yourself -- Summary -- Chapter 4: Getting Data in and out of ML Studio -- Getting data in ML Studio -- Uploading data from a PC -- The Enter Data module -- The Data Reader module -- Getting data from the Web -- Getting data from Azure -- Data format conversion -- Getting data from ML Studio -- Saving dataset in a PC -- Saving results in ML Studio -- The Writer module -- Summary -- Chapter 5: Data Preparation.…”
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Basic statistics and regression for machine learning in Python / by Safari Books Online

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Data Wrangling with SQL : A Hands-On Guide to Manipulating, Wrangling, and Engineering Data Using SQL / by Kandarpa, Raghav, Safari Books Online

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