St. Xavier's University Library Search for 'su:"Data processing"'

Decision Support and Business Intelligence Systems

]]> By Turban, Efraim.
Noida Dorling Kindersley (India) Pvt.ltd 2014 .
672 , Includes index & glossary 9789332518254

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Decision Support and Business Intelligence Systems

]]> By Turban, Efraim.
Noida Dorling Kindersley (India) Pvt.ltd 2014 .
672 , Includes index & glossary 9789332518254

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Information technology for management

]]> By Muthukumaran, B. .
New Delhi Oxford university press 2010 .
xv, 680 , Includes index 9780198064145

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Introduction HR analytics with machine learning : empowering practitioners, psychologists, and organizations

]]> By Rosett, Christopher M. .
Switzerland Springer 2021 .
vii, 271 9783030676254

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Multivariate data analysis

New Delhi Cengage 2021 .
813p , Chapter 1 Overview of Multivariate Methods Section 1: Preparing for Multivariate Analysis Chapter 2: Examining Your Data Section 2: Interdependence Techniques Chapter 3: Exploratory Factor Analysis Chapter 4: Cluster Analysis Section 3: Dependence Techniques Chapter 5: Multiple Regression Chapter 6: MANOVA: Extending ANOVA Chapter 7: Discriminant Analysis Chapter 8: Logistic Regression: Regression with a Binary Dependent Variable Section 4: Moving Beyond the Basic Techniques Chapter 9: Structural Equation Modeling: An Introduction Chapter 10: Confirmatory Factor Analysis Chapter 11: Testing Structural Equation Models Chapter 12: Advanced Topics in SEM Chapter 13: Partial Least Squares Modeling (PLS-SEM) In addition to the chapters in the print book, e-copies of all other chapters in the previous editions are available to download on the companion website, including canonical correlation, conjoint analysis, multidimensional scaling, and correspondence analysis. Includes index 97893501358

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Linear models and regression with R / an integrated approach :

]]> By Sengupta, Debasis.
London World scientific 2020 .
750p , includes index 9780000988843

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Introductory statistics with R

]]> By Dalgaard, Peter .
New York Springer 2008 .
xvi, 363 , Basics p. 1 The R environment p. 31 Probability and distributions p. 55 Descriptive statistics and graphics p. 67 One- and two-sample tests p. 95 Regression and correlation p. 109 Analysis of variance and the Kruskal-Wallis test p. 127 Tabular data p. 145 Power and the computation of sample size p. 155 Advanced data handling p. 163 Multiple regression p. 185 Linear models p. 195 Logistic regression p. 227 Survival analysis p. 249 Rates and Poisson regression p. 259 Nonlinear curve fitting p. 275 Obtaining and installing R and the ISwR package p. 289 Data sets in the ISwR package p. 293 Compendium p. 325 Answers to exercises p. 337 Bibliography p. 355 Index p. 357 Table of Contents provided by Blackwell. All Rights Reserved. 9780387790534

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Practical time series analysis : prediction with statistics and machine learning

]]> By Nielsen, Aileen .
Kolkata Shroff publishers & distributors 2020 .
xvi, 480 , Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1. Time Series: An Overview and a Quick History. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 The History of Time Series in Diverse Applications 2 Medicine as a Time Series Problem 2 Forecasting Weather 6 Forecasting Economic Growth 7 Astronomy 9 Time Series Analysis Takes Off 10 The Origins of Statistical Time Series Analysis 12 The Origins of Machine Learning Time Series Analysis 13 More Resources 13 2. Finding and Wrangling Time Series Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Where to Find Time Series Data 18 Prepared Data Sets 18 Found Time Series 25 Retrofitting a Time Series Data Collection from a Collection of Tables 26 A Worked Example: Assembling a Time Series Data Collection 27 Constructing a Found Time Series 33 Timestamping Troubles 35 Whose Timestamp? 35 Guesstimating Timestamps to Make Sense of Data 36 What’s a Meaningful Time Scale? 39 Cleaning Your Data 40 Handling Missing Data 40 Upsampling and Downsampling 52 Smoothing Data 55 iiiSeasonal Data 60 Time Zones 63 Preventing Lookahead 67 More Resources 69 3. Exploratory Data Analysis for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Familiar Methods 73 Plotting 74 Histograms 77 Scatter Plots 78 Time Series–Specific Exploratory Methods 81 Understanding Stationarity 82 Applying Window Functions 86 Understanding and Identifying Self-Correlation 91 Spurious Correlations 102 Some Useful Visualizations 104 1D Visualizations 104 2D Visualizations 105 3D Visualizations 113 More Resources 117 4. Simulating Time Series Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 What’s Special About Simulating Time Series? 120 Simulation Versus Forecasting 121 Simulations in Code 121 Doing the Work Yourself 122 Building a Simulation Universe That Runs Itself 128 A Physics Simulation 134 Final Notes on Simulations 140 Statistical Simulations 141 Deep Learning Simulations 141 More Resources 142 5. Storing Temporal Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Defining Requirements 145 Live Data Versus Stored Data 146 Database Solutions 148 SQL Versus NoSQL 149 Popular Time Series Database and File Solutions 152 File Solutions 157 NumPy 158 Pandas 158 iv | Table of ContentsStandard R Equivalents 158 Xarray 159 More Resources 160 6. Statistical Models for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Why Not Use a Linear Regression? 163 Statistical Methods Developed for Time Series 166 Autoregressive Models 166 Moving Average Models 181 Autoregressive Integrated Moving Average Models 186 Vector Autoregression 196 Variations on Statistical Models 201 Advantages and Disadvantages of Statistical Methods for Time Series 203 More Resources 204 7. State Space Models for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 State Space Models: Pluses and Minuses 209 The Kalman Filter 210 Overview 210 Code for the Kalman Filter 212 Hidden Markov Models 218 How the Model Works 218 How We Fit the Model 220 Fitting an HMM in Code 224 Bayesian Structural Time Series 229 Code for bsts 230 More Resources 235 8. Generating and Selecting Features for a Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Introductory Example 240 General Considerations When Computing Features 241 The Nature of the Time Series 242 Domain Knowledge 242 External Considerations 243 A Catalog of Places to Find Features for Inspiration 243 Open Source Time Series Feature Generation Libraries 244 Domain-Specific Feature Examples 249 How to Select Features Once You Have Generated Them 252 Concluding Thoughts 255 More Resources 256 Table of Contents | v9. Machine Learning for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Time Series Classification 260 Selecting and Generating Features 260 Decision Tree Methods 264 Clustering 272 Generating Features from the Data 273 Temporally Aware Distance Metrics 280 Clustering Code 285 More Resources 287 10. Deep Learning for Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Deep Learning Concepts 292 Programming a Neural Network 294 Data, Symbols, Operations, Layers, and Graphs 294 Building a Training Pipeline 298 Inspecting Our Data Set 299 Steps of a Training Pipeline 302 Feed Forward Networks 318 A Simple Example 318 Using an Attention Mechanism to Make Feed Forward Networks More Time-Aware 321 CNNs 324 A Simple Convolutional Model 325 Alternative Convolutional Models 327 RNNs 330 Continuing Our Electric Example 332 The Autoencoder Innovation 334 Combination Architectures 335 Summing Up 340 More Resources 341 11. Measuring Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 The Basics: How to Test Forecasts 344 Model-Specific Considerations for Backtesting 347 When Is Your Forecast Good Enough? 348 Estimating Uncertainty in Your Model with a Simulation 350 Predicting Multiple Steps Ahead 353 Fit Directly to the Horizon of Interest 353 Recursive Approach to Distant Temporal Horizons 354 Multitask Learning Applied to Time Series 354 Model Validation Gotchas 355 More Resources 355 vi | Table of Contents12. Performance Considerations in Fitting and Serving Time Series Models. . . . . . . . . . . . 357 Working with Tools Built for More General Use Cases 358 Models Built for Cross-Sectional Data Don’t “Share” Data Across Samples 358 Models That Don’t Precompute Create Unnecessary Lag Between Measuring Data and Making a Forecast 360 Data Storage Formats: Pluses and Minuses 361 Store Your Data in a Binary Format 361 Preprocess Your Data in a Way That Allows You to “Slide” Over It 362 Modifying Your Analysis to Suit Performance Considerations 362 Using All Your Data Is Not Necessarily Better 363 Complicated Models Don’t Always Do Better Enough 363 A Brief Mention of Alternative High-Performance Tools 364 More Resources 365 13. Healthcare Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Predicting the Flu 367 A Case Study of Flu in One Metropolitan Area 367 What Is State of the Art in Flu Forecasting? 383 Predicting Blood Glucose Levels 384 Data Cleaning and Exploration 385 Generating Features 390 Fitting a Model 396 More Resources 401 14. Financial Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Obtaining and Exploring Financial Data 404 Preprocessing Financial Data for Deep Learning 410 Adding Quantities of Interest to Our Raw Values 410 Scaling Quantities of Interest Without a Lookahead 411 Formatting Our Data for a Neural Network 413 Building and Training an RNN 416 More Resources 423 15. Time Series for Government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Obtaining Governmental Data 426 Exploring Big Time Series Data 428 Upsample and Aggregate the Data as We Iterate Through It 431 Sort the Data 432 Online Statistical Analysis of Time Series Data 436 Remaining Questions 446 Further Improvements 446 More Resources 447 Table of Contents | vii16. Time Series Packages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Forecasting at Scale 449 Google’s Industrial In-house Forecasting 450 Facebook’s Open Source Prophet Package 452 Anomaly Detection 457 Twitter’s Open Source AnomalyDetection Package 457 Other Time Series Packages 460 More Resources 461 17. Forecasts About Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Forecasting as a Service 463 Deep Learning Enhances Probabilistic Possibilities 464 Increasing Importance of Machine Learning Rather Than Statistics 465 Increasing Combination of Statistical and Machine Learning Methodologies 466 More Forecasts for Everyday Life 466 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 9789352139255

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R in a nutshell

]]> By Adler, Joseph .
Kolkata Shroff publishers & distributors 2012 .
xix, 699 , Includes index 9789350239209

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Data science for business

]]> By Provost, Foster .
New Delhi Shroff publishers & distributors pvt. ltd. 2013 .
xviii, 384 , Includes index, glossary & bibliography 9789351102670

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Data strategy : how to profit from a world of big data, analytics and artificial intelligence

]]> By Marr, Bernard.
New Delhi Kogan 2022 .
259p. , Data is an integral strategic asset for all businesses. Learn how to leverage this data and generate valuable insights and true business value with bestselling author and data guru Bernard Marr. Data has massive potential for all businesses when used correctly, from small organizations to tech giants and huge multinationals, but this resource is too often not fully utilized. Data Strategy is the must-read guide on how to create a robust, data-driven approach that will harness the power of data to revolutionize your business. Explaining how to collect, use and manage data, this book prepares any organization with the tools and strategies needed to thrive in the digital economy. Now in its second edition, this bestselling title is fully updated with insights on understanding your customers and markets and how to provide them with intelligent services and products. With case studies and real-world examples throughout, Bernard Marr offers unrivalled expertise on how to gain the competitive advantage in a data-driven world. 9781398602588

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Predictive analytics for dummies

]]> By Bari, Anasse .
New Delhi Wiley India 2017 .
viii, 443 , Includes index 9788126567935

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Communicating with data visualisation : a practical guide /

]]> New Delhi Sage 2022 .
352p. , Part 1: The Data Visualisation Process Chapter 1: Find, design, make, refine Chapter 2: A spectrum of right answers Chapter 3: Find Chapter 4: Designing Static Graphics Chapter 5: Making Static Graphics Chapter 6: An Introduction to Interactive Data Visualisation Chapter 7: Designing Motion Graphics Chapter 8: Making Motion Graphics Chapter 9: Designing Interactive Infographics Chapter 10: Making Interactive Infographics Chapter 11: Refine Chapter 12: Resources 9781529743777

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A Treatise on information technology management

]]> By Biswas, Supriya.
Kolkata Aryan 2023 .
852p. , includes index 9788195189861

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Data analytics : transforming data into insights

]]> By Indumathi, J..
Delhi Cengage 2025 .
vii, various pages 9789366601779

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