Getting started with data science : (Record no. 7451)

MARC details
000 -LEADER
fixed length control field 08486cam a22002177i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20180223171104.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180223b20152016ii a|||| |||| 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789332570252 (pbk)
040 ## - CATALOGING SOURCE
Transcribing agency NCL
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Edition number 23
Item number HAI-G 2016 9734
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Haider, Murtaza.
245 10 - TITLE STATEMENT
Title Getting started with data science :
Remainder of title making sense of data with analytics /
Statement of responsibility, etc. Murtaza Haider.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. UP India:
Name of publisher, distributor, etc. Pearson,
Date of publication, distribution, etc. 2016.
300 ## - PHYSICAL DESCRIPTION
Extent xxx, 573 pages :
Other physical details illustrations ;
Dimensions 23 cm
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Data Science: The Sexiest Job in the 21st Century -- Storytelling at Google and Walmart -- Getting Started with Data Science -- Do We Need Another Book on Analytics? -- Repeat, Repeat, Repeat, and Simplify -- Chapters' Structure and Features -- Analytics Software Used -- What Makes Someone a Data Scientist? -- Existential Angst of a Data Scientist -- Data Scientists: Rarer Than Unicorns -- Beyond the Big Data Hype -- Big Data: Beyond Cheerleading -- Big Data Hubris -- Leading by Miles -- Predicting Pregnancies, Missing Abortions -- What's Beyond This Book? -- Summary -- Endnotes -- The Liberated Data: The Open Data -- The Caged Data -- Big Data Is Big News -- It's Not the Size of Big Data; It's What You Do with It -- Free Data as in Free Lunch -- FRED -- Quandl -- U.S. Census Bureau and Other National Statistical Agencies -- Search-Based Internet Data -- Google Trends -- Google Correlate -- Survey Data -- PEW Surveys -- ICPSR -- Summary -- Endnotes -- The Final Deliverable -- What Is the Research Question? -- What Answers Are Needed? -- How Have Others Researched the Same Question in the Past? -- What Information Do You Need to Answer the Question? -- What Analytical Techniques/Methods Do You Need? -- The Narrative -- The Report Structure -- Have You Done Your Job as a Writer? -- Building Narratives with Data -- "Big Data, Big Analytics, Big Opportunity" -- Urban Transport and Housing Challenges -- Human Development in South Asia -- The Big Move -- Summary -- Endnotes -- 2014: The Year of Soccer and Brazil -- Using Percentages Is Better Than Using Raw Numbers -- Data Cleaning -- Weighted Data -- Cross Tabulations -- Going Beyond the Basics in Tables -- Seeing Whether Beauty Pays -- Data Set -- What Determines Teaching Evaluations? -- Does Beauty Affect Teaching Evaluations? -- Putting It All on (in) a Table -- Generating Output with Stata -- Summary Statistics Using Built-In Stata -- Using Descriptive Statistics -- Weighted Statistics -- Correlation Matrix -- Reproducing the Results for the Hamermesh and Parker Paper -- Statistical Analysis Using Custom Tables -- Summary -- Endnotes -- Telling Stories with Figures -- Data Types -- Teaching Ratings -- The Congested Lives in Big Cities -- Summary -- Endnotes -- Random Numbers and Probability Distributions -- Casino Royale: Roll the Dice -- Normal Distribution -- The Student Who Taught Everyone Else -- Statistical Distributions in Action -- Z-Transformation -- Probability of Getting a High or Low Course Evaluation -- Probabilities with Standard Normal Table -- Hypothetically Yours -- Consistently Better or Happenstance -- Mean and Not So Mean Differences -- Handling Rejections -- The Mean and Kind Differences -- Comparing a Sample Mean When the Population SD Is Known -- Left Tail Between the Legs -- Comparing Means with Unknown Population SD -- Comparing Two Means with Unequal Variances -- Comparing Two Means with Equal Variances -- Worked-Out Examples of Hypothesis Testing -- Best Bu-Apple Store Comparison -- Assuming Equal Variances -- Exercises for Comparison of Means -- Regression for Hypothesis Testing -- Analysis of Variance -- Significantly Correlated -- Summary -- Endnotes -- The Department of Obvious Conclusions -- Why Regress? -- Introducing Regression Models -- All Else Being Equal -- Holding Other Factors Constant -- Spuriously Correlated -- A Step-By-Step Approach to Regression -- Learning to Speak Regression -- The Math Behind Regression -- Ordinary Least Squares Method -- Regression in Action -- This Just In: Bigger Homes Sell for More -- Does Beauty Pay? Ask the Students -- Survey Data, Weights, and Independence of Observations -- What Determines Household Spending on Alcohol and Food -- What Influences Household Spending on Food? -- Advanced Topics -- Homoskedasticity -- Multicollinearity -- Summary -- Endnotes -- To Smoke or Not to Smoke: That Is the Question -- Binary Outcomes -- Binary Dependent Variables -- Let's Question the Decision to Smoke or Not -- Smoking Data Set -- Exploratory Data Analysis -- What Makes People Smoke: Asking Regression for Answers -- Ordinary Least Squares Regression -- Interpreting Models at the Margins -- The Logit Model -- Interpreting Odds in a Logit Model -- Probit Model -- Interpreting the Probit Model -- Using Zelig for Estimation and Post-Estimation Strategies -- Estimating Logit Models for Grouped Data -- Using SPSS to Explore the Smoking Data Set -- Regression Analysis in SPSS -- Estimating Logit and Probit Models in SPSS -- Summary -- Endnotes -- What Is Categorical Data? -- Analyzing Categorical Data -- Econometric Models of Binomial Data -- Estimation of Binary Logit Models -- Odds Ratio -- Log of Odds Ratio -- Interpreting Binary Logit Models -- Statistical Inference of Binary Logit Models -- How I Met Your Mother? Analyzing Survey Data -- A Blind Date with the Pew Online Dating Data Set -- Demographics of Affection -- High-Techies -- Romancing the Internet -- Dating Models -- Multinomial Logit Models -- Interpreting Multinomial Logit Models -- Choosing an Online Dating Service -- Pew Phone Type Model -- Why Some Women Work Full-Time and Others Don't -- Conditional Logit Models -- Random Utility Model -- Independence From Irrelevant Alternatives -- Interpretation of Conditional Logit Models -- Estimating Logit Models in SPSS -- Summary -- Endnotes -- Fundamentals of GIS -- GIS Platforms -- Freeware GIS -- GIS Data Structure -- GIS Applications in Business Research -- Retail Research -- Hospitality and Tourism Research -- Lifestyle Data: Consumer Health Profiling -- Competitor Location Analysis -- Market Segmentation -- Spatial Analysis of Urban Challenges -- The Hard Truths About Public Transit in North America -- Toronto Is a City Divided into the Haves, Will Haves, and Have Nots -- Income Disparities in Urban Canada -- Where Is Toronto's Missing Middle Class? It Has Suburbanized Out of Toronto -- Adding Spatial Analytics to Data Science -- Race and Space in Chicago -- Developing Research Questions -- Race, Space, and Poverty -- Race, Space, and Commuting -- Regression with Spatial Lags -- Summary -- Endnotes -- Introducing Time Series Data and How to Visualize It -- How Is Time Series Data Different'? -- Starting with Basic Regression Models -- What Is Wrong with Using OLS Models for Time Series Data? -- Newey-West Standard Errors -- Regressing Prices with Robust Standard Errors -- Time Series Econometrics -- Stationary Time Series -- Autocorrelation Function (ACF) -- Partial Autocorrelation Function (PCF) -- White Noise Tests -- Augmented Dickey Fuller Test -- Econometric Models for Time Series Data -- Correlation Diagnostics -- Invertible Time Series and Lag Operators -- The ARMA Model -- ARIMA Models -- Distributed Lag and VAR Models -- Applying Time Series Tools to Housing Construction -- Macro-Economic and Socio-Demographic Variables Influencing Housing Starts -- Estimating Time Series Models to Forecast New Housing Construction -- OLS Models -- Distributed Lag Model -- Out-of-Sample Forecasting with Vector Autoregressive Models -- ARIMA Models -- Summary -- Endnotes -- Can Cheating on Your Spouse Kill You? -- Are Cheating Men Alpha Males? -- UnFair Comments: New Evidence Critiques Fair's Research -- Data Mining: An Introduction -- Seven Steps Down the Data Mine -- Establishing Data Mining Goals -- Selecting Data -- Preprocessing Data -- Transforming Data -- Storing Data -- Mining Data -- Evaluating Mining Results -- Rattle Your Data -- What Does Religiosity Have to Do with Extramarital Affairs? -- The Principal Components of an Extramarital Affair -- Will It Rain Tomorrow? Using PCA For Weather Forecasting -- Do Men Have More Affairs Than Females? -- Two Kinds of People: Those Who Have Affairs, and Those Who Don't -- Models to Mine Data with Rattle -- Summary -- Endnotes.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Cost, normal purchase price Inventory number Total Checkouts Total Renewals Full call number Barcode Date last seen Date checked out Price effective from Koha item type
          Namal Library Namal Library Computer Science 02/23/2018 1022.00 Bill No. 4084 7 8 006.312 HAI-G 2016 9734 0009734 05/24/2023 05/11/2023 02/23/2018 Books