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