Verfügbarkeit: Principles of data science

$Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning$

Principles of data science: a beginner's guide to essential math and coding skills for data fluency and machine learning

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Bibliographische Detailangaben
1. Verfasser:	Ozdemir, Sinan (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Birmingham Packt Publishing Jan 2024
Ausgabe:	Third edition
Schlagworte:	Data Science Maschinelles Lernen
Online-Zugang:	DE-1050 DE-91 DE-706 DE-945 URL des Erstveröffentlichers
Beschreibung:	1 Online-Ressource (xix, 300 Seiten)
ISBN:	9781837636006

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Datensatz im Suchindex

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contents	Intro -- Title Page -- Copyright and Credits -- Dedication -- Contributor -- Table of Contents -- Preface -- Chapter 1: Data Science Terminology -- What is data science? -- Understanding basic data science terminology -- Why data science? -- Example -- predicting COVID-19 with machine learning -- The data science Venn diagram -- The math -- Computer programming -- Example -- parsing a single tweet -- Domain knowledge -- Some more terminology -- Data science case studies -- Case study -- automating government paper pushing -- Case study -- what's in a job description? -- Summary Chapter 2: Types of Data -- Structured versus unstructured data -- Quantitative versus qualitative data -- Digging deeper -- The four levels of data -- The nominal level -- Measures of center -- The ordinal level -- The interval level -- The ratio level -- Data is in the eye of the beholder -- Summary -- Questions and answers -- Chapter 3: The Five Steps of Data Science -- Introduction to data science -- Overview of the five steps -- Exploring the data -- Guiding questions for data exploration -- DataFrames -- Series -- Exploration tips for qualitative data -- Summary Chapter 4: Basic Mathematics -- Basic symbols and terminology -- Vectors and matrices -- Arithmetic symbols -- Summation -- Logarithms/exponents -- Set theory -- Linear algebra -- Matrix multiplication -- How to multiply matrices together -- Summary -- Chapter 5: Impossible or Improbable -- A Gentle Introduction to Probability -- Basic definitions -- What do we mean by "probability"? -- Bayesian versus frequentist -- Frequentist approach -- The law of large numbers -- Compound events -- Conditional probability -- How to utilize the rules of probability -- The addition rule -- Mutual exclusivity The multiplication rule -- Independence -- Complementary events -- Introduction to binary classifiers -- Summary -- Chapter 6: Advanced Probability -- Bayesian ideas revisited -- Bayes' theorem -- More applications of Bayes' theorem -- Random variables -- Discrete random variables -- Continuous random variables -- Summary -- Chapter 7: What Are the Chances? An Introduction to Statistics -- What are statistics? -- How do we obtain and sample data? -- Obtaining data -- Observational -- Experimental -- Sampling data -- How do we measure statistics? -- Measures of center -- Measures of variation The coefficient of variation -- Measures of relative standing -- The insightful part -- correlations in data -- The empirical rule -- Example -- exam scores -- Summary -- Chapter 8: Advanced Statistics -- Understanding point estimates -- Sampling distributions -- Confidence intervals -- Hypothesis tests -- Conducting a hypothesis test -- One-sample t-tests -- Type I and Type II errors -- Hypothesis testing for categorical variables -- Chi-square goodness of fit test -- Chi-square test for association/independence -- Summary -- Chapter 9: Communicating Data -- Why does communication matter?
ctrlnum	(ZDB-221-PAI)9781837636006 (OCoLC)1427321625 (DE-599)BVBBV049600217
discipline	Informatik
edition	Third edition
format	Electronic eBook
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spelling	Ozdemir, Sinan Verfasser (DE-588)1153501937 aut Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning Sinan Ozdemir Third edition Birmingham Packt Publishing Jan 2024 1 Online-Ressource (xix, 300 Seiten) txt rdacontent c rdamedia cr rdacarrier Data Science (DE-588)1140936166 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Science (DE-588)1140936166 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-83763-630-3 https://portal.igpublish.com/iglibrary/search/PACKT0007069.html Verlag URL des Erstveröffentlichers Volltext
spellingShingle	Ozdemir, Sinan Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning Intro -- Title Page -- Copyright and Credits -- Dedication -- Contributor -- Table of Contents -- Preface -- Chapter 1: Data Science Terminology -- What is data science? -- Understanding basic data science terminology -- Why data science? -- Example -- predicting COVID-19 with machine learning -- The data science Venn diagram -- The math -- Computer programming -- Example -- parsing a single tweet -- Domain knowledge -- Some more terminology -- Data science case studies -- Case study -- automating government paper pushing -- Case study -- what's in a job description? -- Summary Chapter 2: Types of Data -- Structured versus unstructured data -- Quantitative versus qualitative data -- Digging deeper -- The four levels of data -- The nominal level -- Measures of center -- The ordinal level -- The interval level -- The ratio level -- Data is in the eye of the beholder -- Summary -- Questions and answers -- Chapter 3: The Five Steps of Data Science -- Introduction to data science -- Overview of the five steps -- Exploring the data -- Guiding questions for data exploration -- DataFrames -- Series -- Exploration tips for qualitative data -- Summary Chapter 4: Basic Mathematics -- Basic symbols and terminology -- Vectors and matrices -- Arithmetic symbols -- Summation -- Logarithms/exponents -- Set theory -- Linear algebra -- Matrix multiplication -- How to multiply matrices together -- Summary -- Chapter 5: Impossible or Improbable -- A Gentle Introduction to Probability -- Basic definitions -- What do we mean by "probability"? -- Bayesian versus frequentist -- Frequentist approach -- The law of large numbers -- Compound events -- Conditional probability -- How to utilize the rules of probability -- The addition rule -- Mutual exclusivity The multiplication rule -- Independence -- Complementary events -- Introduction to binary classifiers -- Summary -- Chapter 6: Advanced Probability -- Bayesian ideas revisited -- Bayes' theorem -- More applications of Bayes' theorem -- Random variables -- Discrete random variables -- Continuous random variables -- Summary -- Chapter 7: What Are the Chances? An Introduction to Statistics -- What are statistics? -- How do we obtain and sample data? -- Obtaining data -- Observational -- Experimental -- Sampling data -- How do we measure statistics? -- Measures of center -- Measures of variation The coefficient of variation -- Measures of relative standing -- The insightful part -- correlations in data -- The empirical rule -- Example -- exam scores -- Summary -- Chapter 8: Advanced Statistics -- Understanding point estimates -- Sampling distributions -- Confidence intervals -- Hypothesis tests -- Conducting a hypothesis test -- One-sample t-tests -- Type I and Type II errors -- Hypothesis testing for categorical variables -- Chi-square goodness of fit test -- Chi-square test for association/independence -- Summary -- Chapter 9: Communicating Data -- Why does communication matter? Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
subject_GND	(DE-588)1140936166 (DE-588)4193754-5
title	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning
title_auth	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning
title_exact_search	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning
title_exact_search_txtP	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning
title_full	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning Sinan Ozdemir
title_fullStr	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning Sinan Ozdemir
title_full_unstemmed	Principles of data science a beginner's guide to essential math and coding skills for data fluency and machine learning Sinan Ozdemir
title_short	Principles of data science
title_sort	principles of data science a beginner s guide to essential math and coding skills for data fluency and machine learning
title_sub	a beginner's guide to essential math and coding skills for data fluency and machine learning
topic	Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet	Data Science Maschinelles Lernen
url	https://portal.igpublish.com/iglibrary/search/PACKT0007069.html
work_keys_str_mv	AT ozdemirsinan principlesofdatascienceabeginnersguidetoessentialmathandcodingskillsfordatafluencyandmachinelearning

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