Fundamentals of data science:
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-tradi...
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2022
|
| Ausgabe: | first edition |
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features:Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets.Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice.Information is presented in an accessible way for students, researchers and academicians and professionals |
| Beschreibung: | xiv, 282 Seiten Illustrationen, Diagramme 708 grams |
| ISBN: | 9781138336186 9781032079868 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Preface................................................................................................................ xi Authors............................................................................................................ xiii Part I Introduction to Data Science 1 Importance of Data Science...................................................................... 3 1.1 Need for Data Science........................................................................3 1.2 What Is Data Science?.........................................................................7 1.3 Data Science Process......................................................................... 9 1.4 Business Intelligence and Data Science......................................... 10 1.5 Prerequisites for a Data Scientist....................................................11 1.6 Components of Data Science.......................................................... 11 1.7 Tools and Skills Needed..................................................................12 1.8 Summary..........................................................................................13 References.................................................................................................. 15 2 Statistics and Probability.........................................................................17 2.1 Data Types........................................................................................ 17 2.2 Variable Types................................................................................. 18 2.3
Statistics............................................................................................19 2.4 Sampling Techniques and Probability...........................................22 2.5 Information Gain and Entropy...................................................... 24 2.6 Probability Theory.......................................................................... 31 2.7 Probability Types............................................................................ 33 2.8 Probability Distribution Functions................................................36 2.9 Bayes Theorem............................................................................... 38 2.10 Inferential Statistics..........................................................................39 2.11 Summary..........................................................................................43 References.................................................................................................. 44 3 Databases for Data Science..................................................................... 45 3.1 SQL - Tool for Data Science............................................................. 45 3.1.1 Basic Statistics with SQL.................................................... 45 3.1.2 Data Munging with SQL.................................................... 47 3.1.3 Filtering, Joins, and Aggregation.......................................48 3.1.4 Window Functions and Ordered Data............................. 57 3.1.5 Preparing Data for Analytics Tool..................................... 72 3.2
Advanced NoSQL for Data Science............................................... 77 v
vi Contents 3.2.1 Why NoSQL.........................................................................77 3.2.2 Document Databases for Data Science............................. 77 3.2.3 Wide-Column Databases for Data Science........................78 3.2.4 Graph Databases for Data Science..................................... 79 3.3 Summary........................................................................................... 79 References...................................................................................................84 Part II Data Modeling and Analytics 4 Data Science Methodology...................................................................... 87 4.1 Analytics for Data Science.............................................................. 87 4.2 Examples of Data Analytics............................................................ 89 4.3 Data Analytics Life Cycle................................................................ 90 4.3.1 Data Discovery.................................................................... 91 4.3.2 Data Preparation................................................................. 91 4.3.3 Model Planning................................................................... 94 4.3.4 Model Building................................................................... 96 4.3.5 Communicate Results........................................................ 98 4.3.6 Operationalization.............................................................. 99 4.4
Summary.......................................................................................... 99 References................................................................................................. 100 5 Data Science Methods and Machine Learning...................................103 5.1 Regression Analysis....................................................................... 103 5.1.1 Linear Regression..............................................................103 5.1.2 Logistic Regression............................................................109 5.1.3 Multinomial Logistic Regression................. Ill 5.1.4 Time-Series Models........................................................... 113 5.2 Machine Learning.......................................................................... 114 5.2.1 Decision Trees....................................................................114 5.2.2 Naïve Bayes........................................................................ 116 5.2.3 Support Vector Machines................................................. 117 5.2.4 Nearest Neighbor learning................................................119 5.2.5 Clustering........................................................................... 120 5.2.6 Confusion Matrix.............................................................. 122 5.3 Summary........................................................................................ 126 References.................................................................................................126 6 Data Analytics and Text
Mining...........................................................129 6.1 Text Mining..................................................................................... 129 6.1.1 Major Text Mining Areas................................................... 130 6.1.1.1 Information Retrieval.........................................131 6.1.1.2 Data Mining........................................................ 131 6.1.1.3 Natural Language Processing (NLP)................131
vii Contents 6.2 Text Analytics................................................................................ 135 6.2.1 Text Analysis Subtasks......................................................135 6.2.1.1 Cleaning and Parsing......................................... 135 6.2.1.2 Searching and Retrieval......................................136 6.2.1.3 Text Mining.........................................................136 6.2.1.4 Part-of-Speech Tagging.......................................136 6.2.1.5 Stemming........................................................... 136 6.2.1.6 Lemmatization....................................................137 6.2.2 Basic Text Analysis Steps..................................................137 6.3 Introduction to NaturalLanguage Processing............................. 138 6.3.1 Major Components of NLP............................................... 139 6.3.2 Stages of NLP.................................................................... 140 6.3.3 Statistical Processingof Natural Language......................141 6.3.3.1 Document Preprocessing...................................141 6.3.3.2 Parameterization................................................ 141 6.3.4 Applications of NLP..........................................................141 6.4 Summary........................................................................................ 142 References................................................................................................142 Part III Platforms for Data Science 7 Data Science Tool:
Python..................................................................... 147 7.1 Basics of Python for Data Science................................................. 147 7.2 Python Libraries: DataFrame Manipulation with pandas and NumPy....................................................................... 153 7.3 Exploration Data Analysis with Python...................................... 159 7.4 Time Series Data.............................................................................161 7.5 Clustering with Python................................................................. 163 7.6 ARCH and GARCH.......................................................................168 7.7 Dimensionality Reduction............................................................ 170 7.8 Python for Machine ML.................................................................174 7.9 KNN/Decision Tree/ Random Forest/SVM................................177 7.10 Python IDEs for Data Science........................................................182 7.11 Summary....................................................................................... 183 References................................................................................................184 8 Data Science Tool: R................................................................................187 8.1 Reading and Getting Data into R................................................. 187 8.1.1 Reading Data into R...........................................................187 8.1.2 Writing Data into
Files......................................................189 8.1.3 scanQ Function.................................................................. 190 8.1.4 Built-in Data Sets............................................................... 190 8.2 Ordered and Unordered Factors...................................................190
viii Contents 8.3 Arrays and Matrices...................................................................... 192 8.3.1 Arrays.................................................................................192 8.3.1.1 Creating an Array..............................................192 8.3.1.2 Accessing Elements in an Array.......................193 8.3.1.3 Array Manipulation...........................................193 8.3.2 Matrices..............................................................................194 8.3.2.1 Creating a Matrix............................................... 194 8.3.2.2 Matrix Transpose............................................... 194 8.3.2.3 Eigenvalues and Eigenvectors........................... 195 8.3.2.4 Matrix Concatenation.........................................195 8.4 Lists and Data Frames................................................................... 196 8.4.1 Lists.................................................................................... 196 8.4.1.1 Creating a List..................................................... 196 8.4.1.2 Concatenation of Lists.......................................196 8.4.2 Data Frames....................................................................... 197 8.4.2.1 Creating a Data Frame........................................197 8.4.2.2 Accessing the Data Frame..................................197 8.4.2.3 Adding Rows and Columns..............................198 8.5 Probability Distributions.............................................................. 198 8.5.1 Normal
Distribution..........................................................199 8.6 Statistical Models in R................................................................... 201 8.6.1 Model Fitting.....................................................................202 8.6.2 Marginal Effects................................................................ 203 8.7 Manipulating Objects.................................................................... 203 8.7.1 Viewing Objects................................................................ 203 8.7.2 Modifying Objects............................................................ 204 8.7.3 Appending Elements........................................................ 204 8.7.4 Deleting Objects................................................................ 205 8.8 Data Distribution...........................................................................206 8.8.1 Visualizing Distributions.................................................206 8.8.2 Statistics in Distributions..................................................206 8.9 Summary........................................................................................ 207 References................................................................................................208 9 Data Science Tool: MATLAB................................................................. 209 9.1 Data Science Workflow with MATLAB....................................... 209 9.2 Importing Data...............................................................................211 9.2.1 How Data is
Stored............................................................211 9.2.2 How MATLAB Represents Data...................................... 213 9.2.3 MATLAB Data Types........................................................214 9.2.4 Automating the Import Process....................................... 215 9.3 Visualizing and Filtering Data......................................................216 9.3.1 Plotting Data Contained in Tables....................................217 9.3.2 Selecting Data from Tables............................................... 218 9.3.3 Accessing and Creating Table Variables.......................... 219
Contents ix 9.4 Performing Calculations............................................................... 220 9.4.1 Basic Mathematical Operations....................................... 220 9.4.2 Using Vectors.....................................................................222 9.4.3 Using Functions................................................................ 223 9.4.4 Calculating Summary Statistics...................................... 224 9.4.5 Correlations between Variables.......................... 226 9.4.6 Accessing Subsets of Data................................................226 9.4.7 Performing Calculations by Category............................ 228 9.5 Summary........................................................................................ 230 References.................................................................................................231 10 GNU Octave as a Data Science Tool................................................... 233 10.1 Vectors and Matrices.....................................................................233 10.2 Arithmetic Operations..................................................................238 10.3 Set Operations............................................................................... 240 10.4 Plotting Data.................................................................................. 242 10.5 Summary........................................................................................ 247 References.................................................................................................248 11
Data Visualization Using Tableau....................................................... 249 11.1 Introduction to Data Visualization...............................................249 11.2 Introduction to Tableau.................................................................250 11.3 Dimensions and Measures, Descriptive Statistics......................252 11.4 Basic Charts.................................................................................... 256 11.5 Dashboard Design Principles....................................................259 11.6 Special Chart Types....................................................................... 261 11.7 Integrate Tableau with Google Sheets..........................................264 11.8 Summary........................................................................................ 265 References.................................................................................................267 Index................................................................................................................ 269
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| adam_txt |
Contents Preface. xi Authors. xiii Part I Introduction to Data Science 1 Importance of Data Science. 3 1.1 Need for Data Science.3 1.2 What Is Data Science?.7 1.3 Data Science Process. 9 1.4 Business Intelligence and Data Science. 10 1.5 Prerequisites for a Data Scientist.11 1.6 Components of Data Science. 11 1.7 Tools and Skills Needed.12 1.8 Summary.13 References. 15 2 Statistics and Probability.17 2.1 Data Types. 17 2.2 Variable Types. 18 2.3
Statistics.19 2.4 Sampling Techniques and Probability.22 2.5 Information Gain and Entropy. 24 2.6 Probability Theory. 31 2.7 Probability Types. 33 2.8 Probability Distribution Functions.36 2.9 Bayes' Theorem. 38 2.10 Inferential Statistics.39 2.11 Summary.43 References. 44 3 Databases for Data Science. 45 3.1 SQL - Tool for Data Science. 45 3.1.1 Basic Statistics with SQL. 45 3.1.2 Data Munging with SQL. 47 3.1.3 Filtering, Joins, and Aggregation.48 3.1.4 Window Functions and Ordered Data. 57 3.1.5 Preparing Data for Analytics Tool. 72 3.2
Advanced NoSQL for Data Science. 77 v
vi Contents 3.2.1 Why NoSQL.77 3.2.2 Document Databases for Data Science. 77 3.2.3 Wide-Column Databases for Data Science.78 3.2.4 Graph Databases for Data Science. 79 3.3 Summary. 79 References.84 Part II Data Modeling and Analytics 4 Data Science Methodology. 87 4.1 Analytics for Data Science. 87 4.2 Examples of Data Analytics. 89 4.3 Data Analytics Life Cycle. 90 4.3.1 Data Discovery. 91 4.3.2 Data Preparation. 91 4.3.3 Model Planning. 94 4.3.4 Model Building. 96 4.3.5 Communicate Results. 98 4.3.6 Operationalization. 99 4.4
Summary. 99 References. 100 5 Data Science Methods and Machine Learning.103 5.1 Regression Analysis. 103 5.1.1 Linear Regression.103 5.1.2 Logistic Regression.109 5.1.3 Multinomial Logistic Regression. Ill 5.1.4 Time-Series Models. 113 5.2 Machine Learning. 114 5.2.1 Decision Trees.114 5.2.2 Naïve Bayes. 116 5.2.3 Support Vector Machines. 117 5.2.4 Nearest Neighbor learning.119 5.2.5 Clustering. 120 5.2.6 Confusion Matrix. 122 5.3 Summary. 126 References.126 6 Data Analytics and Text
Mining.129 6.1 Text Mining. 129 6.1.1 Major Text Mining Areas. 130 6.1.1.1 Information Retrieval.131 6.1.1.2 Data Mining. 131 6.1.1.3 Natural Language Processing (NLP).131
vii Contents 6.2 Text Analytics. 135 6.2.1 Text Analysis Subtasks.135 6.2.1.1 Cleaning and Parsing. 135 6.2.1.2 Searching and Retrieval.136 6.2.1.3 Text Mining.136 6.2.1.4 Part-of-Speech Tagging.136 6.2.1.5 Stemming. 136 6.2.1.6 Lemmatization.137 6.2.2 Basic Text Analysis Steps.137 6.3 Introduction to NaturalLanguage Processing. 138 6.3.1 Major Components of NLP. 139 6.3.2 Stages of NLP. 140 6.3.3 Statistical Processingof Natural Language.141 6.3.3.1 Document Preprocessing.141 6.3.3.2 Parameterization. 141 6.3.4 Applications of NLP.141 6.4 Summary. 142 References.142 Part III Platforms for Data Science 7 Data Science Tool:
Python. 147 7.1 Basics of Python for Data Science. 147 7.2 Python Libraries: DataFrame Manipulation with pandas and NumPy. 153 7.3 Exploration Data Analysis with Python. 159 7.4 Time Series Data.161 7.5 Clustering with Python. 163 7.6 ARCH and GARCH.168 7.7 Dimensionality Reduction. 170 7.8 Python for Machine ML.174 7.9 KNN/Decision Tree/ Random Forest/SVM.177 7.10 Python IDEs for Data Science.182 7.11 Summary. 183 References.184 8 Data Science Tool: R.187 8.1 Reading and Getting Data into R. 187 8.1.1 Reading Data into R.187 8.1.2 Writing Data into
Files.189 8.1.3 scanQ Function. 190 8.1.4 Built-in Data Sets. 190 8.2 Ordered and Unordered Factors.190
viii Contents 8.3 Arrays and Matrices. 192 8.3.1 Arrays.192 8.3.1.1 Creating an Array.192 8.3.1.2 Accessing Elements in an Array.193 8.3.1.3 Array Manipulation.193 8.3.2 Matrices.194 8.3.2.1 Creating a Matrix. 194 8.3.2.2 Matrix Transpose. 194 8.3.2.3 Eigenvalues and Eigenvectors. 195 8.3.2.4 Matrix Concatenation.195 8.4 Lists and Data Frames. 196 8.4.1 Lists. 196 8.4.1.1 Creating a List. 196 8.4.1.2 Concatenation of Lists.196 8.4.2 Data Frames. 197 8.4.2.1 Creating a Data Frame.197 8.4.2.2 Accessing the Data Frame.197 8.4.2.3 Adding Rows and Columns.198 8.5 Probability Distributions. 198 8.5.1 Normal
Distribution.199 8.6 Statistical Models in R. 201 8.6.1 Model Fitting.202 8.6.2 Marginal Effects. 203 8.7 Manipulating Objects. 203 8.7.1 Viewing Objects. 203 8.7.2 Modifying Objects. 204 8.7.3 Appending Elements. 204 8.7.4 Deleting Objects. 205 8.8 Data Distribution.206 8.8.1 Visualizing Distributions.206 8.8.2 Statistics in Distributions.206 8.9 Summary. 207 References.208 9 Data Science Tool: MATLAB. 209 9.1 Data Science Workflow with MATLAB. 209 9.2 Importing Data.211 9.2.1 How Data is
Stored.211 9.2.2 How MATLAB Represents Data. 213 9.2.3 MATLAB Data Types.214 9.2.4 Automating the Import Process. 215 9.3 Visualizing and Filtering Data.216 9.3.1 Plotting Data Contained in Tables.217 9.3.2 Selecting Data from Tables. 218 9.3.3 Accessing and Creating Table Variables. 219
Contents ix 9.4 Performing Calculations. 220 9.4.1 Basic Mathematical Operations. 220 9.4.2 Using Vectors.222 9.4.3 Using Functions. 223 9.4.4 Calculating Summary Statistics. 224 9.4.5 Correlations between Variables. 226 9.4.6 Accessing Subsets of Data.226 9.4.7 Performing Calculations by Category. 228 9.5 Summary. 230 References.231 10 GNU Octave as a Data Science Tool. 233 10.1 Vectors and Matrices.233 10.2 Arithmetic Operations.238 10.3 Set Operations. 240 10.4 Plotting Data. 242 10.5 Summary. 247 References.248 11
Data Visualization Using Tableau. 249 11.1 Introduction to Data Visualization.249 11.2 Introduction to Tableau.250 11.3 Dimensions and Measures, Descriptive Statistics.252 11.4 Basic Charts. 256 11.5 Dashboard Design Principles.259 11.6 Special Chart Types. 261 11.7 Integrate Tableau with Google Sheets.264 11.8 Summary. 265 References.267 Index. 269 |
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| author | Wagh, Sanjeev J. Bhende, Manisha S. 1977- Thakare, Anuradha D. 1978- |
| author_GND | (DE-588)1212808916 (DE-588)125029441X (DE-588)1250294711 |
| author_facet | Wagh, Sanjeev J. Bhende, Manisha S. 1977- Thakare, Anuradha D. 1978- |
| author_role | aut aut aut |
| author_sort | Wagh, Sanjeev J. |
| author_variant | s j w sj sjw m s b ms msb a d t ad adt |
| building | Verbundindex |
| bvnumber | BV047583121 |
| classification_rvk | ST 530 |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | first edition |
| format | Book |
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Wagh, Manisha S. Bhende, and Anuradha D. Thakare</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">first edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Press, Taylor & Francis Group</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xiv, 282 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">708 grams</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features:Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets.Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. 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| genre | (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV047583121 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:33:47Z |
| indexdate | 2024-07-10T09:15:29Z |
| institution | BVB |
| isbn | 9781138336186 9781032079868 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032968468 |
| oclc_num | 1289323799 |
| open_access_boolean | |
| owner | DE-29T DE-739 DE-573 DE-898 DE-BY-UBR |
| owner_facet | DE-29T DE-739 DE-573 DE-898 DE-BY-UBR |
| physical | xiv, 282 Seiten Illustrationen, Diagramme 708 grams |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| spelling | Wagh, Sanjeev J. Verfasser (DE-588)1212808916 aut Fundamentals of data science Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare first edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2022 xiv, 282 Seiten Illustrationen, Diagramme 708 grams txt rdacontent n rdamedia nc rdacarrier Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processesReaders will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.Features:Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets.Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice.Information is presented in an accessible way for students, researchers and academicians and professionals bisacsh / COMPUTERS / Programming / Games bisacsh / COMPUTERS / Database Management / General Data Science (DE-588)1140936166 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Data Science (DE-588)1140936166 s DE-604 Bhende, Manisha S. 1977- Verfasser (DE-588)125029441X aut Thakare, Anuradha D. 1978- Verfasser (DE-588)1250294711 aut Erscheint auch als Online-Ausgabe 978-0-429-44323-7 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032968468&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wagh, Sanjeev J. Bhende, Manisha S. 1977- Thakare, Anuradha D. 1978- Fundamentals of data science bisacsh / COMPUTERS / Programming / Games bisacsh / COMPUTERS / Database Management / General Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)4151278-9 |
| title | Fundamentals of data science |
| title_auth | Fundamentals of data science |
| title_exact_search | Fundamentals of data science |
| title_exact_search_txtP | Fundamentals of data science |
| title_full | Fundamentals of data science Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare |
| title_fullStr | Fundamentals of data science Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare |
| title_full_unstemmed | Fundamentals of data science Sanjeev J. Wagh, Manisha S. Bhende, and Anuradha D. Thakare |
| title_short | Fundamentals of data science |
| title_sort | fundamentals of data science |
| topic | bisacsh / COMPUTERS / Programming / Games bisacsh / COMPUTERS / Database Management / General Data Science (DE-588)1140936166 gnd |
| topic_facet | bisacsh / COMPUTERS / Programming / Games bisacsh / COMPUTERS / Database Management / General Data Science Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032968468&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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