Data science and analytics with Python:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2017]
|
| Series: | Chapman & Hall/CRC data mining and knowledge discovery series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxxv, 376 Seiten Illustrationen 24 cm |
| ISBN: | 9781498742092 9781138043176 |
Staff View
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| adam_text | Contents
Trials and Tribulations of a Data Scientist i
1.1 Data? Science? Data Science! 2
1.1.1 So, What Is Data Science? 3
1.2 The Data Scientist: A Modern Jackalope 7
1.2.1 Characteristics of a Data Scientist and a Data Science Team
1.3 Data Science Tools 17
2.3.1 Open Source Tools 20
1.4 From Data to Insight: the Data Science Workflow 22
1.4.1 Identify the Question 24
7.4.2 Acquire Data 23
1.4.3 DataMunging 25
2.4.4 Modelling and Evaluation 26
1.4.5 Representation and Interaction 26
1.4.6 Data Science: an Iterative Process 27
2.5 Summary 28
X J. ROGEL-SALAZAR
Python: For Something Completely Different
2.1
2.1.1
2.1.2
2.2
2.2.1
2.2.2
2.2.3
2.2.4
2.2.5
2.2.6
2.2.7
2.3
2.3.1
2.3.2
2.3.3
2.3.4
2.3.5
Why Python? Why not?! 33
To Shell or not To Shell 36
iPython/Jupyter Notebook 39
FzVsis Slithers with Python 40
Basic Types 40
Numbers 41
Strings 41
Complex Numbers 43
Lz sfs 44
Tup/es 49
Dictionaries 52
Control Flow 54
if... elif... else 55
while 56
for 57
try... except 58
Functions 61
2.3.6 Scripts and Modules 65
2.4 Computation and Data Manipulation 68
2.4.1 Matrix Manipulations and Linear Algebra
2.4.2 NumPy Arrays and Matrices 71
2.4.3 Indexing and Slicing 74
DATA SCIENCE AND ANALYTICS WITH PYTHON xi
2.5 Pandas to the Rescue 76
2.6 Plotting and Visualising: Matplotlib 81
2.7 Summary 83
The Machine that Goes Ping : Machine Learning and Pattern •
Recognition 87
3.1 Recognising Patterns 87
3.2 Artificial Intelligence and Machine Learning 90
3.3 Data is Good, but other Things are also Needed 92
3.4 Learning, Predicting and Classifying 94
3.5 Machine Learning and Data Science 98
3.6 Feature Selection 100
3.7 Bias, Variance and Régularisation: A Balancing Act 102
3.8 SottiE Useful Measures: Distance and Similarity 105
3.9 Beware the Curse of Dimensionality 110
3.10 Scikit-Learn is our Friend 116
3.11 Training and Testing 119
3.12 Cross-Validation 124
3.32.3 k-fold Cross-Validation 125
3.13 Summary 128
XU J. ROGEL-SALAZAR
The Relationship Conundrum: Regression 131
4.1 Relationships between Variables: Regression 131
4.2 Multivariate Linear Regression 136
4-3 Ordinary Least Squares 138
4.3.1 The Maths Way 139
4.4 Brain and Body: Regression with One Variable 144
4.4.1 Regression with Scikit-learn 133
4-5 Logarithmic Transformation 155
4.6 Making the Task Easier: Standardisation and Scaling
4.6.1 Normalisation or Unit Scaling 161
4.6.2 z-Score Scaling 162
4-7 Polynomial Regression 164
4.7.1 Multivariate Regression 169
4.8 Variance-Bias Trade-Off 170
4.9 Shrinkage: LASSO and Ridge 172
4.10 Summary 179
5 Jackalopes and Hares: Clustering 181
5.2 Clustering 182
5.2 Clustering with k-means 283
5.2.1
5.2.2
Cluster Validation 186
k-means in Action 189
DATA SCIENCE AND ANALYTICS WITH PYTHON Xlll
5.3 Summary 193
Unicorns and Horses: Classification 195
6.1 Classification 196
6.1.1 Confusion Matrices 198
6.1.2 ROCandAUC 202
6.2 Classification with KNN 205
6.2.1 KNN in Action 206
6.3 Classification with Logistic Regression 211
6.3.1 Logistic Regression Interpretation 216
6.3.2 Logistic Regression in Action 218
6.4 Classification with Naïve Bayes 226
6.4.1 Naïve Bayes Classifier 232
6.4.2 Naïve Bayes in Action 233
6.5 Summary 238
Decisions, Decisions: Hierarchical Clustering, Decision Trees and
Ensemble Techniques 241
7.1 Hierarchical Clustering 242
7.1.1 Hierarchical Clustering in Action 245
7.2 Decision Trees 249
7.2.1 Decision Trees in Action 256
XIV J. ROGEL-SALAZAR
7.3 Ensemble Techniques 265
7.3.1 Bagging 271
7.3.2 Boosting 272
7.3.3 Random Forests 274
7.3.4 Stacking and Blending 276
7.4 Ensemble Techniques in Action 277
7.5 Summary 282
8 Less is More: Dimensionality Reduction
8.1 Dimensionality Reduction 286
8.2 Principal Component Analysis 291
8.2.1 PCA in Action 295
8.2.2 PCA in the Iris Dataset 300
8.3 Singular Value Decomposition 304
8.3.1 SVD in Action 306
8.4 Recommendation Systems 310
8.4.1 Content-Based Filtering in Action 312
8.4.2 Collaborative Filtering in Action 316
8.5 Summary 323
9 Kerne/ Tricks up the Sleeve: Support Vector Machines
9.1 Support Vector Machines and Kernel Methods 328
DATA SCIENCE AND ANALYTICS WITH PYTHON XV
9.1.1 Support Vector Machines 331
9.1.2 The Kernel Trick 340
9.1.3 SVM in Action: Regression 343
9.1.4 SVM in Action: Classification 347
9.2 Summary 353
Pipelines in Scikit-Learn 355
Bibliography 361
Index 369
|
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| dewey-full | 006.3/12 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3/12 |
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| discipline | Informatik |
| format | Book |
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| isbn | 9781498742092 9781138043176 |
| language | English |
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| publishDate | 2017 |
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| spelling | Rogel-Salazar, Jesús Verfasser (DE-588)1139793233 aut Data science and analytics with Python Jesús Rogel-Salazar Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2017] xxxv, 376 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC data mining and knowledge discovery series Includes bibliographical references and index Data mining Python (Computer program language) Databases Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Datenbank (DE-588)4011119-2 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Python Programmiersprache (DE-588)4434275-5 s Datenbank (DE-588)4011119-2 s DE-604 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=030320823&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rogel-Salazar, Jesús Data science and analytics with Python Data mining Python (Computer program language) Databases Python Programmiersprache (DE-588)4434275-5 gnd Datenbank (DE-588)4011119-2 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4011119-2 (DE-588)4428654-5 |
| title | Data science and analytics with Python |
| title_auth | Data science and analytics with Python |
| title_exact_search | Data science and analytics with Python |
| title_full | Data science and analytics with Python Jesús Rogel-Salazar |
| title_fullStr | Data science and analytics with Python Jesús Rogel-Salazar |
| title_full_unstemmed | Data science and analytics with Python Jesús Rogel-Salazar |
| title_short | Data science and analytics with Python |
| title_sort | data science and analytics with python |
| topic | Data mining Python (Computer program language) Databases Python Programmiersprache (DE-588)4434275-5 gnd Datenbank (DE-588)4011119-2 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Python (Computer program language) Databases Python Programmiersprache Datenbank Data Mining |
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