Data science and analytics with Python
Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and o...
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| Main Author | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Boca Raton, FL :
CRC Press, Taylor & Francis Group,
[2017]
|
| Series | Chapman & Hall/CRC data mining and knowledge discovery series.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781315151670 1315151677 9781498742115 1498742114 9781523120000 1523120002 9781498742092 1498742092 9781138043176 1138043176 |
| Physical Description | 1 online resource (xxxv, 376 pages) |
Cover
Table of Contents:
- Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; List of Figures; List of Tables; Preface; Readerâ#x80;#x99;s Guide; About the Author; 1: Trials and Tribulations of a Data Scientist; 1.1 Data? Science? Data Science!; 1.1.1 So, What Is Data Science?; 1.2 The Data Scientist: A Modern Jackalope; 1.2.1 Characteristics of a Data Scientist and a Data Science Team; 1.3 Data Science Tools; 1.3.1 Open Source Tools; 1.4 From Data to Insight: the Data Science Workflow; 1.4.1 Identify the Question; 1.4.2 Acquire Data; 1.4.3 Data Munging; 1.4.4 Modelling and Evaluation.
- 1.4.5 Representation and Interaction1.4.6 Data Science: an Iterative Process; 1.5 Summary; 2: Python: For Something Completely Different; 2.1 Why Python? Why not?!; 2.1.1 To Shell or not To Shell; 2.1.2 iPython/Jupyter Notebook; 2.2 Firsts Slithers with Python; 2.2.1 Basic Types; 2.2.2 Numbers; 2.2.3 Strings; 2.2.4 Complex Numbers; 2.2.5 Lists; 2.2.6 Tuples; 2.2.7 Dictionaries; 2.3 Control Flow; 2.3.1 if ... elif ... else; 2.3.2 while; 2.3.3 for; 2.3.4 try ... except; 2.3.5 Functions; 2.3.6 Scripts and Modules; 2.4 Computation and Data Manipulation; 2.4.1 Matrix Manipulations and Linear Algebra.
- 2.4.2 NumPy Arrays and Matrices2.4.3 Indexing and Slicing; 2.5 Pandas to the Rescue; 2.6 Plotting and Visualising: Matplotlib; 2.7 Summary; 3: The Machine that Goes â#x80;#x9C;Pingâ#x80;#x9D;: Machine Learning and Pattern Recognition; 3.1 Recognising Patterns; 3.2 Artificial Intelligence and Machine Learning; 3.3 Data is Good, but other Things are also Needed; 3.4 Learning, Predicting and Classifying; 3.5 Machine Learning and Data Science; 3.6 Feature Selection; 3.7 Bias, Variance and Regularisation: A Balancing Act; 3.8 Some Useful Measures: Distance and Similarity; 3.9 Beware the Curse of Dimensionality.
- 3.10 Scikit-Learn is our Friend3.11 Training and Testing; 3.12 Cross-Validation; 3.12.1 k-fold Cross-Validation; 3.13 Summary; 4: The Relationship Conundrum: Regression; 4.1 Relationships between Variables: Regression; 4.2 Multivariate Linear Regression; 4.3 Ordinary Least Squares; 4.3.1 The Maths Way; 4.4 Brain and Body: Regression with One Variable; 4.4.1 Regression with Scikit-learn; 4.5 Logarithmic Transformation; 4.6 Making the Task Easier: Standardisation and Scaling; 4.6.1 Normalisation or Unit Scaling; 4.6.2 z-Score Scaling; 4.7 Polynomial Regression; 4.7.1 Multivariate Regression.
- 4.8 Variance-Bias Trade-Off4.9 Shrinkage: LASSO and Ridge; 4.10 Summary; 5: Jackalopes and Hares: Clustering; 5.1 Clustering; 5.2 Clustering with k-means; 5.2.1 Cluster Validation; 5.2.2 k-means in Action; 5.3 Summary; 6: Unicorns and Horses: Classification; 6.1 Classification; 6.1.1 Confusion Matrices; 6.1.2 ROC and AUC; 6.2 Classification with KNN; 6.2.1 KNN in Action; 6.3 Classification with Logistic Regression; 6.3.1 Logistic Regression Interpretation; 6.3.2 Logistic Regression in Action; 6.4 Classification with NaÃv̄e Bayes; 6.4.1 NaÃv̄e Bayes Classifier; 6.4.2 NaÃv̄e Bayes in Action.