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 Interaction 

     1.4.6 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 Matrices 

         2.4.3 Indexing and Slicing 

         2.5 Pandas to the Rescue 

         2.6 Plotting and Visualizing: Matplotlib 

         2.7 Summary  

3 The Machine that Goes “Ping”: Machine Learning and Pattern Recognition 

     3.1 Recognizing 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 100

     3.7 Bias, Variance and Regularizations: A Balancing Act 

     3.8 Some Useful Measures: Distance and Similarity 

     3.9 Beware the Curse of Dimensionality

     3.10 Scikit-Learn is our Friend 

     3.11 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-Off 

    4.9 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 Naïve Bayes 

         6.4.1 Naïve Bayes Classifier 

         6.4.2 Naïve Bayes in Action 

     6.5 Summary  

7 Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensemble Techniques 

     7.1 Hierarchical Clustering 

          7.1.1 Hierarchical Clustering in Action 

     7.2 Decision Trees 

          7.2.1 Decision Trees in Action 

     7.3 Ensemble Techniques 

          7.3.1 Bagging 

          7.3.2 Boosting 

          7.3.3 Random Forests 

          7.3.4 Stacking and Blending 

     7.4 Ensemble Techniques in Action 

     7.5 Summary  

8 Less is More: Dimensionality Reduction 

     8.1 Dimensionality Reduction 

     8.2 Principal Component Analysis 

          8.2.1 PCA in Action 

          8.2.2 PCA in the Iris Dataset 

     8.3 Singular Value Decomposition 

          8.3.1 SVD in Action 

     8.4 Recommendation Systems 

          8.4.1 Content-Based Filtering in Action 

          8.4.2 Collaborative Filtering in Action 

      8.5 Summary 

9 Kernel Tricks up the Sleeve: Support Vector Machines 

     9.1 Support Vector Machines and Kernel Methods   

          9.1.1 Support Vector Machines 

          9.1.2 The Kernel Trick 

          9.1.3 SVM in Action: Regression 

          9.1.4 SVM in Action: Classification 

       9.2 Summary  

10 Pipelines in Scikit-Learn