Data Science and Data Engineering for Architects

Outline

Chapter 1. Python for Data Science

• Python Data Science-Centric Libraries
• SciPy
• NumPy
• pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Python Dev Tools and REPLs
• IPython
• Jupyter Notebooks
• Anaconda
• Summary

Chapter 2. Data Visualization in Python

• Why Do I Need Data Visualization?
• Data Visualization in Python
• Getting Started with matplotlib
• A Basic Plot
• Scatter Plots
• Figures
• Saving Figures to a File
• Seaborn
• Getting Started with seaborn
• Histograms and KDE
• Plotting Bivariate Distributions
• Scatter Plots in seaborn
• Pair plots in seaborn
• Heatmaps
• A Seaborn Scatterplot with Varying Point Sizes and Hues
• Summary

Chapter 3. Introduction to NumPy

• What is NumPy?
• The First Take on NumPy Arrays
• The ndarray Data Structure
• Understanding Axes
• Indexing Elements in a NumPy Array
• Re-Shaping
• Commonly Used Array Metrics
• Commonly Used Aggregate Functions
• Sorting Arrays
• Vectorization
• Vectorization Visually
• Broadcasting
• Broadcasting Visually
• Filtering
• Array Arithmetic Operations
• Reductions: Finding the Sum of Elements by Axis
• Array Slicing
• 2-D Array Slicing
• The Linear Algebra Functions
• Summary

Chapter 4. Introduction to pandas

• What is pandas?
• The DataFrame Object
• The DataFrame's Value Proposition
• Creating a pandas DataFrame
• Getting DataFrame Metrics
• Accessing DataFrame Columns
• Accessing DataFrame Rows
• Accessing DataFrame Cells
• Deleting Rows and Columns
• Adding a New Column to a DataFrame
• Getting Descriptive Statistics of DataFrame Columns
• Getting Descriptive Statistics of DataFrames
• Sorting DataFrames
• Reading From CSV Files
• Writing to a CSV File
• Summary

Chapter 5. Repairing and Normalizing Data

• Repairing and Normalizing Data
• Dealing with the Missing Data
• Sample Data Set
• Getting Info on Null Data
• Dropping a Column
• Interpolating Missing Data in pandas
• Replacing the Missing Values with the Mean Value
• Scaling (Normalizing) the Data
• Data Preprocessing with scikit-learn
• Scaling with the scale() Function
• The MinMaxScaler Object
• Summary

Chapter 6. Defining Data Science

• What is Data Science?
• Data Science, Machine Learning, AI?
• The Data Science Ecosystem
• Tools of the Trade
• The Data-Related Roles
• Data Scientists at Work
• Examples of Data Science Projects
• The Concept of a Data Product
• Applied Data Science at Google
• Data Science and ML Terminology: Features and Observations
• Terminology: Labels and Ground Truth
• Label Examples
• Terminology: Continuous and Categorical Features
• Encoding Categorical Features using One-Hot Encoding Scheme
• Example of 'One-Hot' Encoding Scheme
• Gartner's Magic Quadrant for Data Science and Machine Learning Platforms (a Labeling Example)
• Machine Learning in a Nutshell
• Common Distance Metrics
• The Euclidean Distance
• Decision Boundary Examples (Object Classification)
• What is a Model?
• Training a Model to Make Predictions
• Types of Machine Learning
• Supervised vs Unsupervised Machine Learning
• Supervised Machine Learning Algorithms
• Unsupervised Machine Learning Algorithms
• Which ML Algorithm to Choose?
• Bias-Variance (Underfitting vs Overfitting) Trade-off
• Underfitting vs Overfitting (a Regression Model Example) Visually
• ML Model Evaluation
• Mean Squared Error (MSE) and Mean Absolute Error (MAE)
• Coefficient of Determination
• Confusion Matrix
• The Binary Classification Confusion Matrix
• The Typical Machine Learning Process
• A Better Algorithm or More Data?
• The Typical Data Processing Pipeline in Data Science
• Data Discovery Phase
• Data Harvesting Phase
• Data Cleaning/Priming/Enhancing Phase
• Exploratory Data Analysis and Feature Selection
• Exploratory Data Analysis and Feature Selection Cont'd
• ML Model Planning Phase
• Feature Engineering
• ML Model Building Phase
• Capacity Planning and Resource Provisioning
• Communicating the Results
• Production Roll-out
• Data Science Gotchas
• Summary

Chapter 7. Overview of the scikit-learn Library

• The scikit-learn Library
• The Navigational Map of ML Algorithms Supported by scikit-learn
• Developer Support
• scikit-learn Estimators, Models, and Predictors
• Annotated Example of the LinearRegression Estimator
• Annotated Example of the Support Vector Classification Estimator
• Data Splitting into Training and Test Datasets
• Data Splitting in scikit-learn
• Cross-Validation Technique
• Summary

Chapter 8. Classification Algorithms (Supervised Machine Learning)

• Classification (Supervised ML) Use Cases
• Classifying with k-Nearest Neighbors
• k-Nearest Neighbors Algorithm Visually
• Decision Trees
• Decision Tree Terminology
• Decision Tree Classification in the Context of Information Theory
• Using Decision Trees
• Properties of the Decision Tree Algorithm
• The Simplified Decision Tree Algorithm
• Random Forest
• Properties of the Random Forest Algorithm
• Support Vector Machines (SVMs)
• SVM Classification Visually
• Properties of SVMs
• Dealing with Non-Linear Class Boundaries
• Logistic Regression (Logit)
• The Sigmoid Function
• Logistic Regression Classification Example
• Logistic Regression's Problem Domain
• Naive Bayes Classifier (SL)
• Naive Bayesian Probabilistic Model in a Nutshell
• Bayes Formula
• Document Classification with Naive Bayes
• Summary

Chapter 9. Unsupervised Machine Learning Algorithms

• PCA
• PCA and Data Variance
• PCA Properties
• Importance of Feature Scaling Visually
• Unsupervised Learning Type: Clustering
• Clustering vs Classification
• Clustering Examples
• k-means Clustering
• k-means Clustering in a Nutshell
• k-means Characteristics
• Global vs Local Minimum Explained
• Summary

Lab Exercises

• Lab 1. Learning the CoLab Jupyter Notebook Environment
• Lab 2. Data Visualization in Python
• Lab 3. Understanding NumPy
• Lab 4. Data Repairing
• Lab 5. Understanding Common Metrics
• Lab 6. Coding kNN Algorithm in NumPy (Optional)
• Lab 7. Understanding Machine Learning Datasets in scikit-learn
• Lab 8. Building Linear Regression Models
• Lab 9. Spam Detection with Random Forest
• Lab 10. Spam Detection with Support Vector Machines
• Lab 11. Spam Detection with Logistic Regression
• Lab 12. Comparing Classification Algorithms
• Lab 13. Feature Engineering and EDA
• Lab 14. Understanding PCA