Table of Contents
1. Introduction to Data Science
1.1 Historical overview
1.2 Why data science?
1.3 Careers in data science
1.4 Data science lifecycle
1.5 Ethics in data science
1.6 Case study: Netflix
2. Python for Data Science
2.1 Programming with Python and Jupyter
2.2 Python data types
2.3 Python functions
2.4 Data science packages
2.5 NumPy package
2.6 pandas package
2.7 matplotlib package
2.8 Case study: Hawks
3. Probability and Statistics
3.1 Data collection
3.2 Descriptive statistics
3.3 Probability
3.4 Probability distributions
3.5 Inferential statistics
3.6 Inference for proportions and means
3.7 Case study: Flight delays
4. SQL for Data Science
4.1 Relational databases
4.2 Simple queries
4.3 Special operators and clauses
4.4 Aggregate functions
4.5 Join queries
4.6 Subqueries
4.7 Queries in Python
4.8 Case study: Queries in SQL and pandas
5. Data Wrangling
5.1 Data wrangling
5.2 Manipulating data
5.3 Structuring data
5.4 Cleaning data
5.5 Enriching data
5.6 Case study: Diamond prices
6. Data Exploration
6.1 Visualizing data with one feature
6.2 Visualizing data with multiple features
6.3 Best practices for visualizing data
6.4 Tools for visualizing data
6.5 Performing exploratory data analysis
6.6 Detecting outliers
6.7 Case study: Palmer penguins
7. Regression
7.1 Introduction to regression
7.2 Simple linear regression
7.3 Linear regression assumptions
7.4 Multiple linear regression
7.5 Logistic regression
7.6 Case study: Energy consumption
7.7 Case study: Customer churn
8. Evaluating Model Performance
8.1 Model error
8.2 Training, validation, and test sets
8.3 Loss functions for regression
8.4 Loss functions for classification
8.5 Binary classification metrics
8.6 Cross-validation
8.7 Bootstrap method
8.8 Comparing models
8.9 Case study: Home prices
9. Supervised Learning
9.1 Introduction to supervised learning
9.2 K-nearest neighbors
9.3 Naive Bayes classification
9.4 Support vector machines
9.5 Case study: Classifying cells
10. Unsupervised Learning
10.1 Introduction to unsupervised learning
10.2 K-means clustering
10.3 Hierarchical clustering
10.4 Detecting outliers using DBSCAN
10.5 Analyzing factors
10.6 Analyzing factors using PCA
10.7 Case study: Travel reviews
11. Decision Trees
11.1 Introduction to decision trees
11.2 Regression trees
11.3 Classification trees
11.4 Random forests
11.5 Case study: Marijuana legalization
12. Artificial Neural Networks
12.1 Introduction to artificial neural networks
12.2 Single-layer perceptron
12.3 Nonlinear activation functions
12.4 Multilayer perceptron
12.5 Case study: Bike share demand
13. Ensemble Techniques
13.1 Introduction to ensemble models
13.2 Boosting
13.3 Bagging
13.4 Stacking
13.5 Case study: Bob Ross
14. Appendix
14.1 Datasets: CSV files
Teach data science with Python with the only interactive introduction that’s fully integrated with Jupyter Notebooks
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Data science is interactive; it requires coding and live investigations of data sets. To do all that within a digital zyBook is really powerful.”
– Co-author Dr. Aimee Schwab-McCoy
Dr. Schwab-McCoy explains the benefits of zyBooks for data science instructors and students:
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Embedded Jupyter Notebooks
The Data Science Foundations with Python zyBook is fully integrated with the industry standard Jupyter Notebooks web-based computing platform. So students will gain real-life experience writing and editing live code, creating data visualizations, and experimenting by changing parameters of different models to evaluate their performance with a professional application.
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Authors
Chris Chan
Senior Manager, Content Development in Math, Stats, and Data Science / zyBooks / MA in Mathematics / San Francisco State University
Matt Rissler
Data Science Content Developer / PhD in Mathematics / University of Notre Dame
Aimee Schwab-McCoy
Data Science Content Developer / PhD in Statistics / University of Nebraska–Lincoln
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