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
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
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
5. Data Wrangling
5.1 Data wrangling
5.2 Structuring data
5.3 Cleaning data
5.4 Enriching data
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: 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
8. Evaluating Model Performance
8.1 Model error
8.2 Binary classification metrics
8.3 Regression metrics
8.4 Training, validation, and test sets
8.5 Cross-validation
8.6 Bootstrap method
8.7 Comparing models
9. Supervised Learning
9.1 Introduction to supervised learning
9.2 K-nearest neighbors
9.3 Naive Bayes classification
9.4 Support vector machines
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
11. Decision Trees
11.1 Introduction to decision trees
11.2 Regression trees
11.3 Classification trees
11.4 Random forests
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
13. Ensemble Techniques
13.1 Introduction to ensemble models
13.2 Boosting
13.3 Bagging
13.4 Stacking
14. Appendix
14.1 Datasets: CSV files
What You’ll Find In This zyBook:
More action with less text.
- Builds student understanding and confidence through learning questions and coding activities
- Students learn the necessary skills required for the more quantitative and technical aspects of data science and machine learning
- Each section covers topics from a conceptual standpoint without assuming prerequisite knowledge in statistics and programming
- Jupyter Notebooks integration allows students to write and edit live code, create data visualizations, and experiment by changing the parameters of different models
- Test bank with more than 330 questions
The zyBooks Approach
Less text doesn’t mean less learning.
The Data Science Foundations with Python zyBook provides an interactive introduction to common algorithms and techniques in data science. This zyBook covers data preprocessing, regression techniques, supervised and unsupervised learning algorithms, decision trees, neural networks, ensemble methods, and model evaluation techniques.
Authors
Chris Chan
Senior Manager, Content Development in Math, Stats, and Data Science / zyBooks / M.A. in Mathematics / San Francisco State University
Matt Rissler
Data Science Content Developer / Ph.D. in Mathematics / University of Notre Dame
Aimee Schwab-McCoy
Data Science Content Developer / Ph.D. in Statistics / University of Nebraska–Lincoln