Table of Contents

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

2.1 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

3.1 Data collection
3.2 Descriptive statistics
3.3 Probability
3.4 Probability distributions
3.5 Inferential statistics
3.6 Inference for proportions and means

4.1 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

5.1 Data wrangling
5.2 Structuring data
5.3 Cleaning data
5.4 Enriching data

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

7.1 Introduction to regression
7.2 Simple linear regression
7.3 Linear regression assumptions
7.4 Multiple linear regression
7.5 Logistic regression

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

9.1 Introduction to supervised learning
9.2 K-nearest neighbors
9.3 Naive Bayes classification
9.4 Support vector machines

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

11.1 Introduction to decision trees
11.2 Regression trees
11.3 Classification trees
11.4 Random forests

12.1 Introduction to artificial neural networks
12.2 Single-layer perceptron
12.3 Nonlinear activation functions
12.4 Multilayer perceptron

13.1 Introduction to ensemble models
13.2 Boosting
13.3 Bagging
13.4 Stacking

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

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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.

“It is already clear that this represents the future of programming text books. Its basic expository content is the equal of any paper text, but it really shines in using the natural advantages of online vs. static teaching material ­ animation and interactivity ­ to excellent effect, giving the student an additional dimension of insight.”

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