Nguồn ngoài

Principles of Data Science

Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important.

Data Science Engineering

Tác giả: OpenStax
Nhà xuất bản: LibreTexts
Nguồn: LibreTexts
Giấy phép: CC BY
Ngôn ngữ: EN
Danh mục: Công nghệ thông tin

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Mục lục· 122 mục

01: What Are Data and Data Science
1.00: Introduction
1.01: What Is Data Science
1.02: Data Science in Practice
1.03: Data and Datasets
1.04: Using Technology for Data Science
1.05: Data Science with Python
1.06: Key Terms
1.07: Group Project
1.08: Chapter Review
1.09: Critical Thinking
1.10: Quantitative Problems
1.11: References
02: Collecting and Preparing Data
2.00: Introduction
2.01: Overview of Data Collection Methods
2.02: Survey Design and Implementation
2.03: Web Scraping and Social Media Data Collection
2.04: Data Cleaning and Preprocessing
2.05: Handling Large Datasets
2.06: Key Terms
2.07: Group Project
2.08: Critical Thinking
2.09: References
03: Descriptive Statistics- Statistical Measurements and Probability Distributions
3.00: Introduction
3.01: Measures of Center
3.02: Measures of Variation
3.03: Measures of Position
3.04: Probability Theory
3.05: Discrete and Continuous Probability Distributions
3.06: Key Terms
3.07: Group Project
3.08: Quantitative Problems
04: Inferential Statistics and Regression Analysis
4.00: Introduction
4.01: Statistical Inference and Confidence Intervals
4.02: Hypothesis Testing
4.03: Correlation and Linear Regression Analysis
4.04: Analysis of Variance (ANOVA)
4.05: Key Terms
4.06: Group Project
4.07: Quantitative Problems
05: Time Series and Forecasting
5.00: Introduction
5.01: Introduction to Time Series Analysis
5.02: Components of Time Series Analysis
5.03: Time Series Forecasting Methods
5.04: Forecast Evaluation Methods
5.05: Key Terms
5.06: Group Project
5.07: Critical Thinking
5.08: Quantitative Problems
06: Decision-Making Using Machine Learning Basics
6.00: Introduction
6.01: What Is Machine Learning
6.02: Classification Using Machine Learning
6.03: Machine Learning in Regression Analysis
6.04: Decision Trees
6.05: Other Machine Learning Techniques
6.06: Key Terms
6.07: Group Project
6.08: Chapter Review
6.09: Critical Thinking
6.10: Quantitative Problems
6.11: References
07: Deep Learning and AI Basics
7.00: Introduction
7.01: Introduction to Neural Networks
7.02: Backpropagation
7.03: Introduction to Deep Learning
7.04: Convolutional Neural Networks
7.05: Natural Language Processing
7.06: Key Terms
7.07: Group Project
7.08: Chapter Review
7.09: Critical Thinking
7.10: Quantitative Problems
7.11: References
08: Ethics Throughout the Data Science Cycle
8.00: Introduction
8.01: Ethics in Data Collection
8.02: Ethics in Data Analysis and Modeling
8.03: Ethics in Visualization and Reporting
8.04: Key Terms
8.05: Group Project
8.06: Chapter Review
8.07: Critical Thinking
8.08: References
09: Visualizing Data
9.00: Introduction
9.01: Encoding Univariate Data
9.02: Encoding Data That Change Over Time
9.03: Graphing Probability Distributions
9.04: Geospatial and Heatmap Data Visualization Using Python
9.05: Multivariate and Network Data Visualization Using Python
9.06: Key Terms
9.07: Group Project
9.08: Critical Thinking
10: Reporting Results
10.00: Introduction
10.01: Writing an Informative Report
10.02: Validating Your Model
10.03: Effective Executive Summaries
10.04: Key Terms
10.05: Group Project
10.06: Chapter Review
10.07: Critical Thinking
10.08: References
11: Appendix
11.00: Appendix A- Review of Excel for Data Science
11.01: Appendix B- Review of R Studio for Data Science
11.02: Appendix C- Review of Python Algorithms
11.03: Appendix D- Review of Python Functions
12: Answer Key
12.00: Chapter 1
12.01: Chapter 3
12.02: Chapter 5
12.03: Chapter 6
12.04: Chapter 7
12.05: Chapter 8
12.06: Chapter 10