MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02572cam a22002298i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780367609504 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.452 HUA.D |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Huang, Shuai |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Data analytics : |
| Remainder of title |
a small data approach / |
| Statement of responsibility, etc. |
Shuai Huang & Houtao Deng. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Boca Raton: |
| Name of publisher, distributor, etc. |
CRC Press, |
| Date of publication, distribution, etc. |
2021. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv;257p.: |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Abstraction -- Recognition -- Resonance -- Learning (I) -- Diagnosis -- Learning (II) -- Scalability : LASSO & PCA -- Pragmatism -- Synthesis : architecture & pipeline. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from established R packages. Also, as data science practice is a process that should be told as a story, in this book there are many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines. The main models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three techniques. For each technique, the book highlights the intuition and rationale first, then shows how mathematics is used to articulate the intuition and formulate the learning problem. R is used to implement the techniques on both simulated and real-world dataset. Python code is also available at the book's website: http://dataanalyticsbook.info. Shuai Huang is an associate professor at the department of industrial & systems engineering at the university of Washington. He conducts interdisciplinary research in machine learning, data analytics, and applied operations research with applications on healthcare, manufacturing, and transportation areas. Houtao Deng is a data science researcher and practitioner. He developed several new decision tree methods such as inTrees. He has built data-driven products for forecasting, scheduling, pricing, recommendation, fraud detection, and image recognition"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Quantitative research. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Quantitative research |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
R (Computer program language) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language) |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Deng, Houtao, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
