Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
"A unique and important addition to any data scientist’s library."
Jim Porzak, Cofounder Bay Area R Users Group
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. It shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.
Inside:
- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations
Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.
Covers the process end-to-end, from data exploration to modeling to delivering the results.
Nezih Yigitbasi, Intel
Full of useful gems for both aspiring and experienced data scientists.
Fred Rahmanian, Siemens Healthcare
Hands-on data analysis with real-world examples. Highly recommended.
Dr. Kostas Passadis, IPTO
NARRATED BY JOSEF GAGNIER
Table of contents
- Chapter 1. The data science process
- Chapter 1. Stages of a data science project
- Chapter 1. Modeling
- Chapter 1. Setting expectations
- Chapter 2. Loading data into R
- Chapter 2. Using R on less-structured data
- Chapter 2. Working with relational databases
- Chapter 2. Loading data from a database into R
- Chapter 3. Exploring data
- Chapter 3. Typical problems revealed by data summaries
- Chapter 3. Spotting problems using graphics and visualization
- Chapter 3. Visually checking distributions for a single variable
- Chapter 3. Visually checking relationships between two variables
- Chapter 4. Managing data
- Chapter 4. Data transformations
- Chapter 4. Sampling for modeling and validation
- Chapter 5. Choosing and evaluating models
- Chapter 5. Solving scoring problems
- Chapter 5. Evaluating models
- Chapter 5. Evaluating scoring models
- Chapter 5. Evaluating probability models
- Chapter 5. Evaluating ranking models
- Chapter 5. Validating models
- Chapter 5. Ensuring model quality
- Chapter 6. Memorization methods
- Chapter 6. Building single-variable models
- Chapter 6. Using cross-validation to estimate effects of overfitting
- Chapter 6. Building models using many variables
- Chapter 6. Using nearest neighbor methods
- Chapter 6. Using Naive Bayes
- Chapter 6. Summary
- Chapter 7. Linear and logistic regression
- Chapter 7. Building a linear regression model
- Chapter 7. Finding relations and extracting advice
- Chapter 7. Reading the model summary and characterizing coefficient quality
- Chapter 7. Statistics as an attempt to correct bad experimental design
- Chapter 7. Using logistic regression
- Chapter 7. Building a logistic regression model
- Chapter 7. Finding relations and extracting advice from logistic models
- Chapter 7. Reading the model summary and characterizing coefficients
- Chapter 7. Null and residual deviances
- Chapter 7. Logistic regression takeaways
- Chapter 8. Unsupervised methods
- Chapter 8. Hierarchical clustering with hclust()
- Chapter 8. Picking the number of clusters
- Chapter 8. The k-means algorithm
- Chapter 8. Association rules
- Chapter 8. Mining association rules with the arules package
- Chapter 8. Association rule takeaways
- Chapter 9. Exploring advanced methods
- Chapter 9. Using bagging to improve prediction
- Chapter 9. Using random forests to further improve prediction
- Chapter 9. Using generalized additive models (GAMs) to learn non-monotone relationships
- Chapter 9. Extracting the nonlinear relationships
- Chapter 9. Using kernel methods to increase data separation
- Chapter 9. Using an explicit kernel on a problem
- Chapter 9. Using SVMs to model complicated decision boundaries
- Chapter 9. Trying an SVM on artificial example data
- Chapter 9. Support vector machine takeaways
- Chapter 10. Documentation and deployment
- Chapter 10. Using knitr to produce milestone documentation
- Chapter 10. Using knitr to document the buzz data
- Chapter 10. Using comments and version control for running documentation
- Chapter 10. Using version control to record history
- Chapter 10. Using version control to explore your project
- Chapter 10. Using version control to share work
- Chapter 10. Deploying models
- Chapter 11. Producing effective presentations
- Chapter 11. Summarizing the project’s goals
- Chapter 11. Presenting your model to end users
- Chapter 11. Presenting your work to other data scientists
Product information
- Title: Practical Data Science with R video edition
- Author(s):
- Release date: March 2014
- Publisher(s): Manning Publications
- ISBN: None
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