Practical Data Science with R Video Edition

Practical Data Science with R Video Edition

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8h 09m | 1.57 GB

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

This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.

Table of Contents

01 The data science process
02 Stages of a data science project
03 Modeling
04 Setting expectations
05 Loading data into R
06 Using R on less-structured data
07 Working with relational databases
08 Loading data from a database into R
09 Exploring data
10 Typical problems revealed by data summaries
11 Spotting problems using graphics and visualization
12 Visually checking distributions for a single variable
13 Visually checking relationships between two variables
14 Managing data
15 Data transformations
16 Sampling for modeling and validation
17 Choosing and evaluating models
18 Solving scoring problems
19 Evaluating models
20 Evaluating scoring models
21 Evaluating probability models
22 Evaluating ranking models
23 Validating models
24 Ensuring model quality
25 Memorization methods
26 Building single-variable models
27 Using cross-validation to estimate effects of overfitting
28 Building models using many variables
29 Using nearest neighbor methods
30 Using Naive Bayes
31 Summary
32 Linear and logistic regression
33 Building a linear regression model
34 Finding relations and extracting advice
35 Reading the model summary and characterizing coefficient quality
36 Statistics as an attempt to correct bad experimental design
37 Using logistic regression
38 Building a logistic regression model
39 Finding relations and extracting advice from logistic models
40 Reading the model summary and characterizing coefficients
41 Null and residual deviances
42 Logistic regression takeaways
43 Unsupervised methods
44 Hierarchical clustering with hclust()
45 Picking the number of clusters
46 The k-means algorithm
47 Association rules
48 Mining association rules with the arules package
49 Association rule takeaways
50 Exploring advanced methods
51 Using bagging to improve prediction
52 Using random forests to further improve prediction
53 Using generalized additive models (GAMs) to learn non-monotone relationships
54 Extracting the nonlinear relationships
55 Using kernel methods to increase data separation
56 Using an explicit kernel on a problem
57 Using SVMs to model complicated decision boundaries
58 Trying an SVM on artificial example data
59 Support vector machine takeaways
60 Documentation and deployment
61 Using knitr to produce milestone documentation
62 Using knitr to document the buzz data
63 Using comments and version control for running documentation
64 Using version control to record history
65 Using version control to explore your project
66 Using version control to share work
67 Deploying models
68 Producing effective presentations
69 Summarizing the project’s goals
70 Presenting your model to end users
71 Presenting your work to other data scientists