Practical Data Science with R
I started reading the book Practical Data Science with R, 2nd Edition.
Progress
PART 1: INTRODUCTION TO DATA SCIENCE
- 1 The data science process
- 2 Starting with R and data
- 3 Exploring data
- 4 Managing data
- 5 Data Engineering and Data Shaping
PART 2: MODELING METHODS
- 6 Choosing and evaluating models 7 Linear and logistic regression
- 8 Advanced Data Preparation
- 9 Unsupervised methods
- 10 Exploring advanced methods
PART 3: WORKING IN THE REAL WORLD
- 11 Documentation and deployment
- 12 Producing effective presentations
APPENDIXES:
- A Working with R and other tools
- B Important statistical concepts
- C Bibliography
Notes
Most common data science modeling tasks:
- Classification—Deciding if something belongs to one category or another
- Scoring—Predicting or estimating a numeric value, such as a price or probability
- Ranking—Learning to order items by preferences
- Clustering—Grouping items into most-similar groups
- Finding relations—Finding correlations or potential causes of effects seen in the data
- Characterization—Very general plotting and report generation from data