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