Textbook Study for R, Python

Ⅰ. R

1st: R for Data Science (R4DS)

- Explore

1. Data visualisation
2. Workflow: basics
3. Data transformation
4. Workflow: scripts
5. Exploratory data analysis
6. Workflow: projects

- Wrangle

7. Tibbles
8. Data import_readr
9. Tidy data_tidyr
10. Relational data_dplyr
11. Strings_stringr
12. Factors_forcats
13. Dates and times_lubridateb

- Program

14. Pipes_magrittr
15. Functions
16. Vectors
17. Iteration_purrr

- Model

18. Model basics_modelr
19. Model building
20. Many models_purrr&broom

- Communicate

21. R Markdown
22. Graphics for communication
23. R markdown formats
24. R markdown workflow

2nd: Discovering Statistics Using R

5. Exploring assumptions
6. Correlation
7. Regression

Ⅱ. Python

3rd: 10 minutes to Pandas

1. Object Creation
2. Viewing Data
3. Selection
4. Missing Data
5. Operation
6. Merge
7. Grouping
8. Reshaping
9. Time Series
10. Categoricals
11. Plotting
12. Getting Data In / Out
13. Gotchas

4th: Python Machine Learning Guide

1. 파이썬 기반의 머신러닝과 생태계 이해
2. 사이킷런으로 시작하는 머신러닝
3. 평가
4. 분류
5. 회귀
6. 차원 축소

5th: Natural Language Processing

2. 자연어 처리 개발 준비
3. 자연어 처리 개요
4. 텍스트 분류
5. 텍스트 유사도
6. 사전 학습 모델