Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
Tiffany Timbers is an Assistant Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. Trevor Campbell is an Assistant Professor in the Department of Statistics at the University of British Columbia. Melissa Lee is an Assistant Professor of Teaching in the Department of Statistics at the University of British Columbia
Inhaltsangabe
1. R and the tidyverse 2. Reading in data locally and from the web 3. Cleaning and wrangling data 4. Effective data visualization 5. Classification I: training & predicting 6. Classification II: evaluation & tuning 7. Regression I: K-nearest neighbors 8. Regression II: linear regression 9. Clustering 10. Statistical inference 11. Combining code and text with Jupyter 12. Collaboration with version control 13. Setting up your computer
1. R and the tidyverse 2. Reading in data locally and from the web 3. Cleaning and wrangling data 4. Effective data visualization 5. Classification I: training & predicting 6. Classification II: evaluation & tuning 7. Regression I: K-nearest neighbors 8. Regression II: linear regression 9. Clustering 10. Statistical inference 11. Combining code and text with Jupyter 12. Collaboration with version control 13. Setting up your computer
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