- Model Evaluation
- Statistical Modeling
- Feature Engineering
- Predictive Analytics
- R Programming
- Data Wrangling
- Forecasting
- Data Preprocessing
- Data Cleansing
- Regression Analysis
- Data Manipulation
- Exploratory Data Analysis
Data Analysis with R
Completed by JOHN CARLS CAYANGA VARGAS
April 6, 2022
16 hours (approximately)
JOHN CARLS CAYANGA VARGAS's account is verified. Coursera certifies their successful completion of Data Analysis with R
What you will learn
Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.
Compare and contrast predictive models using simple linear, multiple linear, and polynomial regression methods.
Examine data using descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics.
Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.
Skills you will gain
