Ementorhub

What you'll 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'll gain

Box Plots

Data Manipulation

Statistical Modeling

Correlation Analysis

Tidyverse (R Package)

Data Wrangling

Statistical Analysis

Supervised Learning

Data Visualization

Data Analysis

Data Cleansing

Exploratory Data Analysis

Regression Analysis

R Programming

Data Transformation

Predictive Analytics

There are 6 modules in this course

You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.

Data Wrangling

Exploratory Data Analysis

Model Development in R

Model Evaluation

Project