Data Science and Machine Learning with R (Part #4) – Hypothesis Testing, Forecasting and Visualisation with R
In the recent years R has become the widely used programming language for computational statistics, visualization and data science. R is used by many statisticians, scientists and data analysts to find a solution for their problem in various fields. R is used as the most important tool for business analytics in companies like Google, Facebook and LinkedIn. R contains every data analytics techniques at your fingertips. It helps you to perform some of the most commonly performed tasks by business analysts with the help of its 4000 packages.
Through this course we will learn about R examples, standard error of the mean, downloading the package, sample differences, hypothesis generation & testing, hypothesis testing, one sided p value, power & sample size, testing hypothesis using r, calculating the z value, lower tail proportion of population proportion, forecasting, time series analysis applications, approaches to forecasting, observation components, traditional approaches, double exponential smoothing, arima steps, forecasting performance, univariate arima, r visualization, why visualize, overlaying plots, graphs representation of data, advanced graphs, bubble charts, anova, concept of effect, estimate of treatment effect, factorial anova, regression, regression model, linear relationship, output of regression model.
This course will be useful for students and Data analysts and people from Analytics domain. It will also be useful for people who wanted to learn business analytics using R.
The pre requisites for this course include basic knowledge of statistics. Knowledge in any other programming language is an added advantage but not a must.