Business Analytics with R | Get Hands-on with Business Analytics
Business Analytics with R Course Description
Business Analytics with R Course Learning Outcomes
- Optimize business situations that involve whole numbers, such as employees to deploy.
- Optimize business decisions that take multiple input variables to predict between two possible outputs.
- Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to risks.
- Compute correlation where, at first glance, there seem to be none – correlation between data points in a time series.
- Compute the regression model for time series data that has correlation within itself.
- Optimize business situations where two variables do not move in a linear fashion.
- Test hypothesis for experiments involving different treatments.
- Model continuous outcomes that depend on more than one input variable.
- Group data points dynamically based on the similarities among the members of each group.
Business Analytics with R Training - Suggested Audience
- Software Engineers
- Data analytics professionals
- Data Analysts
- Business Analysts
- Research professionals
Business Analytics with R Training Duration
- Open-House F2F (Public): 5 days
- In-House F2F (Private): 4/5 days, for commercials please send us an email with group size to email@example.com
Business Analytics with R Training - Prerequisites
- Get introduced to the basics, evolution, and scope of business analytics.
- Learn R and data manipulation, functions and data visualizations in R.
- Understand the various types and applications of statistics as well as the types of data and statistics variables.
- Master the art of making informed decisions using summary statistics.
- Learn random variables, expected value, probability distribution, standard deviation, variance and the types of distributions.
- Learn how to state null and alternative hypotheses, understanding Type-I and Type-II errors. Conduct one-sided hypothesis test for population.
- Apply correlation, strength of linear association, least-squares or regression line, linear regression model
- Gain expertise in multiple regression, regression diagnostics and detection of collinearity: simple signs.
- Learn about fitting of model, diagnostic plots, comparison of models, cross-validation, variable selection, relative importance and Box-Cox transformations.
- Master binary response regression model and linear regression output of proposed model.
- Work on the various problems with linear probability model, logistic function, logistic regression & its interpretation and the various odds ratio, goodness of fit measures and confusion matrix.
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