Business Analytics and Machine Learning with R programming
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Price: 19.99$
This course introduces techniques of Business Analytics to transform data into business intelligence and to use analytics to create business value. Students learn to develop solutions to real-world problems through a combination of videos, case studies, technology demonstrations to analyze and interpret real data. This course consists of four 4 sections: Business Analytics, Statistics, Programming in R, and Case Study. A. Business Analytics Cross-industry standard process for data mining (CRISP-DM) is explained. CRISP-DM breaks the process of data mining or analytics into six major phases:· Business Understanding· Data Understanding· Data Preparation· Modeling· Evaluation· Deployment B. Statistics Analytics professionals need to be trained to use statistical methods not only to interpret numbers but to predict future business scenarios. Statistics is a set of mathematical methods and tools that enable us to answer important questions about data. It is divided into two categories:1. Descriptive Statistics2. Inferential Statistics Statistics and machine learning are two closely related areas. Statistics is an important prerequisite for applied machine learning. It helps us select, evaluate and interpret predictive models. Upon completion of this section, you will be able to:· Define a variety of basic statistical terms and concepts· Perform fundamental statistical calculations· Use your understanding of statistical fundamentals to interpret data C. Programming in RIn this section, you wil learn the fundamentals of R. You will learn how to use R Studio by using tools and packages like Tidyverse, Data Frames, Tibbles, operators, expressions, and data visualization, graphs, plots, and charts. Finally, you will apply your skills to guided examples involving business scenarios D. Case Study With two case studies, you will practice machine learning techniques.
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