In this course, you will be introduced to Business Analytics and a hypothesis to understand the concept better. Later, you will learn about important terms employed, such as data collection, specification, and data transformation for analysis purposes. The main focus of the course will be on Principal Component Analysis, popularly transforming large datasets. The hypothesis helps you get familiar with statistical and error concepts in PCA. Lastly, the instructor will brief you with an example of a real-life case study that will help you understand MS Excel's statistical functions.
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In the introduction part, you will understand Business Analytics in brief. Later, you will understand topics such as data collection and specification of data for the analysis model. Lastly, you will learn to apply algorithms to predict models.
In this module, you will familiarize yourself with two types of hypotheses: Null and alternative. Then you will understand two types of errors that come in the application of these hypotheses. Lastly, you will learn about uniform powerful tests of the hypothesis.
This module discusses how you can apply the hypothesis in various exercises. It also covers various scenarios for applying the Null and Alternative hypotheses.
In this module, you will learn about testing with reference to the previously discussed hypothesis in an example of a Marketing Exam. Using these hypotheses, you will learn to analyze the success and failure of a product in the market.
In the final module, you will understand how you can calculate the probability of Null and Alternative hypotheses for different scenarios using MS Excel. You will also learn some useful statistical functions of Excel.
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