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Business Analytics with Predictive modeling by Princeton Academy

Address: Barakhamba Avenue, Near Modern School, Connaught Place Fire Brigade Lane, Barakhamba New Delhi, Delhi 110001

Program Overview Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges. The employment potential in Data Analytics and Big Data comes in the form of the very highly skilled jobs for data analysts/data scientist’s proficient in areas such as mathematics, statistics, economics, engineering and management science and with the associated IT skills required to mine and analyze the data concerned. Along with the demand for big data, better data, and the need for greater insight into organizations operations comes the need for analytic professionals who can effectively leverage this data to maximize business benefits.   Program Objective Understanding of Business Analytics How could it be applied in Organization? How you can initiate Business Analysis Project? How to use Statistics for Predictive Analytics? Course Outline TOPIC 1: SIMPLE REGRESSION Descriptive Statistics Central Limit Theorem Sampling and Hypothesis Testing How Regression works Case Study on Predicting Sales from Ads Evaluating a regression model Doubt Clarifications: Q&A TOPIC 2: MULTIPLE REGRESSION Bringing in more factors to make a prediction Dummy variable regression Interpreting a multiple regression output Case study on setting the sales target for cross-country sales teams Evaluating a multiple regression model Improving model performance Doubt Clarifications: Q&A TOPIC 3: LOGISITIC REGRESSION Multiple Regression Vs Logistic Regression Interpreting the output of a Logistic Regression Business Use cases across multiple industries Case Study on Applying logistic regression in stock markets Evaluating a logistic regression model Doubt Clarifications: Q&A TOPIC 4: BUSINESS FORECASTING Simple Moving Average Exponential Moving Average ARMA Models ARIMA Models Doubt Clarifications: Q&A


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