Swain Center for Executive Education

Business Analytics: Predictive Analytics

Dates:November 13, 2020 Other Dates
Meets:1:00 PM to 5:00 PM
Instructor:Peter Schuhmann
Fee: $399.00

For inquires contact the Swain Center at 910-962-2728.

There are still openings remaining at this time.

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Description

Businesses have access to more data than ever before. Predictive analytics is the process of using historical data to create actionable insights about future events or outcomes. There are several approaches to predictive analytics, including classification modeling, machine learning, data mining and regression analysis. This short course will provide an overview of various predictive analytics techniques and a hands-on introduction to regression analysis using Excel. Participants will leave this course with the tools needed to help guide data preparation and analysis to facilitate quantitative predictions for their organization.

This program is designed for:

    • are responsible for producing reports in Excel and wish to be more efficient
    • have basic exposure to Excel
    • need to perform analyze data in Excel to enhance decision-making and/or reporting

Course Objectives:

    • 1- Sort and filter data
    • 2 - Create "indicator" variables

    3 - Create scatter plots and time series plots

    • 4 - Calculate summary statistics to describe data
    • 5 - Calculate and interpret correlation coefficients
    • 6- Calculate and interpret the line of best fit
    • 7 - Forecast/predict future values

 

  • Goals for prediction

    • Defining the project and selecting the dependent variable
    • Forecasting vs. interpolation

    Data collection and dataset preparation

    • Sampling
    • Types of data
    • Data transformation and preparation
    • Outliers and missing values

    Model development and model selection

    • The ordinary least squares regression model
    • Selecting independent variables
    • Understanding goodness of fit
    • Interpreting the impact of independent variables

    Using the model to make prediction and inference

    • Forecasting and calculating fitted values
    • Margins of error – specifying confidence in your predictions
    • Cautions, caveats and concerns