Creating competitive advantage through data
Getting started in predictive analytics takes thoughtful planning and a little time, but it’s a necessary tool that virtually any business can implement. Being committed to an approach or model is key, as is the willingness to invest the time and resources needed to achieve success. Ultimately, predictive analytic models can help businesses acquire, manage, and grow new and existing customers.
Using techniques such as data mining, statistical modeling, machine learning, and even artificial intelligence, predictive analytics helps analyze existing data to make future predictions. We can generate future insights with more certainty, and more reliably forecast scenarios, trends, and behaviors. “Predictive analytics is receiving significant support and direct funding from many organizations and industry sectors”, says Dr. Jeff Camm, Associate Dean of Business Analytics at Wake Forest’s School of Business.
Faculty have designed this program for individuals and analytics teams to gain immediate and practical skills to solve pressing business problems and challenges. We will analyze a business cases and use the tools of business analytics to immediately create value in any organization. This program will be offered virtually with all content delivered online.
This program is part two of a series of three open enrollment analyst-based programs leading to a Certificate in Analytics for Analysts. This certificate program offers a unique multi-day deep dive across the entire analytics ecosystem and culminates in a short Analytics Capstone Project. Please note: each program can be taken individually to gain critical knowledge and skills, or, combined as a certificate.
What You’ll Learn:
- Understand how to look at data and gain needed insights to move your business forward.
- Improve and enhance your ability to forecast and make sound, predictable recommendations with data
- Explore statistical methods and machine learning techniques to identify the likelihood of future outcomes based on historical data.
- The right questions to ask in challenging assumptions of analytics and AI
- How to use frameworks and tools to recognize the power and potential of data in driving competitive advantage.
- How data can be used effectively from diverse industries and functional areas by networking with peers
Who Should Attend?
The program is ideal for individuals and teams wishing to advance their careers and organizations. Business analysts, data analysts, financial analysts, analytics managers, program or project managers, and other analytics leaders will be comfortable with the course content, pace, and challenge.
This course assumes only prior knowledge of high-school algebra.
What You’ll Need for the Program
- Students should bring a laptop with Windows 8 or newer on it
- Excel 2016/2019 or Excel for Office 365
- Ability to download and save files
Registration and Payment Form
This is an open enrollment program. Application is not required. Course registration is complete when payment is processed. Upon registration, a representative of Wake Forest will contact you to validate the registration information and to assist in fee payment.
Additional Program Details
Dr. Tonya Etchison Balan is an Associate Teaching Professor in the School of Business at Wake Forest University. She received her undergraduate degree in mathematics and her PhD in Statistics from North Carolina State University. Dr. Balan has extensive experience as a practicing statistician working with customers in such diverse industries as health care, retail, financial services, government, and consumer packaged goods.
Prior to joining Wake Forest, Balan was on the faculty of North Carolina State University’s Poole College of Management. She also spent 10 years as Director of Analytics Product Management at SAS Institute where her team was responsible for providing strategic direction for all aspects of the development and deployment of SAS’ suite of analytics products. Dr. Balan is something of a foodie and enjoys cooking and trying new recipes and cuisines. She also enjoys playing the piano and traveling the world with her family.
This is a two day program, participants must attend both sessions in full:
- January 6, 2021 – 8:00am -12:00pm
- January 7, 2021 – 8:00am – 12:00pm
Predictive Analytics as a stand-alone program – $800
Certificate in Analytics for Analysts – $2,100 for all three (3) programs in certificate track.
The cost of the program is due in full at time of registration. Wake Forest University alumni, faculty, and staff receive a 25% tuition discount. If multiple participants are interested in attending from your organization, please contact us for multi-attendee discount information. Please contact firstname.lastname@example.org for more details.
Predictive Analytics is virtual! Prior to the program date, a Zoom link will be provided to all attendees to access this instructor-led curriculum.
Day 1 – 8:00am – 12:00pm
- Virtual ice breaker / technology testing / etc.
- Session 1: What Is Predictive Analytics?
Predictive Analytics give you the power to make forward-looking inferences based on the historical data you have collected. The methods that fall into this domain overlap significantly with machine learning. In this session, we will discuss the definitions of these terms and explore the ways in which predictive analytics can be used to help move your business forward. We will also discuss the CRISP-DM process for predictive modeling.
- Session 2: Linear Regression
Most people remember linear regression from a statistics class that they took in college. And, while linear regression has its roots in classical statistical theory, it is actually one of the most powerful – and most commonly used – predictive analytics methods out there. After reviewing the basic concepts of linear regression, you will put the method into practice using Excel.
Case Study – linear regression in the real world
- Session 3: Classification Methods
Many of the most pressing business issues in the marketplace today fall into the category of classification problems. Simply put, the goal of a classification problem is to accurately identify to which of two groups a particular entity belongs. For example: Is a credit card transaction fraudulent or not? Will a customer respond positively to a marketing offer or not? In this session, we will talk about classification methods and where they might apply to your business.
- Session 4: Logistic Regression
Widely used in practice, logistic regression is a classification method that builds on the foundation you have established with linear regression. In this session, we will discuss the basics of logistic regression in preparation for applying the method to a case study in tomorrow’s session.
Day 2 – 8:00am – 12:00pm
- Session 5: Logistic Regression in the Real World
In this session, we will apply what we have learned about classification methods and logistic regression to a real-life data set.
Demo – Using Excel to implement logistic regression
- Session 6: Classification Trees
Classification trees provide an intuitive visual representation of the relationships between the variables in your data set. The method involves recursively partitioning your data and yields a set of “if-then-else” rules that can be used to classify the entities in your data. In this session, we will gain an intuitive understanding of classification trees and learn how to build a classification tree in Excel.
Demo – Using Excel to build a classification tree
- Session 7: Classification in the Real World
Almost every business has customers. And, anyone with customers needs to understand their behavior. Which customers are the best prospects for a new product? Which customers are at risk for cancelling their contract? Which customers are most likely to respond to a particular marketing offer? In this session, we will work together to analyze a customer-focused case study using logistic regression and classification tree methods.
- Session 8: Bringing it all together
In this last session, we will summarize what we’ve learned and discuss ways that you can be better prepared to engage your organization’s data science team.
Course materials are included in the cost of tuition. Course materials will be distributed electronically in advance of the program start date. Participants must have a laptop or tablet computer to access course materials.
This course is offered contingent upon sufficient enrollment. If a course must be cancelled, all registered participants will be notified at least five (5) calendar days before the course’s start date. All registered participants will receive a 100% tuition refund. No fees will be charged for cancelled courses.
Individual Cancellation – Refund Policy
Prospective participants who withdraw at least 10 days prior to the start of a course will receive a full refund of tuition paid.
For More Information
For more information or to speak with a program adviser, please send an email to email@example.com.