Big Data and Analytics are all the rage. But many businesses don’t take advantage of the opportunities for analyzing their data, and miss out on using them to improve their bottom line. In fact, a recent Forrester Consulting study found that data-driven companies are 58% more likely to beat revenue goals than non-data-driven companies.
Join Dr. Julie Alig to learn the methods and skills to make sense of your company’s data, so that your company is part of that 58%. Through six (6) in-depth live-online modules over 12 weeks, students will be given real-world business examples to independently investigate, and will include hands-on practice with real data.
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Modules & Certificate Schedule 2023:
View More About What's Covered in Each Module in the "What You Will Learn" Tab.
Completion of all 6 modules will award a Certificate in Business Analytics + A Micro-credential (digital badge) from the University of New Hampshire.
Participants who successfully complete the Certificate in Business Analytics program will automatically receive a digital badge micro-credential that verifies their accomplishment and skill that can be shared on social media, blogs and online portfolios.
Upon completion of the certificate, participants will receive an email with further information on claiming their badge. For more information on digital badges with UNH PD&T, click here.
Questions? Send us a message here.
The foundation of any analytics begins with descriptive analytics. We’ll discuss what analytics is (and isn’t), as well as the tools and methods that are commonly used to perform analytics and communicate results. Students will be presented with examples of descriptive, predictive and prescriptive analytics, with an emphasis on concrete examples of each from business. Then we’ll dive into a dataset and produce the fundamental statistics and analyses that will underpin our later work.
It’s a well-known rule of thumb among data scientists that 80% of project time is spent cleaning, validating, wrangling and prepping the data, and just 20% on the actual analysis. Skipping this step risks ending up with incorrect findings that harm the business. (Garbage in, garbage out.) This module will present a systematic way to clean and validate your data, along with creating new variables and tackling missing data. We’ll put these ideas into use when we merge data from several systems across a business into one dataset that will be used to answer the C-suite’s questions.
We saw ways to get a sense of what your data looks like earlier, in Module 1. But let’s go a bit deeper. What if our story is more complex? What techniques can we use? And, on a more basic level, how does the human brain perceive objects, and how can we leverage that to help get our point across? We’ll review best practices for presenting data through visual methods including graphs, infographics, and dashboards. We’ll weigh the pros and cons of each, and discuss the merits of different types of charts, graphs and visuals to deliver your message.
One especially helpful use of analytics for business is forecasting. We’ll review several types of forecasting that are regularly employed by businesses, time-series (trend lines, moving averages) and associative (correlation, regression). No method is perfect, however, and we’ll discuss the advantages of each as well as the drawbacks. We’ll also look at some of the common pitfalls that both analysts and decision-makers make, and suggest ways to mitigate for that.
Now that you have solid analyses that answer your business questions, it’s time to present them so that your audience actually understands them and can make business decisions based on them. That’s where data storytelling comes in. We’ll investigate several story types, and learn which are more useful in a business setting. And we’ll build upon concepts from the earlier “Data Visualization” module to craft persuasive, approachable data stories for our audience.
Not all data is in the form of numbers. Text and words are data, too, and we can use qualitative analysis techniques to find the patterns and associations hidden within. In this module we’ll consider methods to analyze survey data, especially customer satisfaction data, and discuss the ways we can use the techniques covered in previous modules to communicate findings and implications.
Julie Alig, Ph.D., is Founder and CEO of JLA Analytics, LLC, which helps organizations translate their data into actionable information. After earning her Doctorate in Political Science from the University of Chicago, Dr. Alig spent 20 years in higher education administration providing C-suite decision support using predictive analytics and statistical modeling, data visualizations, and interactive dashboards, among others. She has held multiple leadership and elected positions in the Northeast Association for Institutional Research (NEAIR), and has delivered workshops and talks on data analytics at both regional and national conferences in the higher education field. Dr. Alig is active with the NH Tech Alliance, and with its initiatives surrounding women and girls in STEM.
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