The Business Analytics Bootcamp offered by the Bill and Vieve Gore School of Business June 19–August 9, 2018, is open to professionals who want to expand their understanding of and skills in data collection, analysis, and visualization.
Minimum Requirements to Participate
- Professional business experience
- A desire to learn how to use data to help firms make better business decisions
- Experience using statistics in Excel
Students who need a refresher course in business statistics using Excel may complete a free online course. Register for the business statistics refresher using course ID number 34722 and enrollment key FreeBootcampPrep.
You can submit your anonymized data for students to use as they learn whether or not your firm or business is sending someone to the Business Analytics Bootcamp.
Contact Dr. Hal Snarr with any questions at firstname.lastname@example.org or 801.832.2634.
Main Floor CFA Lab
Bill and Vieve Gore School of Business
Westminster College, 1840 South 1300 East
All four blocks: $2,495
Individual blocks: $795 each
Only 25 seats available per block. Net proceeds will help fund graduate student scholarships.
Block I: Collecting, Cleaning, and Coding
Instructor: Hal Snarr
After mastering basic operations in Python, you will use its NumPy and Pandas libraries to define and manipulate cross-sectional and panel data, respectively, that were imported into Python from Excel or text files. Because there is so much data stored on the web (and data changes from day to day), you will learn how to use Pandas and BeautifulSoup to extract or scrape web data. You will then learn how to store the data in Excel or CSV files and as Python objects. Additionally, you will learn how to clean and code the data in Excel and Python.
Week 1: June 19–21, 9:00 a.m.–1:00 p.m.
Week 2: June 26–28, 9:00 p.m.–1:00 p.m.
Block II: Data Storytelling and Visualization
Instructor: Hal Snarr
With the data cleaned and stored in Excel or CSV files, you will learn how to use summary statistics, tables, and visualizations to create compelling stories out of the data. To do this, you will first learn to calculate summary statistics (mean, median, mode, standard deviation, skew, and correlation); construct charts (pie and bar charts for qualitative variables; histograms and scatterplots for quantitative variables); and build pivot tables using Python and Excel. You will learn how to build interactive pivot tables and pivot charts using slicers and filters in Excel and how to create 3D and 4D scatterplots using Python’s matplotlib library.
Week 3: July 3–5, 9:00 a.m.–3:00 p.m.
Week 4: July 10–12, 9:00 a.m.–1:00 p.m.
Block III: Databases and Dashboards
Instructor: Alton Alexander
You will learn how to extract, transform, and combine datasets using SQL with Microsoft Azure/Spark. You will learn how to create advanced queries with Microsoft Azure/Spark and how to construct dashboards that contain interactive charts and tables using Tableau. Finally, you will learn hypothesis testing, including A/B tests, and be introduced to machine learning using Microsoft Azure/Spark.
Week 5: July 17–19, 9:00 a.m.–1:00 p.m.
Week 6: July 25–26, 9:00 a.m.–3:00 p.m.
Block IV: Modeling and Machine Learning
Instructor: Alton Alexander
You will learn how to build a variety of models, interpret the models, and present the results to an audience. Topics will include deep learning, Markov decision processes (e.g., CNN, RNN, GAN), classification, clustering, dimensionality reduction, and regression using R.
Week 7: July 31 and August 1–2, 9:00 a.m.–1:00 p.m.
Week 8: August 8–10, 9:00 a.m.–1:00 p.m.
About the Instructors
Dr. Hal Snarr has bachelor degrees in business and mathematics and a PhD in economics. Hal taught economics and business statistics at North Carolina A&T State University from 2004 to 2013. In 2013, Hal joined Westminster College where he teaches courses in economics, business analytics, and information technology. In his research, Hal constructs relational databases using SAS, STATA, Python, and R to merge data from the Bureau of Labor Statistics, Federal Reserve Economic Database, IPUMS-CPS, and other public sources. Using R, SAS, and STATA, he codes and cleans these datasets to estimate a variety of regression models to test the effects of federal or state policies on outcomes such as migration, work, fertility, and public assistance participation. He has published 15 peer-reviewed journal articles and two books. Hal enjoys being in the classroom, experimenting with innovative teaching techniques, producing instructional videos for his YouTube channel, the Snarr Institute, and writing articles for Stocks and Jocks (a Chicago talk show), Mises Daily, PolicyMic, and OpEdNews.
Alton Alexander is the founder, president, and chief data scientist at Front Analytics Inc., a professional services and coaching firm that specializes in customer interaction insights using advanced analytics. Companies work with Front Analytics to accelerate their learning from big data and to integrate new sources of continuous insights. Previously, Alton worked for and consulted with data-oriented technology startups, marketing agencies, healthcare groups, legal firms, and manufacturing companies. He has also participated in and won high-impact data science competitions. Alton has taught as an adjunct faculty member in the Master of Information Systems program at the University of Utah, including courses in data science and big data analytics. His exposure to a wide range of companies and technologies adds color to the landscape of technical challenges and creative approaches necessary for successful and practical solutions in the real world. Alton is enthusiastic about advanced analytics, machine learning, and big data.
Contact Alton: email@example.com | Twitter | LinkedIn