BIG DATA BOOTCAMPIntroductory Big Data Bootcamp
Participants will receive 14 CPE credits.
Breakfast, lunch, and snacks are included in your tuition.
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Big Data Class Outline
This two day in-depth course provides an introduction to Big Data and Predictive Analytics analysis. Participants will start by learning what Big Data is and how it can be used to improve business operations and financial performance. In day 1, participants will get hands-on experience with building datasets including gathering, merging, and cleaning Big Data databases. Additionally, the course covers checking datasets for potential problems or data integrity issues, and information governance. In day 2, the course covers using regression analysis, non-liner analysis, fixed effects analysis, and other forms of predictive statistical techniques. Datasets used in the course are focused on financial metrics such as sales and profitability, and on investment metrics like stock returns. Computers are required.
Business professionals, especially in the finance area who want to understand how data can be used to improve business and investment decision making. This course requires knowledge of basic finance concepts. Experience with financial markets is recommended but not required. A computer with Excel is required. Students should ensure that their Excel program has the Analysis Toolpak loaded (Check under File -> Options -> AddIns).
By the end of the course, the participants will be able to:
- Describe the purpose and uses of Big Data in the business world today
- Identify the terminology used in Big Data and quantitative analysis programs in general
- Build a dataset based on gathering data from multiple sources and merging those databases into a single unified set
- Clean a database through automated methods like winsorizing and evaluation of univariate metrics to determine accuracy of inputs
- Build a dataset to enable forecasting of financial metrics
- Identify key risk issues involved in Big Data and the role that information governance plays.
- Identify the role of predictive analytics in business settings today.
- Describe at a high level the theory behind predictive analytics and the settings in which the tools are useful
- Understand the advantages and disadvantages of predictive analytics techniques
- Build predictive analytics models using fixed effects analysis, random effects analysis, logit models, and censored Tobit models
- Perform predictive analytics analysis to forecast returns on various investments