Big Data & Business Intelligence – Michael McDonald

Description

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What you'll learn

• Describe the purpose and uses of Business Intelligence & 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
• Identify key risk issues involved in Big Data and the role that information governance plays.

Description

This course is broken up into four modules.

The first module will prepare participants to begin business intelligence projects at their own firm. The focus of the course is a hands-on approach to gathering and cleaning data. After taking this course, participants will be ready to create their own databases or oversee the creation of databases for their firm. The focus in this course is on “Big Data” datasets containing anywhere from tens of thousands to millions of observations. While the tools used are applicable for smaller datasets of a few hundred data points, the focus is on larger datasets. The course also helps participants with no experience in building datasets to start from scratch. Finally, the course is excellent for users of Salesforce, Tableau, Oracle, IBM, and other BI software packages since it helps viewers see through the “black box” to the underlying mechanics of Business Intelligence practices.

The second module will prepare participants to begin business intelligence projects at their own firm. The focus of the course is a hands-on approach to structuring data including generating new variables based on comparative and relative metrics. The structuring of these variables will be done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course will be on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses.

The third module will prepare participants to begin running data analysis on databases. Both univariate and multivariate analysis will be covered with a particular focus on regression analysis. Regression analysis will be done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course will be on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses.

The fourth and final module will prepare participants to review, analyze, and make decisions based on results from business intelligence projects. The course will cover reading and interpreting regression analysis. The course will also give participants the skills to critically analyze and identify potential limitations on analysis. The course will also cover predicting changes in business outcomes based on analysis and identifying the level of certainty or confidence around those predictions. This paves the way for future detailed courses in predictive analytics.

Who this course is for:

• This course will provide an introduction to the practices and procedures involved with Business Intelligence and applied Big Data analysis. Participants will learn what Big Data is and why it is being discussed as a revolutionary approach to many aspects of business and finance. Participants will get hands-on experience with gathering, merging, and cleaning Big Data databases. The program will also entail critical evaluation of existing datasets to check for potential problems or concerns over data integrity. The course will also include a discussion of information governance.

Course content
6 sections • 35 lectures • 4h 16m total length

• Introduction to Big Data and Business Intelligence
• Data Collection and Cleaning
• Structuring Data
• Fundamentals of Data Analysis
• Using Data Analysis
• Exercises and Cases

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