ACCT5141
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ACCT 5141 - Financial-Data Analytics (2 Cr.)
Accounting (11264)
TCSM - Curtis L. Carlson School of Management
Course description
This is a 2-credit introductory course on financial reporting data analytics for Carlson students. The main learning objective is to introduce students specializing in business (accounting, auditing, tax, finance, marketing, operations, etc.) to data analytics, providing them the necessary knowledge and tools needed to effectively use data analytics in their specialized domain. The goal is thus for students to be able to consume and use available data analytics technologies to complement existing technical skills, rather than to train "data analytics specialists" (although this class is a good jumping-off point for students who wish to pursue a career specializing in data analytics). Prior coding experience is thus not required, although students should have completed business statistics (SCO 2550 or BA 2551 or equivalent statistics course).
After a general overview of data analytics and machine learning, we will dive into the ETL (extract, transform, load) process, covering topics and showcasing applications such as data joins, variable types, formulas, and regular expressions. We will then explore data visualization tools (including pivot tables and dashboards) and conclude the term by modeling data to create business insights via predictions. Students will gain hands-on experience using state-of-the-art data analytics tools and will learn how to conduct basic SQL queries. Students will improve their quantitative and problem-solving skills and learn how to apply scientific research methods to answer questions, present solutions, and discuss limitations. An emphasis will be placed on financial reporting datasets/applications, although the methods and concepts covered are applicable to other business settings/functions. Ultimately, students will enhance their analytical skills and achieve a deeper understanding of issues related to financial reporting specifically and business more generally.
prereq: SCO 2550 or BA 2551 or equivalent statistics course; ACCT 2050 or 2051 or 2051H; BA 3051 is recommended
After a general overview of data analytics and machine learning, we will dive into the ETL (extract, transform, load) process, covering topics and showcasing applications such as data joins, variable types, formulas, and regular expressions. We will then explore data visualization tools (including pivot tables and dashboards) and conclude the term by modeling data to create business insights via predictions. Students will gain hands-on experience using state-of-the-art data analytics tools and will learn how to conduct basic SQL queries. Students will improve their quantitative and problem-solving skills and learn how to apply scientific research methods to answer questions, present solutions, and discuss limitations. An emphasis will be placed on financial reporting datasets/applications, although the methods and concepts covered are applicable to other business settings/functions. Ultimately, students will enhance their analytical skills and achieve a deeper understanding of issues related to financial reporting specifically and business more generally.
prereq: SCO 2550 or BA 2551 or equivalent statistics course; ACCT 2050 or 2051 or 2051H; BA 3051 is recommended
Minimum credits
2
Maximum credits
2
Is this course repeatable?
No
Grading basis
A-F - A-F Grade Basis
Lecture
Requirements
013055
Fulfills the writing intensive requirement?
No
Typically offered term(s)
Every Fall & Spring