Course dates and registration deadlines for all courses offered within the Institute for Analysis and Industry Advancement (IAIA).
Data Science Undergraduate Certificate
CRN
Term
Dates
Registration Deadline
Instructor
85125
Fall POT2
8/19-10/12
8/18/2024
Cohen
85350
Fall POT3
10/14-12/7
10/13/2024
Cohen
Course Description:
Throughout the course, there will be hands-on exercises with computing resources. The course will include introductions to several packages in R, particularly Tidyverse, libraries in Python such as Pandas/NumPy/Statsmodels, SQL clauses and summary statistics, and Spark framework for distributed computing.
CRN
Term
Dates
Registration Deadline
Instructor
54059
Summer POT3
6/27-8/9
6/26/2024
Aksut
Course Description:
Organizations can generate copious amounts of data. Extracting useful knowledge from data Warehouses to utilize in decision-making can provide a competitive advantage for an organization by identifying strengths and weaknesses. In this course, you will learn data warehouse organization, how to analyze data through analytical queries, and apply machine learning algorithms to build predictive models.
CRN
Term
Dates
Registration Deadline
Instructor
85446
Fall POT2
8/19-10/12
8/18/2024
Zambesi
84943
Fall POT3
10/14-12/7
10/13/2024
Zambesi
Course Description:
This builds the fundamentals of statistics necessary for students to perform and interpret appropriate hypothesis tests using software based on the data and research questions at hand.
CRN
Term
Dates
Registration Deadline
Instructor
53224
Summer POT3
6/27-8/9
6/26/2024
Seals
85448
Fall POT2
8/19-10/12
8/18/2024
Seals
84942
Fall POT3
10/14-12/7
10/13/2024
Seals
Course Description:
Statistics for Data Science II is a second course in statistics for students in data science. This course covers the application of regression analysis techniques using softwares for statistical analysis. Broadly, students will learn how to construct statistical models and disseminate results to a wide audience. There will be a focus on choosing the appropriate modeling strategy for the data and research questions at hand.
Data Science Graduate Certificate
CRN
Term
Dates
Registration Deadline
Instructor
85996
Fall POT2
8/19-10/12
8/18/2024
Cohen
85910
Fall POT3
10/14-12/7
10/13/2024
Cohen
Course Description:
Gain hands-on knowledge of tools for data science using R, Python, SQL, and Spark. The course provides introductions to several packages in R, particularly Tidyverse, libraries in Python such as NumPy and Pandas, SQL clauses and summary statistics, and Spark framework for distributed computing. You will also learn about RStudio, GitHub, and RMarkdown. To end this course, you will conduct a final project to work on real-world problems.
CRN
Term
Dates
Registration Deadline
Instructor
53227
Summer POT3
6/27-8/9
6/26/2024
Aksut
85750
Fall POT2
8/19-10/12
8/18/2024
Aksut
85997
Fall POT3
10/14-12/7
10/13/2024
Aksut
Course Description:
Organizations can generate copious amounts of data. Extracting useful knowledge from data Warehouses to utilize in decision-making can provide a competitive advantage for an organization by identifying strengths and weaknesses. In this course, you will learn data warehouse organization, how to analyze data through analytical queries, and apply machine learning algorithms to build predictive models.
CRN
Term
Dates
Registration Deadline
Instructor
85447
Fall POT2
8/19-10/12
8/18/2024
Zambesi
84944
Fall POT3
10/14-12/7
10/13/2024
Zambesi
Course Description:
This builds the fundamentals of statistics necessary for students to perform and interpret appropriate hypothesis tests using software based on the data and research questions at hand.
CRN
Term
Dates
Registration Deadline
Instructor
53761
Summer POT3
6/27-8/9
6/26/2024
Seals
85834
Fall POT2
8/19-10/12
8/18/2024
Seals
85836
Fall POT3
10/14-12/7
10/13/2024
Seals
Course Description:
Statistics for Data Science II is the second course in statistics for students in data science. This course covers the application of regression analysis techniques using software for statistical analysis. Broadly, students will learn how to construct statistical models and disseminate predictions and results to a wide audience. There will be a focus on choosing the appropriate modeling strategy for the data and research questions at hand
