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Program Overview

Analytics and Business Intelligence

Analytics and Business Intelligence for IT Professionals Advanced Graduate Certificate Program

Learn technical and leadership skills required for key positions in today’s data-intensive and data-driven world. This certificate will teach IT professionals how to use new data acquisition technologies to enhance organizational effectiveness, customer service, returns on investment and a myriad of other business goals.

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About the Analytics and Business Intelligence Graduate Certificate Program

Analytics and Business Intelligence Course Schedule

The Analytics and Business Intelligence Course Schedule page provides you with detailed information on the program's offerings.

Analytics and Business Intelligence Faculty

You can find a full listing of instructors for this certificate program on the Analytics and Business Intelligence Faculty page.

Admission for Analytics and Business Intelligence

Applicants to this certificate program must hold a graduate degree from an accredited U.S. college, university or its foreign equivalent. Three to five years of professional experience in the related field is recommended but not required. A list of admission requirements can be found on our Analytics and Business Intelligence Admission page.

Analytics and Business Intelligence Tuition

Tuition costs can vary for each of our programs. For the most up-to-date information on financial obligations, please visit our Analytics and Business Intelligence Tuition page.

Analytics and Business Intelligence Registration Information

Our Analytics and Business Intelligence Registration Information page outlines important dates and deadlines as well as the process for adding and dropping courses.

Gainful Employment Information for Analytics and Business Intelligence

Common questions and answers related to cost, financing and success in this certificate program are found on our Gainful Employment Information for Analytics and Business Intelligence page.

Additional Information

Applicants must possess a graduate degree in order to be considered for this program. However, no prior coursework in IT is required. Students should have knowledge of calculus, linear algebra and relational database training or experience.

Accepted students must successfully complete PREDICT 400 prior to enrolling in PREDICT 401.

 

Find out more about Northwestern's Analytics and Business Intelligence Graduate Certificate Program

Analytics and Business Intelligence Required Courses

To earn a certificate, students must complete the following four courses. In some cases, students who have completed equivalent coursework previously may be allowed to replace the required course with another course in the field.

Please note that courses completed in the certificate program cannot be transferred to the corresponding graduate degree.

Core Courses:Course Detail
Math for Modelers <> MSDS 400-DL

Students learn techniques for building and interpreting mathematical models of real-world phenomena in and across multiple disciplines, including matrices, linear programming, probability, and both differential and integral calculus, with an emphasis on applications. This is for students who want a firm understanding and/or review of these fields of mathematics prior to applying them in subsequent courses. Counts as an elective for students admitted prior to fall 2014. Required as a core course for students admitted for fall 2014 and after.


View MSDS 400-DL Sections
Statistical Analysis <> MSDS 401-DL

Students learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines. Topics covered include probability, descriptive statistics, study design and linear regression. Emphasis will be placed on the application of the data across these industries and disciplines and serve as a core thought process through the entire Predictive Analytics curriculum.

Prerequisite: PREDICT 400-DL Math for Modelers.


View MSDS 401-DL Sections
Courses:Course Detail
Data Science/Machine Learning <> CIS 435-0

This course provides an introduction to data science with a focus on building real-life business applications using machine learning. Students learn data mining applications, core concepts, algorithms and overview of open source/commercial tools. This course takes a hands-on approach to this topic and prepares students for applying appropriate machine learning modeling techniques (Collaborative Filtering, Association, Clustering and Classification) for given real-world use cases. Students learn via experimentation; they observe the outcome of applying machine learning algorithms to structured and unstructured data using open source software.

It is highly recommended that students complete CIS 417 or possess equivalent knowledge and skills prior to taking this course.

Note for students in the MSIS program: This course is required for the specialization in Database and Internet Technologies. This course may be used as an elective towards all other specializations.

Note for students in the MHI/MMI program: This course is an elective for students pursuing the MHI/MMI degree.

Note for students in the MSPA program: This course is an elective for students pursuing the MSPA degree.

Note for all students: This course was formerly called Data Warehousing and Data Mining.


View CIS 435-0 Sections
Big Data Management/Analytics CIS 436-DL

This course reviews concepts behind both centralized and distributed database systems, and relational and not-only-relational database systems. Discussion of open source and commercial solutions, with special attention being paid to large distributed database systems and data warehousing. The course introduces technologies and modeling methods for large-scale, distributed analytics.

Note for MSIS students: It is highly recommended that MSIS students complete CIS 417 and CIS 435 or possess equivalent knowledge and skills prior to taking this course.

Note for MSPA students: Students must complete PREDICT 420 and PREDICT 422 prior to taking this course.


View CIS 436-DL Sections
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