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- Collect, store, and access data by identifying and leveraging applicable technologies
- Create actionable insight across a range of contexts (e.g. societal, business, political), using data and the full data science life cycle
- Apply visualization and predictive models to help generate actionable insight
- Use programming languages such as R and Python to support the generation of actionable insight
- Communicate insights gained via visualization and analytics to a broad range of audiences (including project sponsors and technical team leads)
- Apply ethics in the development, use and evaluation of data and predictive models (e.g., fairness, bias, transparency, privacy)
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The 34-credit M.S.A.D.S. curriculum includes an 18-credit Common Core requirement, a a a 6-credit secondary core requirement across three six tracks, 9 credits of electives, and a 1-credit exit requirement.
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Class # | Course Title |
IST 659 | Data Administration Concepts & Database Management |
IST 687 | Introduction to Data Science |
IST 707 | Applied Machine Learning |
IST 718 | Big Data Analytics |
IST 772686 | Quantitative Reasoning for Data Science |
SCM 651 | Business Analytics |
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Students select 6 credits from one of the tracks below.
AI
Class # | Course Title |
IST 664 | Natural Language Processing |
IST 691 | Deep Learning in Practice |
IST 692 | Responsible AI |
Data and Business Analytics
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Class # | Course Title |
ACC 652 | Accounting Analytics |
FIN 654 | Financial Analytics |
MAR 653 | Marketing Analytics |
| MBC 638 | Data Analysis & Decision-Making |
SCM 702 | Principles of Management Science |
Language Analytics
Understand how to work with unstructured textual data, via both text mining (looking for patterns in the words) and natural language processing (parsing and analyzing the text from a linguistics perspective).
Class # | Course Title |
IST 664 | Natural Language Processing |
IST 736 | Text Mining |
Data Pipelines and Platforms
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Class # | Course Title |
IST 652 | Scripting for Data Analysis |
IST 722 | Data Warehouse |
IST 769 | Advanced Big Data Management |
Project Management
Class # | Course Title |
IST 644 | Managing Data Science Projects |
IST 692 | Responsible AI |
Visual Analytics
Class # | Course Title |
IST 719 | Information Visualization |
IST 737 | Visual Analytics Dashboards |
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Class # | Course Title | ||
IST 615 | Cloud Management | ||
IST 618 | Information Policy | ||
IST 623 | Introduction to Information Security | ||
| IST | 644Managing Data Science Projects | IST 974 | Internship in Applied Data Science |
MAS 766 | Linear Statistical Models | ||
MAS 777 | Time Series Modeling Analysis |
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Term/Semester | Term/Semester | ||
1 | SCM 651 – Business Analytics* | 7 | IST 719 – Information Visualization (example elective) |
2 | IST 687 – Introduction to Data Science* | 8 | IST 664- Natural Language Processing (example secondary core) |
3 | IST 659 - Data Administration Concepts & Database Mgmt* | 9 | IST 736 – Text Mining (example secondary core) |
4 | IST 772686 - Quantitative Reasoning for Data Science* | 10 | IST 722- Data Warehouse (example elective) |
5 | IST 652 – Scripting for Data Analysis (example elective) | 11 | IST 718 – Big Data Analytics* IST 782 – Applied Data Science Portfolio (exit requirement) |
6 | IST 707 – Applied Machine Learning* |
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Term/Semester | Course 1 | Course 2 |
1 | IST 687 – Introduction to Data Science* | SCM 651 – Business Analytics* |
2 | IST 659 – Data Admin Concepts & Database Management* | IST 772686 - Quantitative Reasoning for Data Science* |
3 | IST 707 – Applied Machine Learning* | IST 652 – Scripting for Data Analysis (example elective) |
4 | IST 664- Natural Language Processing (example secondary core) | IST 736 – Text Mining (example secondary core) |
5 | IST 615- Cloud Management (example elective) | IST 719 – Information Visualization (example elective) |
6 | IST 718 – Big Data Analytics* | IST 782 – Applied Data Science Portfolio (exit requirement) |
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Term/Semester | Course 1 | Course 2 | Course 3 |
1 | IST 659 – Data Admin Concepts & Database Management* | IST 687- Introduction to Data Science* | IST 772686 - Quantitative Reasoning for Data Science* |
2 | SCM 651 – Business Analytics* | IST 707- Applied Machine Learning* | IST 652 – Scripting for Data Analysis (example elective) |
3 | IST 664- Natural Language Processing (example secondary core) | IST 719 – Information Visualization (example elective) | IST 736 – Text Mining (example secondary core) |
4 | IST 615- Cloud Management (example elective) | IST 718 – Big Data Analytics* | IST 782 – Applied Data Science Portfolio (exit requirement) |
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Term/ Sem | Course 1 | Course 2 | Course 3 | Course 4 |
1 | IST 659 – Data Admin Concepts & Database Mgmt* | IST 687- Introduction to Data Science* | IST 772686 - Quantitative Reasoning for Data Science* | SCM 651 – Business Analytics* |
2 | IST 652 – Scripting for Data Analysis (example elective) | IST 707- Applied Machine Learning* | IST 664- Natural Language Processing (example secondary core) | IST 736 – Text Mining (example secondary core) |
3 | IST 615- Cloud Management (example elective) | IST 718 – Big Data Analytics* | IST 719 – Information Visualization (example elective) | IST 782 – Applied Data Science Portfolio (exit requirement) |
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- IST 652 is a recommended pre-requisite for IST 707
- IST 652 is a recommended pre-requisite for IST 718
- IST 652 is a recommended pre-requisite for IST 736
- IST 659 is a recommended pre-requisite for IST 652
- IST 659 is a required pre-requisite for IST 722
- IST 659 is a required pre-requisite for IST 769
- IST 664 is a recommended pre-requisite for IST 736
- IST 687 is a required pre-requisite for IST 707
- IST 687 is a required pre-requisite for IST 718719
- IST 687 is a required pre-requisite for IST 719736
- IST 687 707 is a required pre-requisite for IST 736718
- MBC 638 and SCM 651 are recommended pre-requisites for FIN 654
- MBC 617 OR MBC 635 and MBC 638 are required pre-requisites for SCM 702
- MBC 638 is a required pre-requisite for MAS 777
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Course | Machine Learning | Python | SQL | R | Big Data | DB Mgmt | Statistics | Visualization | Google Analytics | Tableau | MS Power BI |
IST 659: Data Admin Concept & Mgmt | X SQL Servicer, Access, Visio | X | |||||||||
IST 687: Intro to Data Science | X | X R/R Studio | X | X | |||||||
IST 707: Applied Machine Learning | X | X R/R Studio: arules, Rweka, class e1021, caret | X | X | |||||||
IST 718: Big Data Analytics | X | X Apache Spark, Hadoop 2.0, Spark ML, Pandas, Matplotlib | X | X | |||||||
IST 772686: Quantitative Reasoning for Data Science | X (R/R Studio: includes packages for Bayesian methods, ANOVA, Regression, Time Series | ||||||||||
SCM 651: Business Analytics | X Neural Networks, K-means clustering, decision trees | X | X | X | X Linear and non-linear regression, logit, probit, ordered logit, multinomial logit, conjoint analysis | X | X Analytics & AD Words | X Analysis & dashboard | X Analysis & dashboard |
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Audited courses are non-credit and are not counted toward enrollment status, however, students are allowed to register for a course they previously audited to receive a grade. Students auditing courses are not responsible for fulfilling the academic requirements of the course, and, therefore, do not receive academic credit for audited courses. If a graduate student is registered full-time, or for nine credits, the fourth class they take can be audited for free. If a graduate student is registered for only six credits and wants to audit a three-credit course, they are financially responsible for the course. Students may only audit courses with the approval of the appropriate department and subject to the restrictions made by the instructor. Online courses may not be audited.
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