ABOUT THE PROGRAM
The Graduate Certificate in Data Analytics (CDA) is a 12-month and 13-credit graduate certificate offered by the Master of Science in Management and Analytics (MSMA) program in the Manoogian Simone College of Business and Economics (MSCBE). Designed for professionals and recent graduates, the program equips participants with the analytical and technical skills needed to compete in today’s data-driven economy.
The curriculum covers both foundational and advanced topics in data analytics, including probability and statistics, data management, machine learning, data visualization, web scraping, and an introduction to natural language processing. Throughout the program, students gain hands-on experience with real-world datasets while developing practical skills in programming and analytical tools such as Python, SQL, and Power BI.
The certificate consists of four graduate-level courses totaling 13 credits, all of which are drawn from the MSMA curriculum (three core courses and one elective). Upon successful completion of the program, graduates will be able to:
- Apply statistical, machine learning, and AI techniques to analyze complex datasets.
- Work with real-world data using modern data management and analytical tools.
- Formulate research questions and design data-driven studies.
- Extract meaningful insights from data and communicate findings effectively.
- Use industry-standard programming languages and software, including Python, SQL, and Power BI.
The CDA program is open to individuals seeking to build or strengthen their expertise in data analytics without pursuing a full master’s degree.
CERTIFICATE COMPLETION REQUIRMENTS
To earn the Certificate in Data Analytics (CDA), students must successfully complete the four required graduate courses (listed below), earn a grade of C or better in each course, and maintain a minimum cumulative grade point average (GPA) of 3.0. Graduates of the CDA program who later choose to pursue the Master of Science in Management and Analytics (MSMA) may transfer the completed CDA courses and earned credits toward the MSMA degree, subject to the program’s transfer policies. All requirements for the CDA must be completed within two years of enrollment in the program.
ENROLLMENT REQUIRMENTS
To be considered for admission to the Graduate Certificate in Data Analytics (CDA) program, applicants must:
- Submit a complete online application through the AUA Jenzabar SONIS system, including all required supporting documents, as specified in the application instructions.
- Demonstrate English language proficiency by submitting valid and official TOEFL iBT (minimum score of 79) or IELTS Academic (minimum score of 6.5) results. Native and near-native English speakers may be eligible for an English proficiency waiver.
- Submit a valid GRE or GMAT score, with a recommended score at or above the 50th percentile on the quantitative section. Test scores must be no more than five years old at the time of application. Graduates with an appropriate academic background may be eligible for a GRE/GMAT waiver.
- Applicants who do not have GRE or GMAT scores may still apply by completing the AUA Internal Assessment. However, all else being equal, preference will be given to applicants who submit official GRE or GMAT scores.
- Hold a bachelor’s degree from an accredited or licensed institution of higher education. Applicants in the final year of their undergraduate studies are also eligible to apply.
For detailed application instructions, deadlines, and required documents, please visit the AUA Admissions website.
COURSE DESCRIPTIONS AND SCHEDULE
Summer Term
MSMA 300 Quantitative Tools for Management (Credits: 4)
This course provides an intensive introduction to core concepts in mathematics and statistics, and the main tools that are necessary for quantitative analysis in decision-making using MS Excel and Python. Topics include optimization, financial mathematics, probability theory and inferential statistics. Materials are of depth and coverage necessary for efficient progress in subsequent courses of business analytics, data management and operations. Students are introduced to main libraries of Python used for data analysis (pandas, numpy, matplotlib, statsmodels) and cover basic data manipulation and visualization techniques used in MS Excel. The course is effectively split into two parts (math and stats followed by python and excel) and the second part is conducted in the labs.
Prerequisites: None.
Fall Semester
MSMA 325 Business Analytics (Credits: 3)
This course will introduce the main concepts in business analytics, which will allow achieving fluency in four paradigms that account for most business decisions: marketing, operations, human resources and financial analytics. Students will learn how to build and evaluate supervised learning models and how to incorporate the results in the decision making process. Regression and classification tasks are the core of the course. Students also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. In the final Project, students will apply their skills to interpret a real-world data set and make appropriate business strategy recommendations. Instructor led classes are combined with dedicated lab sessions.
Prerequisite: MSMA 300
MSMA 329 Data Management (Credits: 3)
The purpose of this course is to give students a comprehensive understanding of data management principles and techniques essential for effective analysis. The course covers various aspects, including data manipulation, scraping, SQL, and visualization. Students will learn to work with data using tools like Pandas, Numpy, and SQL, and will gain proficiency in data visualization using libraries such as Matplotlib, Seaborn, and Plotly. Additionally, they will explore the creation of interactive dashboards using open source frameworks (such as Streamlit) and state of the art off-the-shelf packages (such as PowerBI or Tableau). The course is designed in a way to ensure that students will be proficient in coding and applying the knowledge obtained. Instructor led classes are combined with dedicated lab sessions.
Prerequisite: MSMA 300
Spring Semester
MSMA 328 Advanced Topics in Data Analysis (Credits: 3)
The course builds on the knowledge and skills obtained in Business Analytics and Data management courses. It is designed to provide students with a comprehensive understanding of advanced analytical techniques and tools that are commonly used in the field of business and data analytics. It covers the topics of trend extraction, clustering, state-of-the art NLP techniques, and advanced techniques applicable for marketing analysis and model building (over/under sampling, model interpretation). The course will further develop the skills and knowledge for making informed and data-driven decisions based on the insights gained through data exploration. The course includes a comprehensive project assignment during which the students are expected to apply all the knowledge accumulated in this and prerequisite courses. The classes are held in the computer labs and students are expected to extensively apply their coding skills.
Prerequisite: MSMA 325
Courses in the certificate program are scheduled during evening hours.