Graduate Certificates

Graduate Certificate in Data Analytics (CDA)

ABOUT THE PROGRAM

The Graduate Certificate in Data Analytics (CDA) is a 12-month program offered by the Master of Science in Management and Analytics (MSMA) program in the Manoogian Simone College of Business and Economics (CBE). The program will provide professionals with the skills required to be globally competitive in the field of data analysis. Students will explore introductory and advanced topics in data management, analysis, machine learning and visualization. Probability theory, statistical analysis methods and tools, visual presentations, concepts and techniques for data mining and web scraping, machine learning and introduction to natural language processing will be covered during the 4 graduate level courses that are required for the successful completion of the graduate certificate program. Importantly, all 4 courses are part of MSMA curriculum (3 core plus 1 elective course). Professionals that complete this program will have a robust knowledge of data analysis methods and tools, including:

understanding of machine learning and AI
working knowledge with the real-world data sets
ability to formulate the research question and design studies
ability to discover and present information hidden in the data
working knowledge of data management tools and programming languages like SQL, Python and Power BI.

The program is open to anyone who wishes to prepare for a rewarding career in the field of Data Analytics.

CERTIFICATE COMPLETION REQUIREMENTS

To receive a Certificate in Data Analytics (CDA) students must successfully complete the below listed graduate courses with a grade of C or better in each course and maintain an overall minimum 3.0 grade point average (GPA). CDA graduates who wish to pursue further study towards the MS in Management and Analytics program, may transfer their CDA courses and credits towards the MSMA Program. Required coursework for the completion of CDA must be finished in a 2-years period, after starting the program.

ENROLLMENT REQUIREMENTS

To be considered for acceptance, applicants must:

Submit a complete Application for Enrollment in Certificate Programs at im.aua.am including all required supplements as outlined in the instructions.
Present valid and official results confirming English Language Proficiency typically through the TOEFL iBT (target score of 79*) or IELTS Academic (target score of 6.5*). Native and near native speakers are eligible for a waiver. Present an official GRE or GMAT score at a minimum of 50th percentile on the quantitative section. The score is valid only if the test date was less than five years before the application submission date. AUA graduate alumni with relevant background are eligible for a waiver.
Hold an undergraduate degree from an accredited or licensed institution of higher education. Students in their final year of studies are also eligible to apply.

For information on how to apply, please visit the Admissions website.

COURSE DESCRIPTIONS AND SCHEDULE

Summer Term

MGMT 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

MGMT 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: MGMT 300

MGMT 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: MGMT 300

Spring Semester

MGMT 328 Advanced Topics in Data Analysis (Credits: 2)

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: MGMT 325, MGMT 329

Courses in the certificate program are scheduled during evening hours or Saturdays.