Graduate Certificate in Data Analysis

Graduate Certificate in Data Analytics (pending WSCUC approval)

 

ABOUT THE PROGRAM

The Graduate Certificate in Data Analytics (CDA) is a 16-month program offered by the Master of Science in Economics (MSE) 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 statistics, data management, analysis, and visualization. Probability theory, statistical analysis methods and tools, visual presentations, concepts and techniques for data mining and web scraping will be covered during the 4 graduate level courses that are required for the successful completion of the graduate certificate program. Professionals that complete this program will have a robust knowledge of data analysis methods and tools, including:

  • understanding of Econometric theory
  • 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 statistical software and programming languages like Stata and Python

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 Economics program, may transfer their CDA courses and credits towards the MSE Program. Required coursework for the completion of CDA must be finished in 3-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 (minimum score of 68*) or IELTS Academic (minimum score of 6.0*). 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 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

Fall 1 Semester

ECON 310 Economic Statistics (Credits: 3)*

This course provides students with a survey of statistical methodology. Topics include probability and sampling, distribution theory, hypothesis testing, estimation, analysis of variance, confidence intervals, and linear regression. Students are required to complete biweekly problem sets by solving exercises and using statistical software. Three hours of instructor-led class time per week.

* This is a real-time online blended course. Students can attend blended courses at AUA Yerevan campus, or from their workplace or home. During the quiz and exams, physical presence at AUA is required.

 

Spring Semester

ECON 315 Financial Econometrics and Time Series Analysis (Credits: 3)*

This course is an introduction to data analysis and econometric modeling using applications in finance and time series. The course uses concepts from microeconomics, finance, mathematical optimization, data analysis, probability models, statistical analysis, and econometrics. The course will be 16 weeks long. Each week consists of one 150 minutes lecture. Finance topics include asset return calculations, risk and performance measures, portfolio theory, index models, and applied time series analysis. Quantitative methods involve basic matrix algebra. Statistical topics include probabilities, expectation, joint distributions, covariance, normal distribution, sampling distributions, estimation and hypothesis testing, data analysis, linear regression, time series methods and simulations. There will be weekly frequent homework assignments requiring STATA programming. Students will work independently and periodically in groups to complete problem sets and group projects. Students will be graded on quizzes/problem sets, midterm and final exams.

Prerequisite: ECON310

* This is a real-time online blended course. Students can attend blended courses at AUA Yerevan campus, or from their workplace or home. During the quiz and exams, physical presence at AUA is required.

 

Summer Semester

ECON 317 Data Scraping (Credits: 3)

This course will introduce the main methods of acquiring data from the web and other digital sources. Students will learn how to scrape, parse, and read web data as well as access data using web APIs (e.g. Twitter, LinkedIn etc.). They will work with HTML, JSON and other data formats in Python. They will also learn how to use a set of freely available tools to gather data from the web. The format of the course will be mainly case-based introducing the applications of data scraping in various aspects of business and economics. Student’s work will be evaluated based on class participation, quizzes/problem-sets, midterm and final project. The course qualifies for the MSE Applied Economics track.

Prerequisite: ECON310

Fall 2 Semester

 

ECON 316 Topics in Applied Health Econometrics  (seminar) (Credits: 3)

This course reviews a range of econometric methods (such as Probit, Logit, Tobit, Poisson, Negative Binomial, LAD, Matching, GLM) that have been used for testing economic hypotheses in health outcomes. Starting with an introduction to health outcomes, the course will proceed to advanced econometric methods for addressing specific problems generated by either the nature of the data generation process or the economic relationships being examined. The course will develop your econometric skills in several ways. First, the course will review numerous econometric models and in each case discuss the type of problems the model is suited for, how to test hypotheses, and the shortcomings of various models. Second, you will be asked to prepare two presentations; first presentation will cover a particular method and second presentation will cover health condition related outcomes. Third, to assist in the development of your data management, modeling, computer, and interpretative skills, you will use Stata to analyze data. Students will be graded on quizzes/problem sets, midterm, project/presentation and final exams. The course qualifies for the MSE Applied Economics track.

Prerequisite: ECON310

 

!Courses are scheduled during evening hours or Saturdays.