Accounting and Financial Analytics Advanced certificate

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Overview

Business analytics are becoming essential problem-solving tools in the accounting and finance professions.


Today's accounting and finance professionals are now expected to serve as business partners and experts who can use data analytics to inform recommendations on business strategy. The advanced certificate in accounting and financial analytics provides knowledge in data science and statistical analysis so that accounting and finance professionals can mine and analyze data to apply it in ways that benefit and improve business operations and outcomes.

The advanced certificate in accounting and financial analytics instills data skills in finance and accounting professionals and enables them to operate effectively in the modern data-centric environment. You will learn how to access, interpret, analyze, and report business data by using tools, as well as use visualization as a decision-making tool in functional business areas. Courses completed in the certificate program can be applied later to the master’s degree in business analytics, or can be used as a valuable add-on for students pursuing master's degrees in fields such as finance, accounting, management, applied statistics, and computer science.

Curriculum

Accounting and financial analytics, advanced certificate, typical course sequence

Course Sem. Cr. Hrs.
First Year
ACCT-645
Accounting Information and Analytics
The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction.
3
FINC-780
Financial Analytics
This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.
3
MGIS-650
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
3
Choose one of the following:
3
  BANA-680
   Data Management for Business Analytics
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.
 
  MGIS-725
   Data Management and Analytics
This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.
 
Total Semester Credit Hours
12

    Admission Requirements

    To be considered for admission to the advanced certificate in accounting and financial analytics, candidates must fulfill the following requirements:

    • Complete a graduate application.
    • Hold a baccalaureate degree (or equivalent) from an accredited university or college.
    • Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
    • Have a minimum cumulative GPA of 3.0 (or equivalent).
    • Submit a personal statement and writing sample.
    • Submit a current resume or curriculum vitae.
    • Submit two letters of recommendation from academic or professional sources.
    • International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 88 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.

    Learn about admissions and financial aid 

    Additional Info

    Gainful employment

    Information regarding costs and the U.S. Department of Labor’s Standard Occupational Classification (SOC) code and occupational profiles for this program can be viewed at www.rit.edu/gedt/accounting_financial_analytics/.