With the dawn of a data-driven economy, data literacy is increasingly important. Whether you plan to enter the field of business, academic research, medicine, education or government, data is likely to play a big role. Our collaborative programs emphasize the skills you need to solve real-world problems using data and digital technology. The applications are limitless. What do you want to do?

  • Do you want to be a Data Scientist?

    A data scientist’s job is to arrange undefined sets of data for analysis. This can include writing algorithms or building statistical models. If you have interests in coding and analysis and like the idea of supporting evidence-based decision-making, our Mathematics: Data Science and Statistics (BA) may be for you.

    The Data Science and Statistics Option within the Mathematics Major (Major code: Math-BA; Concentration code: Math-DSS) provides a strong background in statistics and data analysis, with an emphasis on cross-disciplinary and computational courses that are especially tailored for a career in data science. The required coursework focuses on core mathematics (multivariable calculus, linear algebra, logic & proofs, real analysis), statistics from many perspectives (options in math, sociology, biology), high-level theoretical statistics (graduate level probability, statistical inference, bayesian modeling, time series), computer programming fundamentals (C++ / Java), statistical modeling (two econometrics courses, a special writing in the major course focused on prediction models), and practical modeling (via Excel w/VBA, a formal writing in the major course with R). Electives allow students to specialize in different emerging areas of data science such as data engineering, predictive or visualization.

    Graduates of the program are prepared for careers in data science and analytics in any field, as well as for continued study at the graduate level. For more information, email math@qc.cuny.edu or call 718-997-5800 to make an appointment to speak with an advisor.

    Course Requirements

    All students must have completed MATH 151 and MATH 152 (or the equivalents) before beginning the program.

    • MATH 201. Multivariable Calculus (MQR)
    • MATH 231. Linear Algebra I (MQR) (or MATH 237. Honors Linear Algebra (MQR))
    • MATH 241. Introduction to Probability and Mathematical Statistics (MQR) (or MATH 611. Introduction to Mathematical Probability)
    • MATH 310. Elementary Real Analysis (or MATH 320. Introduction to Point Set Topology)
    • MATH 341. Bayesian Models in Data Science and Predictive Analysis
    • MATH 368. Advanced Probability (or MATH 621. Probability)
    • MATH 369. Advanced Statistics (or MATH 633. Statistical Inference)
    • CSCI 111. Introduction to Algorithmic Problem-Solving (MQR)
    • CSCI 212. Object-Oriented Programming in Java (MQR) (or CSCI 211. Object-Oriented Programming in C++ (MQR))
    • ECON 382. Introduction to Econometrics
    • ECON 387. Advanced Econometrics
    • DATA 205. Introductory Analytics (MQR) (or BIOL 230. Biostatistics (SQ))

    Additionally, students must choose three electives from List A and two electives from List B:

    List A

    • DATA 235. Data and Society (SCI, SW)
    • DATA 333. Data Management, Processing, and Visualization
    • CSCI 48. Spreadsheet Programming (MQR)
    • CSCI 211. Object-Oriented Programming in C++ (MQR)
    • CSCI 212. Object-Oriented Programming in Java (MQR)
    • CSCI 220. Discrete Structures
    • CSCI 313. Data Structures
    • BUS 386. Financial Econometrics
    • BIOL 330. Design of Experiments
    • PSYCH 323. Psychometrics
    • Or another relevant course (upon approval by your major advisor)

    List B

    • MATH 202. Advanced Calculus (MQR)
    • MATH 220. Discrete Mathematics
    • MATH 223. Differential Equations with Numerical Methods I
    • MATH 232. Linear Algebra II
    • Any other MATH course numbered 310 or higher.

    The university also has General Education requirements. There are many General Education courses that involve data science concepts; these can be beneficial for a student choosing the Data Science and Statistics option. We recommend the following courses, listed with the core code(s) that are fulfilled.

    • LCD 101. Introduction to Language (LANG)
    • LCD 102. Analyzing Language. (SW, LANG, SCI)
    • PSCI 100. American Politics and Government (USED)
    • PSYCH 101. General Psychology (SW, SCI)
    • PSYCH 213W. Experimental Psychology (LPS, SW, SCI)
    • SOC 101. General Introduction to Sociology (IS)

    LCD 101 and LCD 102 are highly recommended for the student who wishes to learn computational linguistics, an important aspect of modern data science.

  • Do you need skills for entry-level positions involving data?

    The need for data analytics is now relevant to almost every field. The Minor in Data Analytics (20 credits, minor code DATA-MIN) teaches students with little or no background how data are produced, captured, organized, analyzed and presented—and how to perform these data analytics tasks themselves. The curriculum draws upon concepts and skills from statistics, computer science, and computer-based techniques for handling large datasets, as well as capturing and analyzing data and presenting the results of such analyses in a variety of forms, including reports, tables, and visualizations (e.g. charts, maps and graphs).

    Graduates of the Minor are prepared for entry level jobs involving the use of data in government, business, science and technology, policy, education, and research, among others. Because this program is a minor, students are also encouraged to apply their data analytics skills in any major, providing them the advantage of combining valuable data skills with other specializations.

    Course Requirements

    • DATA 235. Data and Society
    • DATA 205. Introductory Analytics
    • DATA 212W. Research Methods
    • DATA 306. Data Modeling
    • DATA 333. Data Processing, Management, and Visualization
    • DATA 334. Applied Research

    The Minor in Data Analytics provides the foundational skills for data literacy as defined by the American Association of College and Universities, and the key competencies identified by the Business Higher Education Forum Task Force for Data Science and Analytics as essential for “data-enabled” graduates, which are in high demand in today’s data-rich job market.

    For more information email datanalytics@qc.cuny.edu or enroll directly into one of DATA 235, 205, or 212W and speak to the instructor about the Minor.

  • Do you want to pursue graduate study in data analytics?

    Like data scientists, data analysts use data to generate meaningful insights that help to solve problems. However, data analysts tend to focus less on machine learning and algorithmic programming, and more on the analysis of data and the communication of findings revealed by analysis. Students interested in careers in data-focused fields, and students interested in solving real-world problems using data, can pursue the Data Analytics and Applied Social Research (MA), which emphasizes research methodology, data analysis, and data visualization using social data - the qualitative and quantitative techniques and processes used to derive actionable information from large data sets.

  • Do you want to learn more about data science and digital humanities?

    To learn more about data literacy and the courses we offer in Data Science and Digital Humanities, see our Courses page.

    To learn more about data and some of its applications, see our Learning Resources page.

    If you have questions about data or data-related careers, see our Frequently Asked Questions page.

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