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DATA LITERACY for DATA SCIENCE & DIGITAL HUMANITIES

Data literacy starts with understanding data. What data are being collected? What is it being used for? What could it be used for? Once you have the basic concepts down, you'll be able to explore more complex topics in data science and digital humanities. We offer courses in data literacy from entry level to advanced. Learn more about your options below.


  • I. Basic Data Literacy: DATA 235

    Basic data literacy involves being able to interpret data visualizations, and recognizing when data is misrepresented or misused.

    Any student interested in data literacy should take Data and Society [DATA 235] - a scientific examination of the relationships of digital technology and big data to the individual and society. Topics include privacy and ethics, artificial intelligence, interpersonal communication, education, business, media, national security and politics, and science and technology. This course also satisfies the Scientific World (SW) requirement.

    Students interested in foundational courses in data-focused fields, or courses in basic data applications may also take:

    • Computers and Computation for Accounting, BALA, Economics, and others [CSCI 12]
    • Elementary Probability & Statistics [MATH 114W]
    • Information and Intelligence [CSCI 100]
    • Python for Data Analysis [CSCI 90]
    • Problem Solving with Computers [CSCI 80]
    • Software for Design [ARTS 191]
    • Spreadsheet Programming [CSCI 48]
  • II. Intermediate Data Literacy: Minor Programs

    Intermediate data literacy includes proficiency in basic data tools and methods, and knowing when to use them.

    The Minor in Data Analytics (20 credits) 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.

    • 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

     
    Students may also be interested in the following minors:

    • Computer Science [CSCI-MIN]
    • Computer Information Technology [CIT-MIN]
    • Computational Linguistics [CSCICL-MIN]
    • Financial Modeling (for majors in CS or Finance) [FINMDL-MIN]
    • Interaction Design [ARTID-MIN]
    • Design [ARTGD-MIN]
    • Animation and Illustration [ARTSAI-MIN]
    • Digital Moviemaking and Imagemaking [ARTSDM-MIN]

     
    Alternatively, students may wish to supplement their majors with data-related courses in a specific field:

    • Applied Programming for Research [SOC 333]*
    • Applied Statistical Analysis [SOC 207]
    • Cyber Security [TBA]*
    • Database Programming in SQL [CSCI 85]*
    • Data Mining and Warehousing [CSCI 334]*
    • Data Structures [CSCI 313]*
    • Digital Imagemaking [ARTS 165]
    • Digital Journalism [JOURN 201]*
    • Digital Recording [MUSIC 318]*
    • Financial Analysis [BUS 356]*
    • Market Research [BUS 344]*
    • Methods of Social Research [SOC 334]*
    • Modern Learning Technologies in the Classroom [EECE 220]*
    • Multimedia Fundamentals and Applications [CSCI 82]*
    • Models of Computation [CSCI 84]*
    • Photoshop Basics [ARTS 195]
    • Research Skills in Political Science [PSCI 200]
    • Statistics as Applied to Economics and Business [ECON 249]*
    • Web Design I [ARTS 214]*
    • Website Development [CSCI 81]*

    *Prerequisites apply.

  • III. Advanced Data Literacy: Major Programs

    In addition to expertise in data tools and methods, advanced data literacy includes being able to think critically about information yielded by data analysis and being able to communicate this information to an audience that lacks data literacy.

    As a large, interdisciplinary field, data science combines technical tools from quantitative disciplines, such as computer science and statistics, with methods of inquiry in other disciplines, including the natural sciences, social sciences and humanities. Students with a serious commitment to developing their data skills may be interested in pursuing one of the following suggested courses of study:

    • Business Analytics = Data Analytics Minor + Financial Modeling Minor + Accounting (BA) or Economics (BS)
    • Computational Linguistics = Data Analytics Minor + Computational Linguistics Minor + Linguistics (BA) or Sociology (BA)
    • Data Engineering = Data Analytics Minor + Computer Science (BS)
    • Data Journalism = Data Analytics Minor + Journalism Minor + Media Studies (BA)
    • Data Science = Data Analytics Minor +  Mathematics: Data Science and Statistics Track (BA)
    • Data Visualization = Data Analytics Minor + BALA Minor + Design (BA)
    • Epidemiology = Data Analytics Minor + Pre-Med Major
    • Financial Analytics = Data Analytics Minor + Computer Science Minor + Finance (BBA)
    • Market Research = Data Analytics Minor + Financial Modeling Minor + Business Administration (BA)
    • Predictive Analytics = Data Analytics Minor + Mathematics: Data Science and Statistics Track (BA)
    • Policy/Political Analysis = Data Analytics Minor + Political Science (BA), Economics (BS) or Sociology (BA)

     
    Alternatively, students may create an interdisciplinary course of study by choosing one or more of the following majors and combining them with one or more of the minors from the previous list in section II, Intermediate Data Literacy:

    • Accounting (BA)
    • Actuarial Studies for Business (BBA)
    • Applied Mathematics, Computer Science Track (BA)
    • Business Administration (BA)
    • Computer Science (BA or BS)
    • Economics (BA or BS)
    • Finance (BBA)
    • International Business (BBA)
    • Mathematics: Data Science and Statistics Track (BA)
    • Political Science (BA)
    • Sociology (BA)

    NOTE: If you major in Business Administration, Computer Science, Mathematics, or Sociology, you may qualify for an ACCELERATED DEGREE.

  • IV. Graduate Level Study in Data Related Fields

    For students who plan on developing their data skills beyond their undergraduate career, the following graduate programs may be of interest:

     
    The Computer Science MA emphasizes the knowledge and skills necessary for Data Engineering: the management of complex data, data structuring and data mining.  The Data Analytics and Applied Social Research (MA) emphasizes research methodology, data analysis, and data visualization.  The Library Science MLS focuses on digitization and storage. And the Risk Management MA offers tracks in Actuarial Studies and Dynamic Financial Analysis.

 

SOFT SKILLS AND CRITICAL THINKING SKILLS for DATA SCIENCE

Whatever level of data literacy you aim to achieve, it is important to develop your soft skills and critical thinking skills, in addition to technical skills:

Soft Skills in Data ScienceCritical Thinking Skills in Data Science
    • Defining the problem
    • Knowing your audience
    • Presentation and storytelling
  • Creativity
  • Logical thinking
  • Critical observation

 
Perhaps more important than any skill will be your ability to think critically about the ethical implications and social impacts of data on society. For example, you will often have to think about the appropriate use of information. Data may be out of date, incomplete, or insufficient to draw meaningful insights from. Data may contain sensitive information. And the collection of large amounts of data can have important consequences for freedom, democracy and privacy. In sum, remember that the power of data can be used for either altruistic or nefarious purposes. Learn more  here, hereand here.


  • Courses in Soft Skills/Critical Thinking Skills

    • Business and Liberal Arts (BALA)BALA 103W - Critical Thinking in Business
      • BALA 165  -  Oral Communication in the Workplace
      • BALA 303 - Analytical Problem Solving and Decision Making in Business
    • MEDST 151 - Public Speaking
    • MEDST 357 - Media, Law, and Ethics
    • PHIL 104 - Introduction to Ethics
    • PHIL 109 - Modern Logic
    • PHIL 120 - Contemporary Issues in Philosophical Perspective
    • SOC 235 - Data and Society
    • URBST 248 - Organizational Behavior and Urban Politics.

 

CO-CURRICULAR OPPORTUNITIES in DATA SCIENCE

Success in any field is not determined by classes alone. Be sure to take advantage of the following co-curricular programs on campus:


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