Data Science FAQ

FREQUENTLY ASKED QUESTIONS

What is data?

While the terms ‘data’ and ‘information’ are often used interchangeably, in the context of computing, data refers to distinct pieces of digital information in its unprocessed or unorganized form. Because data are not easily interpreted, we rely on software and machines to help us process and interpret data. Data Science and Digital Humanities constitute the ways in which we use technology to help us glean insight from data.

What is BIG data?

Big data refers to extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

To learn about the history of digital data and how it got BIG, check out this dynamic article from Thinkful.

To learn about the concerns of Big Data and why data professions require ethical practitioners, check out this article from WIRED.

Why is data literacy important?

Data literacy is a term used to describe an individual’s ability to read, understand, and utilize data in different ways. As data collection and sharing are embedded in almost every aspect of modern life, it is increasingly important to be data literate. Much like literacy as a general concept, data literacy focuses on the competencies involved in understanding data:

  • Basic data literacy involves being able to interpret and understand basic data visualizations, statistics, and infographics, and perhaps most importantly, being able to recognize when data is misrepresented or misused. Learn more here, here, and here.
  • Intermediate data literacy involves proficiency in basic data tools and methods, including knowing when to use them (e.g., qualitative vs quantitative data, surveys vs interviews, tables vs line charts, etc.).
  • Advanced data literacy involves expertise in data tools and methods, which includes being able to think critically about the information yielded by data analysis and being able to communicate this information to an audience that lacks data literacy. To learn more about data literacy, check out this article from Tableau.

 

The Minor in Data Analytics (20 credits, 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. 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

 

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

Which careers involve data art?

Since we react to the aesthetics of visual information just as much as we react to the information itself, how we present information is equally important – design and content go hand in hand. Data art is digital information presented in a way that is both aesthetically appealing and easily comprehensible. Every graph, every PowerPoint presentation, every website, and every email involves data art and design to some degree.

Aside from working as an artist that uses data as a medium, jobs involving data art generally involve being able to tell a story graphically. Such skills are sought after in data analysts, but some companies look to employ dedicated Data Visualization Designers/Editors/Developers. Careers involving data art can be found in:

  • News agencies: Develop infographics, such as maps, charts, and graphs, often as a data journalist.
  • Design studios: Create graphic designs and digital art.
  • Analytics departments: Make data easier to comprehend for decision-making by creating interactive data visualization dashboards.
  • Research labs: Find new ways to represent and visualize data, often at universities or data visualization companies.
  • Freelancing: Develop a compelling portfolio and advertise to businesses that require help with data visualization from time to time.

Apollo Missions by Paul Button

Which careers involve data science?

Data science is an interdisciplinary field that combines technical tools from quantitative disciplines to methods of inquiry in other disciplines. Careers that involve data science generally involve being able to structure data, interpret data, effectively communicate information gleaned from data, and provide evidence-based recommendations and actionable insights. For this reason, knowledge of statistics and technical skills in statistical software (e.g. SPSS, SAS, R), scripting language (e.g. Python), and querying language (e.g. SQL) are key.

These careers also emphasize creative problem solving, critical thinking, teamwork, communication, and asking interesting questions. Because data and digital technologies have become an integral part of nearly every sector, many professions now involve working with data, including jobs that allow you to take on real-world problems in education, government, health, energy, public safety, transportation, economic development, international development, and others. Examples include:

  • Biostatistician
  • Business Intelligence Specialist
  • Cartographer
  • Climatologist
  • Computer Security Analyst
  • Epidemiologist
  • Financial Analyst
  • Data Analyst
  • Data Engineer
  • Data Visualization Developer
  • Data Journalist
  • Database Manager
  • Machine Learning Engineer
  • Market Analyst
  • Policy Analyst
  • Research Coordinator
  • Sports Analyst
  • Social Network Analyst
  • Survey Researcher

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 BA 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 analytics 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.

Who can I speak to if I want to learn more?

For general questions about QC’s INTERDISCIPLINARY DATA COLLABORATIVE, contact Dr. Dan Weinstein.

For questions about data and how it relates to a specific field, make an appointment with a faculty member below:

Where can I learn more about data and its applications?

  • You can explore some of the applications of data through the videos below.

    Data Science

    Data Visualization

    Data Journalism

    Storytelling with data

    Digital Arts and Design

    Data and Applied Statistics

    Data and Public Policy

    Introduction to Big Data

    Thinking Critically about Big Data and Data Policy