Courses at CAIDS

Spring Course Offerings 

DATA 1010: Introduction to Data
DATA 1010 aims to provide students with an overview to what data is, how it is used correctly and incorrectly, how it is found, stored, and managed, and how it can be used as a basis for decision making and analysis. The overall goal of this course is to increase data literacy, such that students are more confidently able to work with the increasing amounts of data in their lives, jobs, and academic careers. This course is aimed towards students in all schools and fields and has no prerequisites. Satisfies Formal Reasoning requirement. 

• MWF 10:00 – 10:50am, Dr. Lisa Dilks

• TR 11:00am - 12:15pm, Dr. John Levendis

• TR 3:30 - 4:45pm, Dr. Jacquelyne Thoni Howard
   

DATA 2150: Artificial Intelligence Tools 

The introduction of widely available and accessible generative Artificial Intelligence tools, such as ChatGPT, democratizes expertise, unlocks knowledge, and bestows impressive abilities. This hands-on course provides students with practical experience employing generative AI to perform real-world tasks. By the end of the course, students will be able to effectively collect accurate historical and real-time information, generate high-quality text and media, transform content between formats, analyze data to derive insights and deploy generative AI to tackle private and professional challenges.

• TR 9:30 – 10:45am, Dr. Julia Lang

DATA 2810: Special Topics – Introduction to Artificial Intelligence

This course traces the evolution of AI from the perceptron to the modern day large language models. Students will be exposed to the major branches of AI and the key concepts that allow computers to mimic (limited) human intelligence. Along the way, students will understand the strengths and weaknesses, and different capabilities, of different AI architectures. Students will be able to speak intelligently about the current types of AI, and they will be able to identify constructive use cases, as well as potential risks and dangers of AI.

• TR 12:30 – 1:45pm, Dr. John Levendis

DATA 3010: Introduction to Data Collection and Wrangling

This course provides an intensive introduction to data collection, wrangling, and summarization using the R programming language. Students will learn the fundamental skills required to collect, re-shape, transform, manipulate, analytically explore, summarize, and visualize data. Students will learn how data must be organized and formatted in order to perform effective data analysis or be inputted into a machine learning algorithm. Further, students will learn how to produce data-driven dynamic web applications. The time students allocate to learn these data-related skills will allow them to create data sets that promote more efficient, reproducible, and understandable data science products. The course is designed for students from any major with real-world examples drawn from a variety of domains. Students of all skill levels are welcome, including those with limited or no statistical, mathematical, or programming backgrounds. All necessary skills will be taught in class.

• MWR 11:00 – 11:50am, Dr. Lisa Dilks

DATA 3530: GIS and Mapping Global Issues

Geographic information systems (GIS) involve creating, storing, retrieving, analyzing, and visualizing spatial data. This course examines the global impact on social, political, economic, and environmental dynamics when using geographic information systems (GIS), global positioning systems (GPS), and other geospatial technologies in daily life. Readings and discussions will focus on global affairs, such as critical cartography, GIS integration with social theories, implications for crime, urban planning, scientific research, health, environmental justice, feminist perspectives, and the intersection of economic development with environmental shifts. This course will also introduce students to foundational concepts and skills in working with spatial data, including finding and creating data, spatial analysis, and GIS-based map production. Specific global affairs topics will be analyzed using ESRIs ArcGIS. Students will gather GIS data, analyze global affairs topics using GIS, and produce their own data projects. Satisfies Global Perspectives and Social & Behavioral Science requirements. 

• TR 2:00 – 3:15pm, Dr. Jacquelyne Thoni Howard 

Summer Course Offerings 

DATA 1010: Introduction to Data

DATA 1010 aims to provide students with an overview to what data is, how it is used correctly and incorrectly, how it is found, stored, and managed, and how it can be used as a basis for decision making and analysis. The overall goal of this course is to increase data literacy, such that students are more confidently able to work with the increasing amounts of data in their lives, jobs, and academic careers. This course is aimed towards students in all schools and fields and has no prerequisites. Satisfies Formal Reasoning requirement. 

• Early Summer, Online, Dr. John Levendis

DATA 2150: Artificial Intelligence Tools 

The introduction of widely available and accessible generative Artificial Intelligence tools, such as ChatGPT, democratizes expertise, unlocks knowledge, and bestows impressive abilities. This hands-on course provides students with practical experience employing generative AI to perform real-world tasks. By the end of the course, students will be able to effectively collect accurate historical and real-time information, generate high-quality text and media, transform content between formats, analyze data to derive insights and deploy generative AI to tackle private and professional challenges.

• NTC Early Summer, MTWRF 10:00-2:00, Prof. Knud Berthelesen

The following curriculum is being proposed to Tulane as a potential certificate offering. Students interested in the proposed certificate should use this information only for preliminary planning purposes. However, they should also be aware that this curriculum offering has not been approved at this time and may change.
 

The 12-credit hour certificate in Data provides students with a solid foundation in data literacy while also providing flexibility to explore areas of interest in data science. The required course - DATA 1010: Introduction to Data - teaches students the core principles and skills of data literacy: the ability to read, work with, analyze, and communicate about data. Students are then free to choose three additional data-focused electives, offering students the opportunity to focus their learning in key data competencies such as analysis, visualization, communication, and artificial intelligence or explore a variety of data science topics. The flexibility of the certificate curriculum is a complement to all undergraduate majors. 

Requirements: 

• Foundational Requirement (1 course, 3 credits): Data 1010 - Introduction to Data
   
• Elective Courses (3 courses, 9 credits): Three (3) additional courses in DATA. 

Additional Requirements:

• A minimum of 50% of the courses applied toward the Certificate of Data must be taken in the CAIDS curriculum (i.e., courses with a DATA prefix or cross-listed with DATA). 
• No more than one course from the Certificate in Data may count toward a student's major. 
• Courses applied toward the Certificate in Data may not be applied toward a student's other certificate(s) or minor(s).
• Transfer credits and Study Abroad courses that a student wishes to count toward the Certificate in Data will be evaluated on a case-by-case basis. 
• The certificate must be earned concurrently with the undergraduate degree.   

The following curriculum is being proposed to Tulane as a potential minor offering. Students interested in the proposed minor should use this information only for preliminary planning purposes. However, they should also be aware that this curriculum offering has not been approved at this time and may change.
 

The 18-credit hour Minor in Data provides students with a solid foundation in data literacy and data science skills necessary to navigate an increasingly data-driven society. Required coursework encompasses the core principles of data literacy - the ability to read, work with, analyze, and communicate about data - while offering a diversity of data applications for students of all majors. Courses offered not only teach students how to utilize and understand numerical data and visualizations but also text, qualitative, and spatial data. The curriculum progresses to build students' capacity to critical evaluate, interpreter, and communicate data insights. 

Requirements: 

• Foundational Requirement (1 course, 3 credits): Data 1010 - Introduction to Data
   
• Communication Requirement (1 course, 3 credits): Choose 1 of the following. 
  • Data 2030: Data Visualization
  • Data 3030: Data Science Research and Communication (in process to change to 2060). 
     
• Analysis Requirement (1 course, 3 credits): Choose 1 of the following. 
  • Data 3520: Data Analysis (previously 2020)
  • Data 3530: GIS and Mapping Global Issues 
  • Data 2040: Text and Qualitative Data Analysis (will be changed to 3540 after Fall 2024). 
     
• Data Collection and Management (1 course, 3 credits): Data 3010: Introduction to Data Collection and Wrangling
   
• Elective Courses (2 courses, 6 credits): Two (2) additional courses in DATA. 

Additional Requirements: 

• A minimum of 50% of the courses applied toward the Minor of Data must be taken in the CAIDS curriculum (i.e., courses with a DATA prefix or cross-listed with DATA). 
• Transfer credits and Study Abroad courses that a student wishes to count toward the Certificate in Data will be evaluated on a case-by-case basis. 
• In accordance with NTCh policy, no courses counting toward the student's first minor will count toward the student's other minors. 
• Courses applied toward the Minor in Data may not be applied to a student's certificate(s). 
• The certificate must be earned concurrently with the undergraduate degree.