Knud Berthelsen
SLAM 2031 Applied Data Analysis at Tulane, paper collage of data points streaming over the Mississippi River into a New Orleans skyline

SLAM 2031 · Tulane University · Summer

Applied Data Analysis

Learn to collect, clean, analyze, and present data, with and without AI.

  • 3 credits
  • Online, early summer
  • Formal Reasoning core

Sooner or later, someone hands you a spreadsheet and asks what it means.

This course teaches you how to answer. You turn a question into one that data can answer, design a survey, collect your own data, clean it, analyze it, and present what you found. You do each step with and without AI, so you know what a correct answer looks like and can tell when the AI gets it wrong.

You don't need a math or programming background. The course starts with the basics and uses Google Sheets and free AI tools, with no prerequisites and no textbook.

You finish a core requirement over the summer, from wherever you are.

Applied data analysis, paper collage of a hand holding a magnifying glass over scattered data points and a spreadsheet with a few cells highlighted
3

credits

Satisfies Formal Reasoning

5

weeks

Online in the early summer session

12

modules

Videos, exercises, and assignments on Canvas

4.8

out of 5

Professor Berthelsen’s instructor evaluations

What it counts for

SLAM 2031 satisfies the Formal Reasoning requirement in the Newcomb-Tulane College core and the Economic & Financial Literacy requirement in the Strategy, Leadership & Analytics Minor.

Course requirements, paper collage of three puzzle pieces with a pie chart, a bar chart and a scatter plot fitting together

NTC Core

Formal Reasoning

Core curriculum requirement

SLAM

Analytics Outcome

Strategy, Leadership & Analytics Minor

SLAM

Economic & Financial Literacy

Strategy, Leadership & Analytics Minor

AI Literacy Minor

Problem Solving with AI

Proposed for this category; approval is pending

Confirm with your advisor how the course fits your degree plan.

Summer data analysis course at Tulane, paper collage of May and June calendar pages shaded through late June beside an open July page with a sun and a beach towel, under a live oak by the Mississippi River at sunset

Why take it this summer

  • Done by the end of JuneYou finish a core requirement and three credits in the early summer session, with July still free.
  • Fits around a job or internshipThere are no set meeting times. You work on your own schedule, within each module’s deadlines, from wherever you are.
  • More room in your regular semesterThree credits done in the summer free up time in the fall or spring (or help you catch up after dropping another course).

With and without AI

Each topic is covered in three steps, so you learn both the method and when to trust the tool.

Analysis with and without AI, paper collage of a hand checking a spreadsheet with a pencil, linked by coral threads to the same table on an AI screen
1

Without AI

You apply the technique yourself, so you know what a correct answer looks like.

2

With AI

You do the same work with current AI tools.

3

Compare

You compare the results, explain the differences, and note what the AI got wrong.

The analysis pipeline

Your projects follow the full pipeline, from formulating a question to making a recommendation.

Data analysis pipeline, paper collage of a question mark dissolving into data tiles that become ordered dots, rising bars and an arrow pointing forward
  1. 01

    Ask

    Turn a problem into a question data can answer

  2. 02

    Collect

    Find sources and design surveys

  3. 03

    Clean

    Find and fix quality problems in the data

  4. 04

    Analyze

    Describe what happened, explain why, and predict what comes next

  5. 05

    Tell the story

    Present the findings with clear charts and a narrative

  6. 06

    Call out misuse

    Spot misleading charts, bad samples, and false causation

Twelve topics

From the first question to the ways data goes wrong: truncated axes, bad samples, and correlation mistaken for causation.

Misuse of data, paper collage of a bar chart with a broken, truncated axis next to a scatter plot with a tangled thread between two unrelated points
  1. 01

    What is data analysis?

  2. 02

    Tools for data analytics

  3. 03

    Formulating questions and research design

  4. 04

    Data types and sources

  5. 05

    Data collection and survey design

  6. 06

    Data cleaning and quality

  7. 07

    Descriptive and exploratory analysis

  8. 08

    Diagnostic analysis and pattern recognition

  9. 09

    Predictive and prescriptive analysis

  10. 10

    Data visualization and storytelling

  11. 11

    Statistical fallacies and misuse of data

  12. 12

    Validity, limitations, and ethics

Online data analysis projects, paper collage of one student working alone and a team of four working together, each turning their analysis into a recorded video presentation

Two projects

  • Your own projectYou pitch a question you care about, collect and analyze the data, and present and defend your findings in a video.
  • A team projectYour team develops a project, analyzes the data, and presents and defends its findings in a video.

How the summer course works

  • Online and asynchronousThere are no class meetings. You work through twelve modules on Canvas and take part in online discussions.
  • No prerequisitesThe course is open to all majors and starts with the basics.
  • No textbookRequired materials are provided through Canvas.
  • No final examGrades come from Canvas assignments, participation, the two projects, and peer assessment.
  • What you needA laptop and a free Google account, for Google Sheets and free AI tools.

The next summer session runs May 24 to June 25, 2027.

Online summer data analysis course, paper collage of three students working on laptops in a dorm room, a kitchen and on a porch, linked by threads to one shared chart
Checking AI output, paper collage of a hand holding a magnifying glass over AI-generated charts on a laptop, with coral threads linking the charts to an open book and a spreadsheet

Using AI in this course

You are expected to use AI, and the course trains you to use it well. You also check what it gives you: you verify AI output, describe how you used AI in your submissions, and back every claim with credible data and an inline reference to its source. An AI tool alone is not a credible source.

An open paper door in a dark green wall, with a river of data dots flowing out of a bright valley beyond

The instructor

Knud E. Berthelsen

Knud Berthelsen is a Professor of Practice at Tulane University and Senior Advisor for AI Literacy at the Connolly Alexander Institute for Data Science. He also teaches DATA 2150 Artificial Intelligence Tools and SLAM 3111 AI and Society, and consults for and trains organizations on how AI works and how to work with it. Before focusing on AI, he spent a decade teaching international strategy and new venture management at Tulane's Freeman School of Business.

More in his CV and on the Teaching page. Questions about the course? Contact Professor Berthelsen.