data science & ai

Data Science & AI challenges members to demonstrate their understanding of data analysis, machine learning, and the principles of artificial intelligence through an objective test. This event introduces members to the role of data and AI in solving real-world problems and driving innovation across industries.

Event Overview

Division: High School
Event Type: Individual
Event Category: Objective Test, 100-multiple choice questions (breakdown of question by competencies below)
Objective Test Time: 50 minutes
Career Cluster Framework Connection: Digital Technology
NACE Competency Alignment: Career & Self-Development, Critical Thinking, Technology

Items Competitor Must Provide: Sharpened pencils, Photo Identification, Conference-provided nametag, Attire that meets the Florida FBLA Dress Code

Objective Test Competencies:

· Probability and Statistics Foundations

· Data Analysis and Statistics for AI

· Tools for Data and AI

· AI Basics

· Machine Learning

· Perception, Representation, and Reasoning

· Privacy and Ethics

· Data Literacy and Foundations

Test questions are based on the knowledge areas and objectives outlined for this event. Detailed objectives can be found in the study guide included in these guidelines.

District

Check with your District leadership for District-specific competition information.

State

Eligibility

·         FBLA membership dues are paid by 11:59 pm Eastern Time on December 1 (or earlier date specified by District Director) of the current program year.
·         Members may compete in an event at the State Leadership Conference (SLC) more than once if they have not previously placed in the top 10 of that event at the National Leadership Conference (NLC). If a member places in the top 10 of an event at the NLC, they are no longer eligible to compete in that event.
·         Members must be registered for the SLC and pay the state conference registration fee in order to participate in competitive events.
·         Members must stay within the official FBLA housing block of the official FBLA hotel to be eligible to compete.
·         Each district may be represented by participant(s) based on the Florida FBLA scaled quota system found on the Florida FBLA website.
·         Each member can only compete in one individual/team event and one chapter event (Community Service Project, Local Chapter Annual Business Report).

·         Identification: Competitors must present valid photo identification (physical) that matches the name on their conference name badge. Acceptable forms include a driver’s license, passport, state-issued ID, or school ID.
·         If competitors are late for an objective test, they may be either disqualified or permitted to begin late with no extension of the time as scheduled.
·         Participants must adhere to the Florida FBLA dress code established by the Florida Board of Directors or they will not be permitted to participate in the competitive event.

Recognition

·         The number of competitors will determine the number of winners. The maximum number of winners for each competitive event is 5.

Event Administration

·         This event is an objective test administered at the SLC.
·         No reference or study materials may be brought to the testing site.
·         No calculators may be brought into the testing site.

Scoring

·         Ties are broken by comparing the correct number of answers to the last 10 questions on the test. If a tie remains, answers to the last 20 questions on the test will be reviewed to determine the winner. If a tie remains, the competitor who completed the test in a shorter amount of time will place higher.
·         Results announced at the State Leadership Conference are considered official and will not be changed after the conclusion of the State Leadership Conference.

Americans with Disabilities Act (ADA)

·         FBLA complies with the Americans with Disabilities Act (ADA) by providing reasonable accommodations for competitors. Accommodation requests must be submitted through the conference registration system by the official registration deadline. All requests will be reviewed, and additional documentation may be required to determine eligibility and appropriate support.

Penalty Points

·         Competitors may be disqualified if they violate the Competitive Event Guidelines or the Honor Code.

Electronic Devices

·         Unless approved as part of a documented accommodation, all cell phones, smartwatches, electronic devices, and headphones must be turned off and stored away before the competition begins. Visible devices during the event will be considered a violation of the FBLA Honor Code.

National

If you are competing on the National level, be sure to see the National guidelines at https://www.fbla.org/divisions/fbla/fbla-competitive-events/

Study Guide: Knowledge Areas and Objectives

Probability and Statistics Foundations (15 test items)

1. Calculate the mean, median, mode, and range of a dataset

2. Discuss the use of measures of statistical variance (e.g., standard deviation, variance, covariance)

3. Discuss the characteristics and importance of Gaussian (normal) distribution

4. Calculate the expected value of a random variable

5. Differentiate between types of variables (e.g., continuous, discrete)

Data Analysis and Statistics for AI (15 test items)

1. Select the most appropriate visual medium to display a dataset

2. Describe different types of diagrams (e.g., boxplots, histograms, scatterplots)

3. Discuss techniques for working with multivariate data (e.g., dependence and interdependence methods, multiple linear and logistic regression)

4. Discuss the importance of cleaning data

5. Identify factors that may affect data quality (e.g., duplicates, low quality sources, incomplete datasets)

6. Describe how data science algorithms are applied to real-world problems (e.g., linear regression, decision trees, k-means)

Tools for Data and AI (10 test items)

1. Write queries in SQL

2. Describe common packages and libraries for working with data and AI (e.g., Pandas, NumPy, PyTorch)

3. Discuss the use of Python for cleaning and wrangling datasets

4. Discuss the use of R for data science

5. Describe characteristics of relational databases

AI Basics (10 test items)

1. Discuss the nature of generative AI

2. Discuss capabilities and limitations of generative AI

3. List uses of generative AI (e.g., healthcare, research, digital art)

4. Describe AI subfields (e.g., computer vision, NLP, human interaction, robotics)

5. Define large language models (LLMs)

6. Discuss the capabilities of large language models (LLMs)

Machine Learning (10 test items)

1. Discuss the nature of machine learning

2. Describe the use of training, test, and validation datasets

3. Describe how machine learning algorithms behave (e.g., neural networks, decision trees, learning functions)

4. Characterize unsupervised, supervised, and reinforcement learning algorithms

5. Select an appropriate machine learning algorithm to solve a reasoning problem (e.g., supervised, unsupervised, reinforcement)

6. Explain the concept of deep learning

Perception, Representation, and Reasoning (10 test items)

1. Explain how predicate logic is used in AI models

2. Give examples of predicate logic

3. Discuss differences between logic-based and probability-based reasoning

4. Describe Bayesian networks and their components (e.g., nodes, edges, Directed Acyclic Graphs)

5. Discuss the nature of knowledge representation and reasoning for AI

Privacy and Ethics (10 test items)

1. Discuss dilemmas that arise from AI systems (e.g., self-driving vehicles, generative AI, surveillance)

2. Describe how AI inherits bias (e.g., algorithmic bias)

3. Discuss security and privacy risks associated with LLMs

4. Discuss credibility concerns of LLMs (e.g., hallucinations, misinformation)

Data Literacy and Foundations (20 test items)

1. Discuss the nature of data science

2. Describe differences between structured and unstructured data

3. Identify numeric and categorical data points

4. Convert among common data representations (e.g., binary, hexadecimal, decimal)

5. Describe the types of data that could be gathered from various sources

6. Describe the importance of data wrangling and transformation

7. Describe the stages of the data science process