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Double Win for Navneeth Badri!

Congratulations to Navneeth Badri for securing 1st place at the Washington Science and Engineering Fair and winning the prestigious Lemelson Early Inventor Prize! 🏆🔬 Your

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Time Innovative Teacher

Time’s first-ever list of Innovative Teachers is out. I’m honored to be a part of this group www.time.com/innovative-teachers View Article

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The Interdisciplinary Nature of STEAM

STEAM encompasses the diverse fields of Science, Technology, Engineering, Art, and Mathematics. These categories work together with interdisciplinary relationships in order to create the world

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STEAM

DRIPBL goes beyond the conventional classroom setting, challenging students to dream big, conduct research, innovate, and engage in hands-on problem/project-based learning. At its core, DRIPBL seeks to

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Data Science and Data Analytics for Business Project

Dr. Calvin Williamson

Professor, Science and Math, State University of New York & Fashion Institute of Technology

Prof. Rajasekhar Vangapaty

Academic Advisor, Fashion Institute of Technology
State University of New York
Founding Member and President of Empowerment Skills International

Schedule: 2 days per week (Tuesday & Thursday)

 

What you’ll learn

Regression

  • Regression
  • Simple Regression
  • Multiple Regression
  • Applications
  • Conjoint Analysis

Introduction to Python

  • Google Colab Notebook
  • Variables, DataTypes
  • Lists, Strings
  • Functions

Machine Learning

  • Classification, Accuracy
  • Training, Testing
  • Decision Trees
  • Pandas, Dataframes

Understanding AI and it’s proper use

Dr. Calvin Williamson

Professor, Science and Math, State University of New York & Fashion Institute of Technology

Prof. Rajasekhar Vangapaty

Academic Advisor, Fashion Institute of Technology
State University of New York
Founding Member and President of Empowerment Skills International

Schedule: 2 days per week (Monday & Wednesday)

 

What you’ll learn

Introduction to Python for Artificial Intelligence

  • Google Colab Notebook
  • Using LLM as Coding Assistant
  • Calculations
  • Variables
  • DataTypes
  • Lists
  • Dictionaries
  • Functions
  • Dataframes
  • f-Strings

Introduction to Large Language Models (LLMs)

  • LLM Examples (GPT, Claude, Gemini)
  • Completions, APIs
  • Prompting
  • Prompt Chaining
  • Roles and Personas
  • Chain of thought
  • Few-shot and zero-shot Learning