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 bridge the gap between theoretical knowledge and practical application, preparing students for the complexities of the real world. 

Dream: The first step in the DRIPBL process is to encourage students to dream. This involves cultivating a sense of curiosity and imagination, inspiring students to envision a world where they can make a positive impact.

Research: DRIPBL places a strong emphasis on research skills. Students are encouraged to delve into topics of interest, conduct thorough investigations, and explore multiple perspectives. This not only deepens their understanding of the subject matter but also instills a love for learning that extends beyond the classroom.

Innovate: Students are challenged to think creatively, explore unconventional solutions, and develop a sense of resilience in the face of challenges. Innovation is not just a buzzword but a practical skill set that students can apply to real-world scenarios.

Problem/Project-Based Learning: Students are presented with real-world challenges or projects that require critical thinking, collaboration, and the application of knowledge acquired through dreaming and research. This hands-on experience equips students with practical skills that go beyond rote memorization.

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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