DRIPBL Research Scholar Application

Our students represent the top of academic excellence and drive. They are the world’s future scholars.

Our three programs are the Individual Research Program (12 weeks), the Premium Research & Publication Program (16-20 weeks), and the Research Fellowship (6-12 months).

As these are rigorous programs, there will also be an interview for shortlisted applicants. If you are conditionally accepted after the interview, you are required to make a deposit of 200 USD, which will then go towards your tuition fee or will be reimbursed if there is not an appropriate match.

If you are applying to the DRIPBL Research Inclusion Foundation, you will be prompted to offer more context on your eligibility in the application.

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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