Summer Extension of RIC

$1,450.00

Summer extension to continue or jumpstart RIC projects over the summer.

Availability: 100 in stock

SKU: DRP-SUMMEREXTE-003 Category: Tags: , , ,

This is an extension of the RIC goals to get a headstart on OR continue their projects over the Summer.

Free consultation: 1 (20-minute).
Joint sessions with scientist instructors: 12.
Individual writing coach sessions: NA.
Dual enrollment eligibility: No.
Time: +2 months (June to August roughly). Meeting time online: Once a week (1 hr). Total meetings included: 12.
Pricing: $1200 Early Bird / $1450 Late Registration. Deadlines: Early Bird: February 28th; Late Registration: Until April 15th.
Add-ons: Individual Mentor Sessions.

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Data Science and Data Analytics for Research 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