Data Analytics Bootcamp New York

MIM Data Science Bootcamp New York

Designed and built by MIT, Carnegie Mellon University, and University of Waterloo graduates to meet the needs of the most sophisticated and demanding tech and Fintech employers

During this 3-month intensive bootcamp, you will learn to apply latest techniques needed for the analysis of Big Data. You will also learn Machine Learning models and their applications in the real world. A preparation phase of three weeks is necessary to facilitate participation in the program.

All students must complete a six-month internship upon completion of the bootcamp to acquire professional experience and apply the techniques learned during the program,. International students receive a J1* visa, which allows them to be paid during the internship
 
*MIM is a J1 visa sponsor for academic programs and internships

Start Dates

Fall : September 27, 2021
Winter: January 11, 2022
Spring: April 12, 2022
Summer: July 12, 2022

Entrance Requirements

Associate
/Bachelor Degree
* TOEFL PBT450/
IBT45 or equivalent

Tuition Fees

$14,000
Internship upon completion of the program

Program Schedule

Prep. Phase

80 hours of Programming in Python, probability and statistics

Week 1 Week 2 Week 3 Week 4

Data Science Fundamentals

  • Python for Data Science
  • Introduction to Big Data analysis: data preparation and wrangling; data exploration objectives and methods: structure and unstructured data
  • Project: Big Data analysis using Python

Week 5 Week 6 Week 7 Week 8

Data Science Models

  • Introduction to Machine Learning: supervised and unsupervised learning
  • Supervised Machine Learning: Support Vector Machines,       
  • k-Nearest Neighbor, ensemble Learning and Random Forest
  • Model Training
  • Project: Implementation of Supervised Machine Learning Model 

Week9 Week10 Week11 Week 12

Data Science

Advanced Topics and Application

  • Supervised Machine Learning Algorithms: PCA
  • Machine Learning Neural Networks, Deep Learning Nets, and Random Forest
  • Project: Implementation of Unsupervised Machine Learning Model
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