Data Science Accelerators (75 Hours)

Data Sheet

The Data Science accelerators are non-credit intense training sessions given daily over the course duration of five weeks. All accelerators include a theoretical component and a significant hands-on component, including in-class problem-solving using popular software packages on real-world datasets.

The accelerators are targeted to practitioners who may have diverse sets of experience and knowledge and do not wish to pursue a degree program. The goal of these accelerators is to provide a wide range of training from refresher courses to theoretical and practical aspects of data science.

Schedule: Accelerators are offered:

  1. Mornings: Mon-Fri: 9 a.m. – 12 p.m.
  2. Evenings: Mon-Fri: 6 p.m. – 9 p.m.​



Cost: $7,500 / accelerator

Accelerator Prerequisites Outcomes
Refresher

Basic mathematics, statistics and Python programming.		 None Become familiar with the math and computer science fundamentals required to continue to a Data Science career.
Data Analytics

Data management, analytics and visualization.		 None Perform data analysis, reporting, and design visual dashboard solutions.
Data Engineering

Big Data infrastructure and data modeling, integration and pipeline processing.		 Basic programming skills, data structures Prepare the big data infrastructure and provide data to be analyzed by data scientists.
Basic Data Science

Basic machine learning using Python and Scikit-learn.		 Basic Python programming, Data structures and algorithms, Calculus and Linear algebra,Probability and Statistics Apply methods in Python scikitlearn library to perform classification, regression, and clustering of data.
Advanced Data Science

Advanced machine learning,time series and visualization.		 Basic Python programming, Data structures and algorithms,Calculus and Linear algebra, Probability and Statistics, basic machine learning Apply methods in Python scikitlearn library to perform feature extraction, visualization and predict time-dependent variables.
Deep Learning (AI)

Neural networks and Artificial Intelligence (AI).		 Basic Python programming, Data structures and algorithms, Calculus and Linear algebra, Probability and Statistics, basic machine learning Design and build deep learning models in Keras. Apply AI techniques on GPU platforms.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Program Contact:

Ryan Mass

973-596-3178

ryan.m.mass@njit.edu

For more information visit jerseycity.njit.edu