Data Science Accelerators

Non-credit intense sessions that provide practitioners a wide range of training from refresher courses to theoretical and practical aspects of data science.

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