Rigorous On-Site Bootcamp in the Evenings

Offered in New York, Boston and Washington DC

USD 6,000.00
See Batches

There are no active batches for this course. If you have any question feel free to contact us


Industry recognized certification enables you to add this credential to your resume upon completion of all courses

Need Custom Training for Your Team?
Get Quote
Call Us

Toll Free (646) 793 6300

Inquire About This Course


Career Transformation Evening Bootcamp (8 weeks)

Course Description

Our career development courses are designed for working professionals with experience in industry looking to up-skill or cross train to a Data Scientist position. Courses are designed for IT/Finance/Engineering/Healthcare professionals who have experience coding. These are 8-week (16 week nights) courses covering theory, coding and labs. You will learn all the necessary skills to help you transition into a career in Data Science.

What am I going to get from this course?

You will be able to apply Deep Learning techniques, primarily TensorFlow to solve real-world business problems and extract insights and value from business data sets. This is a career transformation course which will prepare candidates for a transition into Data Science. 

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Students should have working experience in python (or a related language), elementary statistics, basic calculus and linear algebra. A background in physical sciences, computer sciences, or mathematics is ideal. This course is better suited for candidates coming from a professional background. 

Who should take this course? Who should not?

Candidates who have several years industry experience in software and/or systems engineering in any business vertical. Candidates should be comfortable with statistics and/or mathematics and preferably come from a natural or computer science background. Candidates with very little business domain experience are advised to enroll in the immersive or weekend courses. 


Module 1: Week 1

Lecture 1 Data Science Overview

Background of Data Science, Overview of the Marketplace, Datasets, Issues around Data Science Privacy, Overview of Data Science Tools and Frameworks

Module 2: Week 2

Lecture 2 Statistics and Hadoop

Statistical methods applied to Data Science, Classical vs Bayesian Statistics, Overview of Hadoop including, Yarn, Ambari, and MapReduce, Labs

Module 3: Week 3

Lecture 3 Spark

Overview of the latest Spark Release 2.0. , Data Streams and Data Structures, Spark, Applications, Introduction to Flink, Labs

Module 4: Week 4

Lecture 4 Python

Basic Python overview, Python libraries including Pandas, SciPy and NumPy, Scikit-Learn, Labs

Module 5: Week 5

Lecture 5 Machine Learning Intro

Overview of Machine Learning libraries., Julia Programming Language, Supervised learning, Labs

Module 6: Week 6

Lecture 6 Machine Learning I

Unsupervised learning, Reinforcement learning, Labs

Module 7: Week 7

Lecture 7 Deep Learning

Overview of Deep Learning Frameworks, TensorFlow and Torch, GPU computing, TensorFlow Lab

Module 8: Week 8

Lecture 8 Deep Learning II

Computer Vision – CNN, Natural Language Processing – RNN, TensorFlow Lab, Wrap up