Rigorous On-Site Bootcamp During Evenings

Offered in New York, Boston and Washington DC

USD 6,000
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Industry recognized certification enables you to add this credential to your resume upo completion of all courses

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Instructor

Instructor:

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?

This is a career transformation course which will prepare candidates for a transition into Data Science. 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. 

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. 

Curriculum

Module 1: Week 1

Lecture 1 Data Science & Deep Learning

Background of Data Science and Deep Learning, Cloud computing platforms, Databases - SQL and NoSQL, Overview and analysis of algorithms

Module 2: Week 2

Lecture 2 Hadoop, Spark, and Flink

Introduction to Hadoop, Overview of Data streams, Spark Release 2.0, Introduction to Flink, Labs

Module 3: Week 3

Lecture 3 Machine Learning

Overview of Machine Learning libraries and frameworks, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Labs

Module 4: Week 4

Lecture 4 Deep Learning I

Introduction to Computer Vision and its applications, Convolutional Neural Networks, Frameworks, e.g., TensorFlow, GPU computing, TensorFlow Labs

Module 5: Week 5

Lecture 5 Deep Learning II

Overview of Natural Language Processing, A look at Time Series Data - analysis and datasets, Recurrent Neural Networks, TensorFlow Labs

Module 6: Week 6

Lecture 6 Deep Learning III

A closer look at LSTM, Deep Reinforcement Learning, Applications and Use Cases, TensorFlow Labs

Module 7: Week 7

Lecture 7 Deep Learning IV

Convolutional Neural Networks Introduction, A look at Gaussian processes, Bayesian inference, TensorFlow Lab

Module 8: Week 8

Lecture 8 IoT, Visualisation & Reporting

Overview of IoT with applications, Visualization Tools including d3 and Shiny, Business Reporting, Wrap up

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