Rigorous On-Site Bootcamp in the Evenings

Offered in New York, Boston and Washington DC

USD 6,000.00
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, Instructor - Data Science for Deep Learning Evenings

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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.

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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. 

Curriculum

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