Data Science Bootcamp: Pay only after you get a data science job.
> Unlimited 1:1 mentor support > Hands-on experience > Job guarantee
http://bit.ly/2L4aBJC
Big Data Analysis with Scala and Spark
> Getting Started + Spark Basics > Reduction Operations & Distributed Key-Value Pairs > Partitioning and Shuffling > Structured data: SQL, Dataframes, and Datasets
http://bit.ly/2qGqfz9
Mathematics for Machine Learning
> Linear Algebra > Multivariate Calculus > Principal Component Analysis(PCA)
http://bit.ly/2JQGhSF
Python Data Products for Predictive Analytics
> Basic Data Processing and Visualization > Design Thinking and Predictive Analytics for Data Products > Meaningful Predictive Modeling > Deploying Machine Learning Models
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http://bit.ly/2XT1fn1
Artificial Intelligence Projects with Python-HandsOn
> Use popular libraries such as Keras and TensorFlow for reinforcement learning > Employ the SpaCy and textacy libraries for natural language processing > Extend pre-trained deep learning models
http://bit.ly/2qmbOQG
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Data Science Career Track Prep Course
Learn the foundational coding and statistics skills needed to pass our Data Science Career Track admissions challenge.
> Use Python to complete real-world coding exercises and begin your data science journey > Confidently tackle our Data Science Career Track admissions challenge
http://bit.ly/2Xrb4fk
SQL for Data Science
> Getting Started and Selecting & Retrieving Data with Structured Query language (SQL) > Filtering, Sorting, and Calculating Data with SQL > Subqueries and Joins in SQL > Modifying and Analyzing Data with SQL
http://bit.ly/2LbtI4n
Data Science Career Track Prep Course
Learn the foundational coding and statistics skills needed to pass our Data Science Career Track admissions challenge.
> Use Python to complete real-world coding exercises and begin your data science journey > Confidently tackle our Data Science Career Track admissions challenge
http://bit.ly/2Xrb4fk
Learning Python Artificial Intelligence by Example
> Understand data-mining methods, and how to work with multiple data sets when building a model > Apply open data and deep learning to predict taxi journey times in New York City > Use convolutional neural networks to determine an appropriate steering angle for a self-driving car
http://bit.ly/2SmSHBN