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Data Science I

Overview

This course provides instruction on the theory and practice of data science, including machine learning and natural language processing. This course introduces many of the core concepts behind today’s most commonly used algorithms and introducing them in practical applications. We’ll discuss concepts and key algorithms in all of the major areas – Classification, Regression, Clustering, Dimensionality Reduction, including a primer on Neural Networks. We’ll focus on both single-server tools and frameworks (Python, NumPy, pandas, SciPy, Scikit-learn, NLTK, TensorFlow Jupyter) as well as large-scale tools and frameworks (Spark MLlib, Stanford CoreNLP, TensorFlowOnSpark/Horovod/MLeap, Apache Zeppelin). Download the data sheet to view the full list of objectives and labs.

Prerequisites

Students must have experience with Python and Scala, Spark, and prior exposure to statistics, probability, and a basic understanding of big data and Hadoop principles. While brief reviews are offered in these topics, students new to Hadoop are encouraged to attend the Apache Hadoop Essentials (HDP-123) course and Apache Spark 2.3 (DEV-343), as well as the language-specific introduction courses.


Target Audience


Architects, software developers, analysts and data scientists who need to apply data science and machine learning on Spark/Hadoop
.

1
Day

An Introduction to Data Science, SciKit-Learn, HDFS, Reviewing Spark apps, DataFrames and NOSQL

Objectives

  • Discuss aspects of Data Science, the team members, and the team roles
  • Discuss use cases for Data Science
  • Discuss the current State of the Art and its future direction
  • Review HDFS, Spark, Jupyter, and Zeppelin
  • Work with SciKit-Learn, Pandas, NumPy, Matplotlib, and Seaborn

Labs

  • Hello, ML w/ SciKit-Learn
  • Spark REPLs, Spark Submit, & Zeppelin Review
  • HDFS Review
  • Spark DataFrames and Files
  • NiFi Review

Algorithms in Spark ML and SciKit-Learn: Linear Regression, Logistic Regression, Support Vectors, Decision Trees

K-Means & GMM Clustering, Essential TensorFlow, NLP with NLTK, NLP with Stanford CoreNLP

HyperParameter Tuning, K-Fold Validation, Ensemble Methods, ML Pipelines in SparkML

Live Training

Live Training Self Paced Blended
LIVE CLASS
DATE & TIME
LOCATION
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