HDP Analyst Data Science

Exclusive Content Included With This Course:​
How to Load Ambari from Scratch
Exclusive Content Included With This Course:​
Configuring Local Repositories
Exclusive Content Included With This Course:​
HDPCD - Big Data Certified Developer Exam Prep
Exclusive Content Included With This Course:​
HDPCA - Big Data Certified Administrator Exam Prep
Exclusive Content Included With This Course:​
Free Open Source Components to Solve Big/”ANY” Data Problems
Exclusive Content Included With This Course:​
Deep Dive: Kafka

Overview

This course Provides instruction on the processes and practice of data science, including machine learning and natural language processing. Included are: tools and programming languages (Python, IPython, Mahout, Pig, NumPy, pandas, SciPy, Scikitlearn), the Natural Language Toolkit (NLTK), and Spark MLlib.
 

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

Students must have experience with at least one programming or scripting language, knowledge in statistics and/or mathematics,
and a basic understanding of big data and Hadoop principles. Students new to Hadoop are encouraged to attend the HDP Overview: Apache Hadoop Essentials course.

Recognize use cases for data science on Hadoop

  • Describe the Hadoop and YARN architecture
  • Describe supervised and unsupervised learning differences
  • Use Mahout to run a machine learning algorithm on Hadoop
  • Describe the data science life cycle
  • Use Pig to transform and prepare data on Hadoop
  • Write a Python script
  • Describe options for running Python code on a Hadoop cluster
  • Write a Pig User-Defined Function in Python
  • Use Pig streaming on Hadoop with a Python script
  • Use machine learning algorithms
  • Describe use cases for Natural Language Processing (NLP)
  • Use the Natural Language Toolkit (NLTK)
  • Describe the components of a Spark application
  • Write a Spark application in Python
  • Run machine learning algorithms using Spark MLlib
  • Take data science into production

Day 1: An Introduction to Data Science, Python, Hadoop and Machine Learning


OBJECTIVES

  • Define Data Science and Explain What a Data Scientist Does
  • Differentiate Between Different Types of Data Roles
  • List a Number of Data Science Use Cases
  • Present an Overview of Python
  • Describe the Components of the Big Data Scientific Stack

LABS

  • Using IPython
  • Data Analysis with Python
  • Using HDFS Commands
  • Introduction to Spark REPLs and Zeppelin
  • Using Apache Mahout for Machine Learning


Day 2: Working with Spark RDDs, DataFrames and SparkSQL, Visualization in Zeppelin


OBJECTIVES

  • Explain What an RDD Is
  • Explain How RDDs are Partitioned
  • Create Manipulate and Restore RDDs
  • Use Spark SQL to Create Tables
  • Create an Application and Submit to the Cluster

LABS

  • Create and Manipulate RDDs
  • Create and Save DataFrames
  • Build and Submit Spark Applications


Day 3: Machine Learning Algorithms, Natural Language Processing, and Spark MLlib


OBJECTIVES

  • Describe Common Machine Learning Applications
  • List the Pros and Cons of Various Algorithms
  • Explain what Natural Language Processing is
  • Explain the Feature Engineering Capabilities of Spark MLlib

LABS

  • Use the Python Natural Language Toolkit (NLTK)
  • Classify text using Naïve Bayes
  • Compute K-nearest neighbors
  • Creating a Spam Classifier with MLlib
  • Sentiment Analysis with Spark MLlib

A year-long neXT membership which includes 365 days of…

  • Access to exclusive webinars and neXTpertise mentoring sessions covering content in and out of class
  • Discussion boards to interact with instructors and other members
  • Customized learning paths built to get you to your end goal 
    • Learning paths include vidoes, blogs, quizzes, exam prep, and more

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