Hortonworks HDP Analyst Data Science

Summary

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.

Duration

3 Days/Lecture & Lab

Audience

This course is designed for architects, software developers, analysts, and data scientists who need to apply data science and machine learning on Hadoop.

Topics

• Setting Up a Development Environment
• Block Storage
• Using HDFS Commands
• MapReduce
• Using Apache Mahout for Machine Learning
• Apache Pig
• Getting Started with Apache Pig
• Exploring Data with Pig
• Using the IPython Notebook
• The NumPy Package
• The pandas Library
• Data Analysis with Python
• Interpolating Data Points
• Defining a Pig UDF in Python
• Streaming Python with Pig
• Classification with Scikit-Learn
• Computing K-Nearest Neighbor
• Generating a K-Means Clustering
• POS Tagging Using a Decision Tree
• Using NLTK for Natural Language Processing
• Classifying Text using Naive Bayes
• Using Spark Transformations and Actions
• Using Spark MLlib
• Creating a Spam Classifier with MLlib

Prerequisites

Before taking this course, 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.