HDP Analyst: Data Science Training

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 3 Days
  • Download PDF
  • We can host this training at your preferred location. Contact us!

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, Scikit-learn), the Natural Language Toolkit (NLTK), and Spark MLlib.

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

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

  • At the completion of the course students will be able to:Recognize use cases for data scienceDescribe the architecture of Hadoop and YARN
  • Recognize use cases for data science
  • Describe the architecture of Hadoop and YARN
  • Describe supervised and unsupervised learning differences
  • List the six machine learning tasks
  • 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
  • Use NumPy to analyze big data
  • Use the data structure classes in the pandas library
  • Write a Python script that invokes SciPy machine learning
  • 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
  • Write a Python script that invokes scikit-learn
  • Use the k-nearest neighbor algorithm to predict values
  • Run a machine learning algorithm on a distributed data set
  • Describe use cases for Natural Language Processing (NLP)
  • Perform sentence segmentation on a large body of text
  • Perform part-of-speech tagging
  • 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

  • Setting Up a Development Environment
  • Using HDFS Commands
  • Using Mahout for Machine Learning
  • Getting Started with Pig
  • Exploring Data with Pig
  • Using the IPython Notebook
  • Data Analysis with Python
  • Interpolating Data Points
  • Define a Pig UDF in Python
  • Streaming Python with Pig
  • K-Nearest Neighbor and K-Means Clustering
  • Using NLTK for Natural Language Processing
  • Classifying Text using Naive Bayes
  • Spark Programming and Spark MLlib


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