Hortonworks

Choose training

of a superior quality and value

HDP Analyst: Data Science

Name
Language
Days
Place
Availability
Price excl. VAT *
Terms
Name
HDP Analyst: Data Science
Language
en
Days
3
Place
MHM computer a.s., Prague
Availability
All
Price excl. VAT *
€1,800.00
Terms
* Companies not registered for VAT and private persons will be invoiced including VAT of 21%. Please provide us with your company VAT number to receive the invoice excluding VAT.

Course information

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.

Target Audience

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

Prerequisites

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.

Format

50% Lecture/Discussion

50% Hands on Labs

Objectives:

  • 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


Lab Content:

  • 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

For more information, download the data sheet.

Request training

Course:

HDP Analyst: Data Science

* Required