Data Science for the Hortonworks Data Platform

  • Duration: 3 days
  • Our price: from £1,734 per person

Course 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.

Pre-Requisites:

Delegates 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.
Delegates new to Hadoop are encouraged to attend the HDP Overview: Apache Hadoop Essentials course.

Who Should Attend:

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

Training Course Overview/Content:

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
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

This course is running at the following venues:

3rd Floor, International House, London, E1W 1UN

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