HDP Academic Analyst: Data Science
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This course is designed for students preparing to become familiar with 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.
Hortonworks offers a comprehensive certification program that identifies you as an expert in Apache Hadoop.
Upon completion of this program, participants should be able to:
- 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