Data Science Eğitimi

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Talep Formu

Data Science  

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, 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.  

Course 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 

Hands-On Labs

- 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  

- Spam Classification with MLlib 


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.    
   

Certification

Hortonworks offers a comprehensive certification program that identifies you as an expert in Apache Hadoop. Visit hortonworks.com/training/certification for more information. 

 



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Diğer Amazon Web Services (AWS), Big Data, Hortonworks Eğitimleri