Book Image

Mastering Python for Data Science

By : Samir Madhavan
Book Image

Mastering Python for Data Science

By: Samir Madhavan

Overview of this book

Table of Contents (19 chapters)
Mastering Python for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
7
Estimating the Likelihood of Events
Index

Summary


In this chapter, you were introduced to big data, learned about how the Hadoop software works, and the architecture associated with it. You then learned how to create a mapper and a reducer for a MapReduce program, how to test it locally, and then put it into Hadoop and deploy it. You were then introduced to the Hadoopy library and using this library, you were able to put files into Hadoop. You also learned about Pig and how to create a user-defined function with it. Finally, you learned about Apache Spark, which is an alternative to MapReduce and how to use it to perform distributed computing.

With this chapter, we have come to an end in our journey, and you should be in a state to perform data science tasks with Python. From here on, you can participate in Kaggle Competitions at https://www.kaggle.com/ to improve your data science skills with real-world problems. This will fine-tune your skills and help understand how to solve analytical problems.

Also, you can sign up for the Andrew...