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Data Science and Engineering with Spark

Offered By: Berkeley University of California via edX

Tags

Big Data Courses Data Science Courses Supervised Learning Courses PySpark Courses Feature Extraction Courses Data Engineering Courses Exploratory Data Analysis Courses Model Evaluation Courses Distributed Machine Learning Courses

Course Description

Overview

The Data Science and Engineering with Spark XSeries, created in partnership with Databricks, will teach students how to perform data science and data engineering at scale using Spark, a cluster computing system well-suited for large-scale machine learning tasks. It will also present an integrated view of data processing by highlighting the various components of data analysis pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. Students will gain hands-on experience building and debugging Spark applications. Internal details of Spark and distributed machine learning algorithms will be covered, which will provide students with intuition about working with big data and developing code for a distributed environment.

This XSeries requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), but previous experience with Spark or distributed computing is NOT required. Familiarity with basic machine learning concepts and exposure to algorithms, probability, linear algebra and calculus are prerequisites for two of the courses in this series.


Syllabus

Courses under this program:
Course 1: Big Data Analysis with Apache Spark
Learn how to apply data science techniques using parallel programming in Apache Spark to explore big data.

Course 2: Distributed Machine Learning with Apache Spark
Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark.

Course 3: Introduction to Apache Spark
Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals.


Courses

  • 9 reviews

    2 weeks, 5-10 hours a week, 5-10 hours a week

    View details

    Spark is rapidly becoming the compute engine of choice for big data. Spark programs are more concise and often run 10-100 times faster than Hadoop MapReduce jobs. As companies realize this, Spark developers are becoming increasingly valued.

    This statistics and data analysis course will teach you the basics of working with Spark and will provide you with the necessary foundation for diving deeper into Spark. You’ll learn about Spark’s architecture and programming model, including commonly used APIs. After completing this course, you’ll be able to write and debug basic Spark applications. This course will also explain how to use Spark’s web user interface (UI), how to recognize common coding errors, and how to proactively prevent errors. The focus of this course will be Spark Core and Spark SQL.

    This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), but previous experience with Spark or distributed computing is NOT required. Students should take this Python mini-quiz before the course and take this Python mini-course if they need to learn Python or refresh their Python knowledge.


Taught by

Jon Bates, Ameet Talwalkar and Anthony D. Joseph

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