Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 7 Minute(s)
Language
English
Taught by
Packt Publishing
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Effectively apply Advanced Analytics to large datasets using the power of PySpark
PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. You'll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you'll delve into various Spark components and its architecture.
You'll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You'll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning.
By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization.
About the Author
Danny Meijer works as the Lead Data Engineer in the Netherlands for the Data and Analytics department of a leading sporting goods retailer. He is a Business Process Expert, big data scientist and additionally a data engineer, which gives him a unique mix of skills—the foremost of which is his business-first approach to data science and data engineering.
He has over 13-years' IT experience across various domains and skills ranging from (big) data modeling, architecture, design, and development as well as project and process management; he also has extensive experience with process mining, data engineering on big data, and process improvement.
As a certified data scientist and big data professional, he knows his way around data and analytics, and is proficient in various types of programming language. He has extensive experience with various big data technologies and is fluent in everything: NoSQL, Hadoop, Python, and of course Spark.
Danny is a driven person, motivated by everything data and big-data. He loves math and machine learning and tackling difficult problems.
Course Content
- 9 section(s)
- 41 lecture(s)
- Section 1 Python and Spark: A Match Made in Heaven
- Section 2 Working with PySpark
- Section 3 Preparing Data Using Spark SQL
- Section 4 Machine Learning with Spark MLlib
- Section 5 Classification and Regression
- Section 6 Analyzing Big Data
- Section 7 Processing Natural Language in Spark
- Section 8 Machine Learning in Real-Time
- Section 9 The Power of PySpark
What You’ll Learn
- Gain a solid knowledge of vital Data Analytics concepts via practical use cases
- Create elegant data visualizations using Jupyter
- Run, process, and analyze large chunks of datasets using PySpark
- Utilize Spark SQL to easily load big data into DataFrames
- Create fast and scalable Machine Learning applications using MLlib with Spark
- Perform exploratory Data Analysis in a scalable way
- Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming
Skills covered in this course
Reviews
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NNivetta T
nice conceptual explanation .
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AAravindmohandas
very fast
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FFredrik Lundberg
I would have liked a bit more on the Spark ML library and how the different methods there relates to scalable data analytics
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PPanthea Azadeh
It was a very informative course. As a follow up or addition it will be very helpful to go through a project A-Z to see all the tricks in action. I specifically would like to get a more hands on practice on running spark in production and scales. Thanks for sharing your expertise with us.
