Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 18 Minute(s)
Language
English
Taught by
Loony Corn
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
3 views
Course Overview
Get your data to fly using Spark for analytics, machine learning and data science
Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.
Get your data to fly using Spark for analytics, machine learning and data science
Let’s parse that.
What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
What's Covered:
Lot's of cool stuff ..
- Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
- Dataframes and Spark SQL to work with Twitter data
- Using the PageRank algorithm with Google web graph dataset
- Using Spark Streaming for stream processing
- Working with graph data using the Marvel Social network dataset
.. and of course all the Spark basic and advanced features:
- Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate)
- Pair RDDs , reduceByKey, combineByKey
- Broadcast and Accumulator variables
- Spark for MapReduce
- The Java API for Spark
- Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)
Course Content
- 11 section(s)
- 54 lecture(s)
- Section 1 You, This Course and Us
- Section 2 Introduction to Spark
- Section 3 Resilient Distributed Datasets
- Section 4 Advanced RDDs: Pair Resilient Distributed Datasets
- Section 5 Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes
- Section 6 Java and Spark
- Section 7 PageRank: Ranking Search Results
- Section 8 Spark SQL
- Section 9 MLlib in Spark: Build a recommendations engine
- Section 10 Spark Streaming
- Section 11 Graph Libraries
What You’ll Learn
- Use Spark for a variety of analytics and Machine Learning tasks
- Implement complex algorithms like PageRank or Music Recommendations
- Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
- Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX
Reviews
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SSandeep Singh
just Awesome nothing else!!! execpt that sound in the end of every video is really high pitched.
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SSanjay Gupta
This course is excellent. Instructor is explaining in the best way, so that students can understand the concepts. But there is one place where improvement can be:- Instructor who is running the code is not presenting in the better way.
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SSreekanth Payyavula
The articulation needed to understand a complex topic is good. The tutor goes great lengths to explain the concepts and the examples have been taken which are interesting and relevant.
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AAnonymized User
Thank you Shwetha & team for the wonderful course. I must appreciate the detailing you added and elaborated at every single point. Thanks a ton and all the best to you all.
