Updated a month ago
Scalable data science is a technical course in the area of Big Data, aimed at the needs of the data industry. This course uses Apache Spark, a fast and general engine for large-scale data processing via databricks to compute with datasets that won't fit in a single computer. The course will introduce Spark’s core concepts via hands-on coding, including resilient distributed datasets and map-reduce algorithms, DataFrame and Spark SQL on Catalyst, scalable machine-learning pipelines in MlLib and vertex programs using the distributed graph processing framework of GraphX. We will solve instances of real-world big data decision problems from various scientific domains.
This is being prepared by Raazesh Sainudiin and Sivanand Sivaram with assistance from Paul Brouwers, Dillon George and Ivan Sadikov.
All course projects by seven enrolled and four observing students for Semester 1 of 2016 at UC, Ilam are part of this content.
The 2016 instance of this scalable-data-science course finished on June 30 2016.
To learn Apache Spark for free try databricks Community edition by starting from https://databricks.com/try-databricks.
All course content can be uploaded for self-paced learning by copying the following URL for 2016/Spark1_6_to_1_3/scalable-data-science.dbc archive and importing it from the URL to your free Databricks Community Edition.
The Gitbook version of this content is https://www.gitbook.com/book/raazesh-sainudiin/scalable-data-science/details.
The browsable git-pages version of the content is http://raazesh-sainudiin.github.io/scalable-data-science/.
Scalable Data Science, Raazesh Sainudiin and Sivanand Sivaram, Published by GitBook https://www.gitbook.com/book/raazesh-sainudiin/scalable-data-science/details, 787 pages, 30th June 2016.
Databricks Academic Partners Program and Amazon Web Services Educate.
Week 3: Introduction to Spark SQL, ETL and EDA of Diamonds, Power Plant and Wiki CLick Streams Data
Week 4: Introduction to Machine Learning - Unsupervised Clustering and Supervised Classification
Week 5: Introduction to Non-distributed and Distributed Linear Algebra and Applied Linear Regression
Week 8: Graph Querying in GraphFrames and Distributed Vertex Programming in GraphX
Week 9: Deep Learning, Convolutional Neural Nets, Sparkling Water and Tensor Flow
Week 11 and 12: Student Projects
All course content is currently being pushed by Raazesh Sainudiin after it has been tested in Databricks cloud (mostly under Spark 1.6 and some involving Magellan under Spark 1.5.1).
The markdown version for gitbook
is generated from the Databricks .scala
, .py
and other source codes.
The gitbook is not a substitute for the Databricks notebooks available in the Databricks cloud. The following issues need to be resolved:
display_HTML
and frameIt
with their in-place embeds of web content.Please feel free to fork the github repository:
Furthermore, due to the anticipation of Spark 2.0 this mostly Spark 1.6 version could be enhanced with a 2.0 version-specific upgrade.
Please send any typos or suggestions to [email protected]
Please read a note on babel to understand how the gitbook is generated from the .scala
source of the databricks notebook.
Raazesh Sainudiin, Laboratory for Mathematical Statistical Experiments, Christchurch Centre and School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch 8041, Aotearoa New Zealand
Sun Jun 19 21:59:19 NZST 2016