Take your knowledge to the next level with Cloudera’s Data Scientist Training
The workshop is designed for data scientists who use Python or R to work with small datasets on a single machine and who need to scale up their data science and machine learning workflows to large datasets on distributed clusters. Data engineers, data analysts, developers, and solution architects who collaborate with data scientists will also find this workshop valuable. Workshop participants walk through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and lively discussions. The demonstrations and exercises are conducted in Python (with PySpark) using Cloudera Data Science Workbench (CDSW). Supplemental examples using R (with sparklyr) are provided.
Through narrated lecture, recorded demonstrations, and hands-on exercises,you will learn how to:
- How to use Apache Spark to run data science and machine learning workflows at scale
- How to use Spark SQL and DataFrames to work with structured data
- How to use MLlib, Spark’s machine learning library
- How to use PySpark, Spark’s Python API
- How to use sparklyr, a dplyr-compatible R interface to Spark
- How to use Cloudera Data Science Workbench (CDSW)
- How to use other Cloudera platform components including HDFS, Hive,
- Impala, and Hue
Audience & Prerequisites
Workshop participants should have a basic understanding of Python or R and some experience exploring and analyzing data and developing statistical or machine learning models. Knowledge of Hadoop or Spark is not required.