Essential Data Science for Business: Data Science Workflows

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The tutorials in this NISS series involve the Top 10 analytics approaches of the key topics that are used in business today!  Students and faculty, these are perhaps the top ten most important and practical topics that may not be covered in your program of study.  (Review the Overview Presentation about all 10 Sessions).

Data Science Workflows

Overview

This tutorial focuses on data science workflows (or pipelines) for building machine learning models on structured data using R and Spark. R is a flexible, extensible statistical computing environment, but it is limited to single-core execution. Spark is a distributed computing environment that treats R as a first-class programming language. The first part of the tutorial introduces data science tools supporting reproducible reports, including Linux, bash, Git, Docker, and make. R is then presented as a functional programming language using R markdown documents.

The course covers the initial steps in the data science process using R and Spark: extracting data from source systems; transforming data into a tidy form; and loading data into a relational database (PostgreSQL), a distributed file system (HDFS), a distributed data warehouse (Hive), and into Spark SQL, i.e., ETL.

These R-based workflows are illustrated by using `dplyr` directly and as a frontend to SQL databases. The `sparklyr` package with its `dplyr` interface to Spark is then used for modeling big data using regression and classification supervised learning methods. Unsupervised learning methods, such as clustering and dimension reduction, are also covered. Finally, methods for analyzing streaming data are presented.

Materials

The course content is available as R markdown and PDF files on GitHub here: https://github.com/jharner/rspark-tutorial  
This content is embedded within an Amazon Machine Image (AMI), which provides a complete R and Spark computing environment. Students are encouraged to sign up for an AWS account to run the R markdown files interactively. Follow the instructions in the README file to create an account and launch an AWS instance here: https://github.com/jharner/rspark/tree/master/AWS  

The content is also available as a Dockerized application called `rspark` here:  https://github.com/jharner/rspark-docker  
Follow the instructions in the README file to run `rspark` on supported laptops.

Instructor

E. James Harner (Professor Emeritus and Former Chair in Statistics, West Virginia University)

Goals

NISS is interested in sharing knowledge.  To this end, these webinars have been geared to provide practical information that you can use tomorrow. Examples, projects and code sharing are a part of these sessions wherever possible.

Series Prerequisites

Participants require a working knowledge of probability distributions, statistical inference, statistical modeling and time series analysis as a prerequisite. Students who do not have this foundation or have not reviewed this material within the past couple of years will struggle with the concepts and methods that build on this foundation.

Registration 

Select a registration/payment option above the 'Register for this Event' button ($35 for each Data Science Essentials tutorial session, $250 for all 10 Essential Data Science for Business tutorial sessions.  Missed this Event? To gain access to the recording of this event along with links to supporting files and information, complete the Registration Option for "Post Session Access" on the right hand side of this webpage ). NISS affiliates, (https://www.niss.org/affiliates-list), please send an email to officeadmin@niss.org.).