Data Science Primer | Technologies, Tools & Modern Roles in the Data-Driven Enterprise (TTDS6000)

At Course Completion

This course provides a high-level view of a variety of core, current data science related technologies, strategies, skillsets, initiatives and supporting tools in common business enterprise practices. This list covers a general range of topics current to the time of course distribution.

Students will explore:

· The Hadoop Ecosystem: HDFS; Resource Navigators, MapReduce, Spark, Distributions

· Big Data, NOSQL, and ETL

· ETL: Exchange, Transform, Load

· Handling Data & a Survey of Useful tools

· Enterprise Integration Patterns and Message Busses

· Developing in Hadoop Ecosystem: R, Python, Java, Scala, Pig, and BPMN

· Artificial Intelligence and Business Systems

· Who’s on the Team? Evolving Roles and Functions in Data Science

· Growing your Infrastructure

Audience Profile

This introductory-level / primer course is an overview for Business Analysts, Data Analysts, Data Architects, DBAs, Network (Grid) Administrators, Developers or anyone else in the data science realm who need to have a baseline understanding of some of the core areas of modern Data Science technologies, practices and available tools.

Outline

1. Exploring the Hadoop Ecosystem

· HDFS: Hadoop Distributed File System

· Resource Negotiators: YARN, Mesos, and Spark; ZooKeeper

· Hadoop Map/Reduce

· Spark

· Hadoop Ecosystem Distributions: Cloudera, Hortonworks, OpenSource

2. Artificial Intelligence and Business Systems

· Artificial Intelligence: Myths, Legends, and Reality

· The Math

· Statistics

· Probability

· Clustering Algorithms, Mahout, MLLib, SciKit, and Madlib

· Business Rule Systems: Drools, JRules, Pegasus

3. The Modern Data Team

· Agile Data Science

· NOSQL Data Architects and Administrators

· Developers

· Grid Administrators

· Business and Data Analysts

· Management

· Evolving your Team

· Growing your Infrastructure

4. Supervised Learning with Big Data

· Demo

· Exploratory Data Analysis (Demo – Credit Card Risk Model)

· Wrangling and Cleaning Data

· Building a Supervised Machine Learning Model with MyBinder and JupyterLab

5. Deep Learning with Big Data

· Exploratory Data Analysis

· Data Cleaning and Wrangling

· Build a Deep Learning Model

· Demo with TensorFlow