Data Analytics and Methodologies

Course Description

Data analytics is transforming business processes at organizations large and small. As siloed data becomes more available through the deployments of environments such as data lakes and company-wide data warehousing, opportunities arise to apply analytics to improve efficiency, identify opportunity, and use predictions to take pre-emptive action. This course will introduce participants to the fundamentals of data analytics, big data frameworks, open source analytics tools, design methodologies, and visualization libraries through hands-on case studies.

Course ID: DEF 4616P
Course Format: Classroom

Available Classroom Sections

Start Date End Date Registration Deadline Format Location Cost CRN
Section Details Sep 11, 2018 Sep 13, 2018 Tuesday, September 11, 2018 - 23:59 Classroom Atlanta, Georgia $1,595 18315
View previous sections
View Previous Sections
CRN Start date End date Format Location Cost
16715 Oct 11, 2016 Oct 13, 2016 Classroom Atlanta, Georgia $1,595
17584 May 9, 2017 May 11, 2017 Classroom Atlanta, Georgia $1,595

Who Should Attend

Data scientists looking to expand their knowledge of data analytics techniques and tools. Also those working in social media, marketing, retail, and others looking to incorporate technology into their workplace.

How You Will Benefit

Provide the audience with an overview of industry-leading tools and methodologies for data analytics. Students will be able to:

  • Explain steps to clean and normalize data during fusion
  • Define the analytics pipeline of ETL, analyses, visualization, and reporting
  • Access data from traditional databases, document stores, Hadoop, and open APIs including open source data from social media (Twitter, Instagram, Facebook)
  • Map analytics application requirements to big data frameworks and tools
  • Utilize cloud-based tools, such as AzureML or Amazon Machine Learning Services, for real-time predictive modeling
  • Understand the limitations of data analytics algorithms
  • Integrate science and mathematics into business processes

Content

  • Introduction
  • Case Study
  • Sources of Data
  • Statistics
  • Machine Learning
  • Data Visualization
  • Natural Language Processing
  • Graph Analytics
  • Big Data Platforms
  • Design Principles
  • End-to-end Example

For Course-Related Questions

Please contact the course administrator: Renita Folds

Instructors