Search Engines and Data Mining Lecture & Lab - Adjunct Faculty - Traditional Campus Fall

Job Description

The College of Engineering and Technology employs faculty and faculty leaders who are passionate about engaging and mentoring students to prepare them for the continuously changing worlds of IT and Engineering.

• Tuition benefits specifically for the adjunct employee
• 401(k)
• Employee Perks and Discounts
• Gym and Fitness Center
• Canyon Health & Wellness Center

This course provides a comprehensive introduction to neural networks and deep learning. The location, retrieval, and conversion of raw data into usable information is accomplished by implementing a variety of neural network models. Students implement deep learning algorithms for organizing and searching very large data collections, like those typically found in enterprise databases and on websites. Students use clustering and categorization to generate various information taxonomies based on document ranking, evaluation, and classification.

The laboratory reinforces and expands deep learning principles introduced in the lecture. Hands‑on activities focus on using neural networks for performing data mining on a large business database and extracting trends and actionable information.

• Facilitate classroom lecture and discussions
• Engage students in learning course objectives and topics
• Assess student performance and mentor success in the classroom
• Provide a positive example to students by supporting the University’s Doctrinal Statement, Ethical Position Statement, and Mission of Grand Canyon University

• Preferred:
  Master’s in Computer Science or Statistics or closely related degree AND experience in Big Data Analytics or Data Science with proficiency in Python.
• Minimum:
  Bachelor in Statistics or closely related degree AND 5 or more years of experience in Big Data Analytics or Data Science with proficiency in Python.
• All candidates must be proficient in Python programming, with an emphasis on pandas, numpy, matplotlib, scikit, and tensor flow.

• Your unofficial transcript reflecting degree earned with 18 graduate credit hours in the areas listed above
• Your unofficial transcripts for any applicable conferred graduate degrees