Adjunct Associate Faculty, Applied Machine Learning I (On-Campus, Spring '2 at Columbia University

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Feb, 26

Salary

0.0

Posted On

11 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analytics, Applied Analytics, Machine Learning, Python Programming, Supervised Learning, Linear Regression, Decision Trees, Support Vector Machines, Data Wrangling, Predictive Analytics, Graduate-Level Teaching, Statistical Analysis, Student Evaluation, Instructional Support, Facilitation Skills

Industry

Higher Education

Description

Company Description Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries, and service to society. The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good. Job Description The School of Professional Studies seeks a data analytics professional to serve as a part-time Associate for a graduate-level course called Applied Analytics Frameworks & Methods I. The world is generating data at an even faster pace via business transactions, online searches, social media activities, and various sensors. The ready availability of vast amounts of data creates opportunities to predict outcomes and explain phenomena across a wide range of domains from medicine to business to even space exploration. Supervised learning techniques are being extensively used to make useful predictions and generate insights to tackle problems. These predictive analysis techniques focus on this course, guiding students through the data-wrangling process, starting with data exploration and other foundational approaches. The course then covers an array of supervised learning techniques, including linear regression, decision trees, and support vector machines. Students also have the opportunity to challenge themselves in applying and combining the techniques they have learned through a predictive analytics competition. An Associate is a faculty line junior to a Lecturer that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University. Responsibilities Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions. Evaluate, grade student work and assessments as requested by the course Lecturer. Monitor and address student concerns and inquiries. Qualifications Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting. Requirements Graduate degree in an area related to data science, applied analytics, statistics, or another quantitative discipline. Proficient in Python programming. 3+ years of professional experience in a role involving applied analytics. Preferred Skills & Experience Knowledge of theories and practical application of machine learning. University teaching experience. Additional Information Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity/Affirmative Action employer.

Responsibilities

Attend all class sessions, assist with instruction, and lead breakout sessions. Evaluate and grade student work and address student concerns as needed.