Hello everyone,

The *Quantitative Methods in the Social Sciences* (QMSS
<http://qmss.columbia.edu/>) MA Program at Columbia University still has a
handful of adjunct professor positions available for courses for Fall
2021.  For reference, Columbia's goal is to return to normal university
life in September, which would mean that -- as of now -- instructors would
be expected to teach in our classrooms on campus this Fall.

I would greatly appreciate it if you could forward this on to your
colleagues and recent graduates who might be interested in this
opportunity.  Positive performance can lead to further teaching
opportunities.  Ph.D.s from any discipline are welcome.  Thanks!  - Greg

Our courses in need of instructors are:

* *Theory and Methodology for the Social Sciences:* This course focuses on
foundational ideas of social science research, including strengths and
weaknesses of different research designs, interpretation of data drawn from
various sources, and strategies for evaluating evidence.  Students write
memos, make presentations and write a final research proposal.

* *Practicum on Large-Scale Data Analysis and Processing:*  This course is
a “hands-on” training seminar dealing with organizing large, data-intense
projects. Students will practice accessing a variety of large-scale data
sources and formats; conducting advanced analysis of big data; and
“productizing” their results and findings via dashboards, reports,
interactive graphics, and apps. An instructor with data science experience
in industry would be particularly valuable for this course.

* *Introduction to Data Science Algorithms:* This course introduces the
logic, basic design and analysis of algorithms relevant to data science,
machine learning and data analysis.  Topics include algorithms for:
sorting, searching, graph processing, string processing, SVD and PCA,
hashing, count-min sketch, nearest neighbors, network flows, and data
compression.

* *Advanced Machine Learning*:  Building from knowledge in Python, the
scikit-learn package and GitHub, this course covers supervised learning
methods, including OLS models, linear models for classification, support
vector machines, decision trees and random forests, and gradient boosting.
Unsupervised techniques are covered too: PCA, discriminant analysis,
manifold learning, clustering, mixture models, cluster evaluation.
Particular emphasis will be placed on advanced techniques in neural
networks, convolutional neural networks for image classification,
adversarial neural networks, and recurrent neural networks.

*Qualifications:*  A Ph.D. in a social science or a related field (broadly
defined) is required.  Review of applications will begin immediately and
continue until the positions are filled.  A CV, cover letter, list of
professional references and teaching evaluations (if available) should be
sent as PDF files to [log in to unmask] with the subject line “Fall 2021
Adjunct Instructor Application.”  Let us know which courses you might be
interested in teaching.  An optional writing sample and statement of
teaching philosophy may also be included.

*About QMSS*
QMSS <http://qmss.columbia.edu/> of Columbia University is one of the
oldest interdisciplinary applied statistics MA programs of its kind, with
more than 20 years of training graduate students.  We count hundreds of
data scientists, senior analysts and academic researchers among our
alumni.  We are constantly striving to develop new ways to push the
boundaries of where social science and data science meet at Columbia.
Columbia University is an Equal Opportunity/Affirmative Action employer.

-- 
Gregory M. Eirich, Director
Quantitative Methods in the Social Sciences
Institute for Social and Economic Research and Policy (ISERP)
Columbia University
420 West 118th Street (International Affairs Building) -- 8th Floor, MC 3355
New York, NY 10027
+1 212 854 8682
[log in to unmask]

*http://qmss.columbia.edu/faculty-and-staff/
<http://qmss.columbia.edu/faculty-and-staff/>*

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