CSC206 Text Analytics

Overview

This course introduces computational techniques for extracting information from unstructured text. This includes reading in different types of text, preparing text for further processing, summarizing and visualizing basic descriptive statistics, as well as applications, such as sentiment analysis, information retrieval, information extraction, summarization, and topic modeling.

Learning Objectives

Using Python 3, and introducing several libraries, students will learn how to acquire, manipulate, visualize and analyze textual information of different types, including prose, newspaper text and social media.

Required Text

There is no required text, however a useful companion text is the following. Students DO NOT need to purchase this text, as free access will be provided through the course.

Dipanjan Sarkar. Text Analytics with Python: A Practitioner’s Guide to Natural Language Processing. Second Edition. Apress. 2019.

Assignments & Grades

This grading for this class will consist of regular homework assignments, a final project, and a midterm exam. The midterm will take place in week 7.

The homework assignments will be practical programming assignments. The midterm exam will be taken by you on a date specified by me, at a time to suit you.

The final project will be chosen by you, and will demonstrate your text analysis skills, to produce a final report.

Policies

Late Assignments

There are NO late assignments allowed, without prior approval. If you do not hand in an assignment at the due date, that assignment will score zero.

ALL ASSIGNMENTS MUST RUN without error. Grading an assignment that contains an error will be at my discretion.

Academic Integrity / Honor Code

Union College recognizes the need to create an environment of mutual trust as part of its educational mission. Trust among students ensures that no student has an unfair advantage over another; trust between faculty and students ensures that the effort both parties put into preparation and evaluation of assigned work is not wasted, but can truly advance understanding and learning for students. Creation of this environment of trust is the responsibility of the entire academic community: faculty, staff and students. It requires that students submit work that is prepared in accordance with the course instructor’s requirements and that faculty foster an environment of academic honesty.

Toward this end, professors will uphold the high ethical standards of their discipline, provide to their students clear guidance on the policy and practice of academic integrity, and fairly evaluate students’ work.