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External event: Text Analytics in Python (Advanced)

  • 8 Sep 2020
  • 10:00 AM (AEST)
  • 9 Sep 2020
  • 1:00 PM (AEST)
  • Online

Text Analytics in Python (Advanced)

Course Overview

More than 70% of the data on the internet is unstructured. Among them, text is the most common form that appears in almost all data sources. For example, text data such as emails, online reviews, tweets, news and reports hold valuable information and insight for most research and applications. Text analytics, usually involving techniques from text mining or natural language processing (NLP), can automatically uncover patterns and extract meaning/context from these unstructured texts. 

This course assumes that you have basic Python programming knowledge, or have previously attended "Introduction to Python for Data Science" from Stats Central. This course will provide you the foundation to process and analyze text. 

In this course, we will cover some useful Python features and libraries for text processing and analysis. We will touch on some advanced topics such as sentiment analysis, text classification, and/or topic extraction. 

Presenter: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

Course Requirements: You will need a computer.

Date:               Tuesday 8 & Wednesday 9 September, 2020 (two half-day sessions)

Duration:         10.00 am to 1.00 pm – Each day

Location:         Online

For further details and bookings:

Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website

**Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

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