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Data Analysis and Interpretation

About This Specialization Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world's biggest social challenges by joining one of their competitions, or help The Connection be…

Hands-on Text Mining and Analytics by Yonsei University

Hands-on Text Mining and Analytics

Yonsei University
Created by:   Yonsei University

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.

Min Song
Taught by:    Min Song, Professor
Library & Information Technology

EnglishSubtitles: Chinese (Simplified)
How To PassPass all graded assignments to complete the course.

Course Logistics and the Text Mining Tool for the Course

4 videos1 reading
  1. Video: 1.1 Description of the course including the objectives and outcomes
  2. Video: 1.2 Explanations of the y-TextMiner package and the datasets
  3. Video: 1.3 How-to-do: workspace installation and setup
  4. Video: 1.4 How-to-use: the y-TextMiner package (download it at
  5. Reading: What is Text Mining?
  6. Peer Review: y-TextMiner installation and a simple Java program
Text Preprocessing

5 videos1 reading
  1. Video: 2.1 Description of possible project ideas
  2. Video: 2.2 What is text mining?
  3. Video: 2.3 Description of preprocessing techniques
  4. Video: 2.4 How-to-do: normalization including tokenization and lemmatization
  5. Video: 2.5 How-to-do: N-Grams
  6. Reading: Text Preprocessing
  7. Peer Review: Preprocessing Practice
Text Analysis Techniques

6 videos2 readings
  1. Video: 3.1 Description of stopword removal, stemming, and POS tagging
  2. Video: 3.2 Explanations of named entity recognition
  3. Video: 3.3 Explanations of dependency parsing
  4. Video: 3.4 How-to-do: stopword removal and stemming
  5. Video: 3.5 How-to-do: NER and POS Tagging
  6. Video: 3.6 How-to-do: constituency and dependency parsing
  7. Reading: Stemming and Lemmatization
  8. Reading: Named Entity Recognition
Graded: Text Analysis Practice
Term Weighting and Document Classification

5 videos2 readings
  1. Video: 4.1 Explanations of TF*IDF
  2. Video: 4.2 Explanations of document classification
  3. Video: 4.3 Explanations of sentiment analysis
  4. Video: 4.4 How-to-do: computation of tf*idf weighting
  5. Video: 4.5 How-to-do: classification with Logistic Regression
  6. Reading: Text Classification
  7. Reading: TF-IDF
Graded: Document Classification Practice
Sentiment Analysis

6 videos1 reading
  1. Video: 5.1 Explanations of sentiment analysis with supervised learning
  2. Video: 5.2 Explanations of sentiment analysis with unsupervised learning
  3. Video: 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet
  4. Video: 5.4 How-to-do: sentiment analysis with CoreNLP
  5. Video: 5.5 How-to-do: sentiment analysis with LingPipe
  6. Video: 5.6 How-to-do: sentiment analysis with SentiWordNet
  7. Reading: Opinion mining and sentiment analysis by Bo Pang and Lillian Lee
Graded: Sentiment Analysis Practice
Topic Modeling

5 videos1 reading
  1. Video: 6.1 Description of Topic Modeling
  2. Video: 6.2 Explanations of LDA and DMR
  3. Video: 6.3 Description of Topic Modeling with Mallet
  4. Video: 6.4 How-to-do: LDA
  5. Video: 6.5 How-to-do: DMR
  6. Reading: Introduction to Probabilistic Topic Models by David Blei
Graded: Topic Modeling Practice

How It Works
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Yonsei University
Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia.
Ratings and Reviews
Rated 4.5 out of 5 of 14 ratings


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