taught by Anurag Bhardwaj
In this online course, “Text Mining,” you will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined. After completing this course students will be able to:
• Perform tokenization and create dictionaries to prepare text for classification tasks
• Create numerical vectors from text data
• Build classifiers with decision trees, Naive Bayes and linear models, using training and validation data
• Perform "tagging" of text data
• Cluster documents using the k-means algorithm
• Generate predicted Twitter hashtags for text data
WEEK 1: Introduction and Data Preparation
- Overview of text mining
- Dictionary creation
- Vector generation for prediction
- Feature generation and selection
WEEK 2: Predictive Models for Text
- Document classification
- Document similarity and nearest-neighbor
- Decision rules
- Probabilistic models
- Linear models
- Performance evaluation
WEEK 3: Retrieval and Clustering of Documents
- Measuring similarity for retrieval
- Web-based document search and link analysis
- Document matching
- Clustering by similarity
- k-means clustering
- Hierarchical clustering
- The EM algorithm for clustering
- Evaluation of clustering
WEEK 4: Information Extraction
- Goals of information extraction
- Finding patterns and entities
- Entity Extraction: The Maximum Entropy method
- Extraction from web sources
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
In addition to assigned readings, this course also has a get started guide, and supplemental readings available online.
IT professionals, web marketing analysts, data mining and statistical consultants. In general: analysts and researchers who need to pilot, implement or analyze data mining methods aimed at data containing unstructured text (forms, surveys, etc.).
This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
About 15 hours per week, at times of your choosing.
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
- No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
- Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
- CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). For those successfully completing the course, CEU's and a record of course completion will be issued by The Institute, upon request.
- Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Text Mining and Analytics Specialization which gives a deep dive into text mining, natural language processing and sentiment analysis. Requires Python and some familiarity with Bayesian statistics. Take any three of the four Statistics.com courses on this topic (this course, plus the courses listed to the right under "related courses," not including conferences). For savings, use the promo code "text-specialization" and register for all three courses at once for $1197 ($399 per course, not combinable with other tuition savings). If you register for all four, you'll still receive the discounted rate.
The required text is Fundamentals of Predictive Text Mining (Springer, 2015) by Weiss, Indurkhya and Zhang. Be sure to purchase the 2015 edition. It may be purchased here.
Assignments can be done in Python, and instructor can assist with Python. Some familiarity with Python is needed. An optional alternative is Java-based software available from the instructor's website http://www.data-miner.com/software.html .
To be scheduled.
To be scheduled.
Course Fee: $549
Do you meet course prerequisites? What about book & software? (Click here to learn more)
Group rates: Click here to get information on group rates.
First time student or academic? Click here for an introductory offer on select courses. Academic affiliation? You may be eligible for a discount at checkout.
Take 3 or 4 text analytics courses, save $100 per course (code text2016).
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.
The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).
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