![]() You will apply these to your chosen dataset The Python script for Data Exploration is here.ĭata types exercises: Ch2 ex: 2,3,4,5,7,9 Python tutorial for data exploration- understand the code you are using. WeekĮxercises for this review are linked here Please note that videos of notes and lectures are found on the Moodle page. ![]() Topics sequence and readings based on the Introduction to Data Mining, 2nd ed., Tan et al text. It is best to submit accommodation requests before the semester begins, although requests can be made at any time during the semester.įurther details are found in Rhodes's course policies page. I encourage you to confirm that I have received a copy of your accommodation letter and schedule a time for us to meet to discuss your needs. To arrange for an accommodation based on a documented medical condition, mental health condition or learning disability (or if you suspect you have one), please contact Patty Klug, Director of Student Accessibility Services, by emailing her at calling 81. Juniata is committed to provide equitable access for student learning. My standard policies across all of my courses on attendance, late assignments, academic integrity, etc., are described on my Course Policies web Final data mining analysis project with supporting documentation (25%).Recorded presentation and comment on others (5%).You will apply the data mining tools techniques covered in class on the data set for knowledge discovery and classification, present the results of the project during the last two weeks of the semester and turn in a written project in lieu of a final exam. The data set must meet size criteria as outlined in the detailed description. Identify an existing, substantial data set that can be used to demonstrate the data mining techniques covered in class. Homework sets based on exercises from the textīelow are links to example paper exams from the undergraduate DS 352 (Fall '18) course. Use these to get a sense of testing form and style, but not necessarily a study guide.Projects in Python based on exercises from the text.Grading: 10%Weekly quizzes, on-line in Moodle 25% Assignments good practices of analytics for presentation, and conversely recognizing bad practices and why.commercial and open-source data mining and machine learning tools.in analyzing a dataset, applying the various DM techniques, applying the knowledge to explore a substantial data set.the appropriate use and understanding of classic DM algorithms used for clustering, classification, regression, and associations.transformation and extraction of data in preparation for the algorithms and tools.the data mining process through Knowledge Discovery in Data Mining (KDD).Students will build skills and/or gain understanding in: This course considers the organization of data, the current techniques, overview of algorithms and tools in mining information from these sources. Prerequisite: DS 500, DS 510 or by permission Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Python, Matlab, Weka and others. The link is their current academic download for both Mac and PCĬourse description: This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in wide ranging datasets, from the simple tables to complex data sets and big data situations. Tableau's data visualization software is provided through the Tableau for Teaching program.It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R Project for Statistical Computing R is a free software environment for statistical computing and graphics.Weka 3.6 Data Mining Documentation Īlternative data mining and machine learning tools downloads or links:.DOWNLOAD: Anaconda and Jupyter Getting Started with Python.TEXT: Introduction to Data Mining, 2nd ed., Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Bipin Kumar, published by Pearson, 2019, ISBN 978-0-13-312890-1.Make sure you are logging into Moodle several times each week. Moodle is the course management system for this course and will be used for material access, assignment/project submissions with their timing and deadlines, and grade posting.Materials will be found there. See Moodle for exact times and Zoom link. Office Hours are Monday and Wednesday evenings as regular available times. Office location: Brumbaugh Academic Center, C203
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