Data preparation for data mining 4 credits. Data mining.

Slides:



Advertisements
Similar presentations
Welcome Parents & Families!. Mr. Michel Back to School Night Math: Problem of the Week Full credit even if the answer is wrong Want to see their best.
Advertisements

Test Taking Strategies
BEST PRACTICES IN TEACHING INTRODUCTORY PROGRAMMING Beth Simon, Computer Science and Engineering UC, San Diego.
Supporting your student Preparing for exams. Interim report/ Parents’ Meeting S4 Parents’ Meeting 25 th Oct S5/6 Interim Reports: 26th Oct Try to identify.
Introduction to MATLAB Northeastern University: College of Computer and Information Science Co-op Preparation University (CPU) 10/20/2003.
D2L Gradebook Setup and Administration James Falkofske SCTC.
Exercise Exercise3.1 8 Exercise3.1 9 Exercise
Overview of Financial Management  Introduction  Keys to Success  Recitations  Class Structure - Syllabus  Text – 2nd Preliminary Draft of Fin. Mgmt.
Part II Tools for Knowledge Discovery. Knowledge Discovery in Databases Chapter 5.
Statistical inference
Exercise Exercise Exercise Exercise
Exercise Exercise Exercise Exercise
Exercise Exercise6.1 7 Exercise6.1 8 Exercise6.1 9.
Physics 326: Computer Based Experimentation and Physics Computing
Teacher: Mr. Daigle Semester 1 Year: 2011/2012.  1. Binder  2. Pen, Pencil, Eraser, and Calculator  3. Geometry Set.
Courses - programme Courses Description Syllabus Exercises in groups Differences between project course (PE) and study course (SE) Start of exercise.
Matlab tutorial course Exercises 2:. Exercises Copy the script ‘face_points.m’ from my webpage into your ‘scripts’ folder Create a new folder in your.
Core Methods in Educational Data Mining HUDK4050 Fall 2014.
1.
An Example of Course Project Face Identification.
Bloomfield School District TECH TUESDAY WORKSHOP Technology Services and Support Edline/EGP-Grading Online January 10, 2012 Joanne Decker.
Rationale / value of using statistics statistics is a powerful tool to objectively compare experimental data uncover relationships among variables experience.
Creating With Code.
WXGE 6103 Digital Image Processing Semester 2, Session 2013/2014.
Mika Seppälä June 6, 2014 Traditional teaching, flipped class rooms, and online instruction.
Goals Approach Evaluation Intro to Python The two on-line sources Getting started with LPTHW.
CSCI 51 Introduction to Computer Science Dr. Joshua Stough January 20, 2009.
System Integrity and Validity 1 Running Head: System Integrity and Validity System Integrity and Validity University of Phoenix Date:
An Overview. Google Maps: Satellite Images.
Analyzing Students' Behavior in a Beginner's Programming Course Marija Brkić, Higher Teaching Assistant Maja Matetić, Associate Professor.
Exploratory Data Analysis Exploratory Data Analysis Dr.Lutz Hamel Dr.Joan Peckham Venkat Surapaneni.
BOĞAZİÇİ UNIVERSITY DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS MATLAB AS A DATA MINING ENVIRONMENT.
Scholarship Skills Andrew Black 1 Lecture 1 Scholarship Skills Andrew P Black Winter 2013 All material © 1996–2012 David Maier, Tim Sheard, Andrew Black,
Guidelines for Selecting Your Curriculum at SBS. Students can choose classes amounting to a maximum of 60 credits (30 ECTS) per semester. Students attending.
28 January 2016Dan Remenyi1 Critiquing a piece of work Dr Dan Remenyi
Step 4: Understand Course codes and descriptions in the Faculty of Arts and Science Calendar PHY131H1 Introduction to Physics I A first university physics.
Undergraduate Research Participant Pool (URPP) Winter 2016.
Course Information CSE 2031 Fall Instructor U. T. Nguyen /new-yen/ Office: CSEB Office hours:  Tuesday,
Course Information CSE 2031 Fall Instructor U.T. Nguyen Office: CSE Home page:
C o u r s e O u t l i n e. o Course Objective o Text Books & Reference Books o Main topics to be covered o Weightage o Grading Policy o Assignments o.
WELCOME TO OPEN HOUSE! September 6, 2012 AP BIOLOGY G119 Mrs. Vanderfin Please sign-in at the side counters.
Understanding the Course Syllabus Presented By: Cynthia Curtis Thursday, March 20, 2014.
Course Information EECS 2031 Fall Instructor Uyen Trang (U.T.) Nguyen Office: LAS Office hours: 
Biolab III, 2017 Requirements for credit points:
MSc in Advanced Computer Science Induction 2016/17
Andy Wang Object Oriented Programming in C++ COP 3330
Course Information EECS 2031 – Section A Fall 2017.
Responsibilities CS 4501 / 6501 Software Testing
General Chemistry 1 CHEM 1411 Dr. Lorenz J Bauer
Responsibilities CS 4640 Programming Languages for Web Applications
PPAS/POLS 3190 Winter Term Week 1.
Using local variable without initialization is an error.
Andy Wang Object Oriented Programming in C++ COP 3330
CS6021 Final Project – Team Part
INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE Chapter 5: z-Scores
Time to test your knowledge!
Signals and Systems Chapter 0: Course Policies
CS/SE 4110 Senior Seminar.
Responsibilities CS 4640 Programming Languages for Web Applications
Introduction to Computer Architecture
DT001A, Simulation of communication systems, 7.5 ECTS
Course Information EECS 2031 Fall 2016.
W.A.M. (Writing About Math) Select one of three sets of words: > Choose the word you think is different > Write two or three sentences explaining why.
Math 8 “Year of Fundamentals” Pre-Algebra & Geometry
Bell Work: Falling Objects
Contents Contents Contents Contents Sub Copy Contents Sub Copy
How Should You Participate in this Course?
721070S Globally Responsible Business Course overview winter 2018 Anne Keränen Jan Hermes Pauliina Ulkuniemi.
Standards for Mathematical Practice
SEPTEMBER 2019 ISSUE/42 nd VOLUME.
Presentation transcript:

Data preparation for data mining 4 credits

Data mining

Data preparation Transforming (ie. fixing) of raw data into form suitable for modeling (tool). ° detect and fix errors ° replace missing values ° transform variables, especially categorial to numerical ° transform distributions ° time-series processing ° (image preprocessing)

Contents of the course A look of experience into data mining A uniquely complete look into data preparation Some fooling around with Matlab Discussion! Critique! Arguments!

To pass the course Keep a presentation (30-45 minutes) Attend the course Home exercises Practical exercise

Timetable Introductory lecture on September 22nd First student presentation on September 29th DL for exercises: January 16th Post-course lecture: January 19th

The book $40 + posting in Amazon group order? master copy available