Albert Campbell Collegiate Institute s1

MICHELS ACADEMY COURSE OUTLINE

DEPARTMENT: MATHEMATICS

Headmaster: Henry Michels

Course Name: Mathematics of Data Management Grade: 12 Course Code: MDM4U1 Pre/Co-requisite: Functions (MCR3U1) or Functions & Applications (MCF3M1)

Textbook(s): Mathematics of Data Management (McGraw-Hill Ryerson, 2001) Replacement Cost: $ Mathematics of Data Management Study Guide (2010) $

Course Description

This course broadens students’ understanding of mathematics as it relates to managing data. Students will apply methods for organizing and analysing large amounts of information; solve problems involving probability and statistics; and carry out a culminating investigation that integrates statistical concepts and skills. Students will also refine their use of the mathematical processes necessary for success in senior mathematics. Students planning to enter university programs in business, the social sciences, and the humanities will find this course of particular interest.

Overall Expectations & Units of Study

By the end of this course, students will: Organization Of Data and Statistical Analysis (30 hours) 1. Demonstrate an understanding of the role of data in statistical studies and the variability inherent in data, and distinguish different types of data; 2. Describe the characteristics of a good sample, some sampling techniques, and principles of primary data collection, and collect and organize data to solve a problem; 3. Analyse, interpret, and draw conclusions from one-variable data using numerical and graphical summaries; 4. Analyse, interpret, & draw conclusions from two-variable data using numerical, graphical, & algebraic summaries; 5. Demonstrate an understanding of the applications of data management used by the media and the advertising industry and in various occupations; Counting and Probability (20 hours) 6. Solve problems involving the probability of an event or a combination of events for discrete sample spaces; 7. Solve problems involving the application of permutations & combinations to determine the probability of an event. Probability Distributions (20 hours) 8. Demonstrate an understanding of discrete probability distributions, represent them numerically, graphically, and algebraically, determine expected values, and solve related problems from a variety of applications; 9. Demonstrate an understanding of continuous probability distributions, make connections to discrete probability distributions, determine standard deviations, describe key features of the normal distribution, and solve related problems from a variety of applications; Culminating Data Management Investigations (throughout the course) 10. Design and carry out a culminating investigation* that requires the integration and application of the knowledge and skills related to the expectations of this course; 11. Communicate the findings of a culminating investigation and provide constructive critiques of the investigations of others.

Assessment and Evaluation Strategies

In order to ensure that assessment and evaluation are valid and reliable, and lead to improvement of student learning, teachers of this course use a variety of the following strategies to assess student learning and to provide them with feedback:  teacher observation  oral presentations, interviews  essays, reports, reviews, critiques, letters, journals, creative writing, computer lab work  media works  quizzes, tests, examinations  performance tasks, dramatic presentations  portfolios, design projects, lab work  self-assessment, peer assessment  check lists, rubrics  questions and answers

Some of these strategies are also used for evaluation. However, evaluation is the responsibility of the teacher and is based on individual student demonstration of course expectations. Evaluated group tasks likewise must reflect individual accountability for learning and demonstration of course expectations through work submitted.

Achievement Categories/Strands Calculation of Final Mark

Knowledge / Understanding 35% See Below. Thinking 15% Application 35% Communication 15% Levels of Achievement Learning Skills . Responsibility Level 4 80 % - 100 % . Organization Level 3* 70 % - 79 % . Independent Work Level 2 60 % - 69 % . Collaboration Level 1 50 % - 59 % . Initiative Level R < 50 % . Self-regulation Learning skills are necessary for success and affect * Level 3 is defined as the Provincial standard. A level of achievement. They will be reported as student achieving a Level 3 should be well prepared follows: for work in the next grade level or the next course. E – Excellent S – Satisfactory G – Good N – Needs Improvement

Mark Content Session 1 Session 2

Test # 1 11 % Chapters 2 and 3 Test # 2 11 % Chapters 4, 5 and 6 Test # 3 11 % Chapters 7 and 8 Mini-Report 5 % Statistics Presentation 5 % Casino Project TBA TBA Peer Critique 2 % Casino Project Quizzes, Classwork 25 % TBA TBA and Assignments Casino Project 15 % Game Design & Probability Chapters 2-8 Final Exam 15 % (Full Course material)

Grade 9 Grade 9 Grade 9 Principles: Foundations: L.D.C.C. Academic T Applied

Grade 10 Grade 10 Grade 10 Principles: Foundations: L.D.C.C. Academic Applied

Grade 11 U Grade 11 U/C Grade 11 C Grade 11 Math Functions Functions and Foundations for for Work and Applications College Math Everyday Life

Grade 12 Grade 12 Grade 12 Grade 12 Grade 12 C Grade 12 Math Vectors and Advanced Data College Foundations for for Work and Calculus SAMPLEFunctions Management Technology College Math Everyday Life