Heidi Williams is a passionate coding and computational thinking advocate. Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. ONL DIL A significant portion of the material for this course will presented only in lecture, so students are expected to regularly attend lectures. Introduction to Computation and Programming Using Python: With Application to Understanding Data. Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. MIT Press, 2016. The course is rigorous and rich in computational . An overall grade will be assigned based on the following scale: 90% - 100% A 89% - 80% B 79% - 70% C 69% - 60% D 0% - 59% F. Do NOT buy the textbook materials access until you receive detailed instructions from your instructor! This course provides a rigorous introduction to computational problem solving, thinking, and debugging, for those with little-to-no experience in computer science. This course is designed for students who are serious about programming, and it requires both a strong algebraic background and strong problem-solving skills. Credit Fall 2021 An overall grade will be assigned based on the following scale: 90% - 100% A 89% - 80% B 79% - 70% C 69% - 60% D 0% - 59% F. Do NOT buy the textbook materials access until you receive detailed instructions from your instructor! 20012022 Massachusetts Institute of Technology, Electrical Engineering and Computer Science. 2nd ed. In fact, we encourage students from any field of study to take this course. Computational Thinking & Block Programming in K-12 Education Specialization Beginner Level Approx. Model sequences as recurrence relations, 6. Laboratory csc-131-computational-thinking. Note: click on this, and actually read it; it's part of the syllabus: SyllabusGeneral . The videos linked below are also available in the form of a YouTube playlist. 20012022 Massachusetts Institute of Technology, Electrical Engineering and Computer Science, Introduction to Computational Thinking and Data Science. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. Ralph Hooper, Section 001 Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. Learning Progression for Mathematics and Computational Thinking . Recitation attendance is encouraged but not required. to instill ideas and practices of computational thinking, and to have students engage in activities that show how computing changes the world. This is an introductory course on computational thinking. DLS DIL DLS DIL Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. This course includes both an overview of Computational Thinking tools (Abstraction, Decomposition, Pattern Recognition, and Algorithm Design) and an Introduction to the Discrete Mathematical topics of Logic, Proof, Sets, Functions, Relations, Counting, and Graphs. 6.00 satisfies all degree / minor requirements that can be satisfied by taking both 6.0001 and 6.0002. [1] All assignments are due no later than 11:59 PM on the date specified. Up to three late days may be accumulated in this fashion in this course, i.e., you can only have a maximum of 3 late days at any point in time. Sometimes, new material may be covered in recitation. Late days are discrete (a student cannot use half a late day). Formulate and Solve problems using probability and counting techniques, 8. P a rt i ci p a n t P ro f i l e T h i s Co mp u t a t i o n a l T h i n ki n g co u rse i s d e si g n e d f o r a l l K -1 2 e d u ca t i o n a u d i e n ce s se e ki n g t o ONL DIL Apply correct mathematical terminology and notation to formulate problems, 5. Overview. 11 hours to complete English Subtitles: English Could your company benefit from training employees on in-demand skills? Provide an understanding of the role computation can play in solving problems. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits). Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving 3. Course Materials. Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. It is available both in hard copy and as an e-book. Model sequences as recurrence relations, 6. An emphasis is placed on using logical notation to express rigorous mathematical arguments. However, before we are able to write a program to implement an algorithm, we must understand what the computer is capable of doing -- in particular, how it executes instructions and how it uses data. DLS DIL This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. CBSE Class 12 Computer Science Detailed Syllabus Unit 1: Computational Thinking & Programming -2. Students need to install the Julia programming language, as well as other tools and packages. Freely sharing knowledge with learners and educators around the world. Meet the instructors for the course in the video. 01/19/2021 - 05/16/2021, Section 001 Computational Thinking and Programming: Syllabus Computational Thinking and Programming DSCI 15310 Sec 003 Fall 2013 Course Description: Introductory, broad, and hands-on coverage of basic aspects of computational thinking with emphasis on problem solving using a high-level programming language. W 18:00 - 20:45 Distance Learning Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. Freely sharing knowledge with learners and educators around the world. An emphasis is placed on using logical notation to express rigorous mathematical arguments. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. dstfdsf This begins with an awareness of mathematics in science. OCW has additional versions of 6.00 that include useful materials; this course will closely parallell the material covered in these versions: The textbook is Guttag, John. Lectures: 2 sessions / week, 1 hour / session. Homework consists of coding in the form of 10 problem sets, released on Thursdays and due before the following Thursdays class.. Laboratory [Preview with Google Books] The book and the course lectures parallel each other, though there is more detail in the book about some topics. As we assign final grades, we will maximize your score based on the choice to roll the weight of at most two problem sets into your final exam score. To avoid surprises, we suggest that after you submit your problem set, you double check to make sure the submission was uploaded correctly. computational thinking for solving problems in Data Science. More Info Syllabus Readings Lecture Videos Lecture Slides and Files Assignments Software Lecture Slides and Files. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. Help students, including those who do not necessarily plan to major in Computer Science and Electrical Engineering, feel confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language. Each problem set will involve programming in Python. The Unit 1 of Computer Science Class 12 Syllabus focuses on advanced-level computational thinking and programming including concepts like basic of python, function, python libraries, etc. In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. Apply correct mathematical terminology and notation to formulate problems, 5. Syllabus Course Meeting Times Lectures: 2 sessions / week, 1 hour / session Recitations: 1 sessions / week, 1 hour / session Prerequisites 6.0001 Introduction to Computer Science and Programming in Python or permission of the instructor. Submissions that do not run will receive at most 20% of the points. Ralph Hooper, 5 Discussion assignments average will be 20% of your grade, 5 Terminology assignments average will be 20% of your grade, 5 Project assignments average will be 30% of your grade, 3 Competency Exams average will be 30% of your grade. Introduction to Computation and Programming Using Python: With Application to Understanding Data. All of the Pluto notebook files for lecture sessions and homework are also available on the original GitHub site developed for the course. Instructor: Stephen R. Tate (Steve) Lectures: Mon/Wed 10:00-10:50, Petty 223 Lab: Fri 10:00-11:50, Petty 222 . W 20:45 - 21:45 2.1 - Principal Component Analysis 2.2 - Sampling and Random Variables 2.3 - Modeling with Stochastic Simulation 2.4 - Random Variables as Types 2.5 - Random Walks 2.6 - Random Walks II 2.7 - Discrete and Continuous 2.8 - Linear Model, Data Science, & Simulations 2.9 - Optimization. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. Any additional late work beyond these late days will not be accepted. Readings | Introduction to Computational Thinking and Data Science | Electrical Engineering and Computer Science | MIT OpenCourseWare Readings The textbook is Guttag, John. [1] All assignments are due no later than 11:59 PM on the date specified. Unit 1: Computational Thinking and Programming - II. Let's take a look at the syllabus for Unit 1: Scope, parameter passing, mutable/immutable properties . Try Coursera for Business Skills you will gain Education want hopefully Brainstorming Instructor Prerequisites: None. Laboratory The 6.0002 final will serve as the 6.00 final. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. Ralph Hooper, 12 Discussion assignments average will be 20% of your grade, 12 Project assignments average will be 50% of your grade, 3 Exams average will be 30% of your grade. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that . There will be one final exam. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. This is an introductory course on computational thinking. Formulate and Solve problems using probability and counting techniques, 8. 1. Syllabus, Lecture Materials, Assignments, and Labs. Programming and Computational Thinking Paul H. Chook Department of Information Systems and Statistics, Baruch College ID: CIS 2300 MSA [31783] Term: Fall 2022 Time: Saturdays, 11:10am-2:05pm, Jan 28, 2022-May 24, 2022 (3 hours; 3 credits) Location: In-Person: B - Vert 11-145 If switching to virtual is needed, the Zoom link is below. Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. Distance Learning The course includes an introduction to computational thinking and a broad definition of each concept, a series of real-world cases that illustrate how computational thinking can be used to solve complex problems, and a student project that asks you to apply what they are learning about Computational Thinking in a real-world situation. Credit Fall 2020 Computational Thinking - CSCI E-1b Computational Thinking by Nick Wong '20 Binary We're used to thinking in decimal; we have 10 fingers after all! In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. If you're comfortable in decimal, you could argue binary is easier; only 2 numbers, not 10 Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses. This course covers fundamental aspects of computational logic, with a focus on how to use logic to verify computing systems, and can be used as a breadth course for Software Engineering, Programming Languages, and Information Security. At the beginning of the term, students are given two late days that they can use on problem sets. Thursday sessions consist of a half-hour prerecorded video lecture followed by a half-hour online discussion. This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. We do not grant any extensions. Elementary: Students should be encouraged to use mathematics and computational thinking in ALL areas of science. Course Syllabus; Course Content Lecture Materials. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. This course includes both an overview of Computational Thinking tools (Abstraction, Decomposition, Pattern Recognition, and Algorithm Design) and an Introduction to the Discrete Mathematical topics of Logic, Proof, Sets, Functions, Relations, Counting, and Graphs. The 6.0001 final will serve as a 6.00 midterm. Description: This file contains the information regarding the Optimization Problems. Parameter Passing Topics include: This subject is aimed at students with little or no programming experience. An overall grade will be assigned based on the following scale: 90% - 100% A 89% - 80% B 79% - 70% C 69% - 60% D 0% - 59% F. Do NOT buy the textbook materials access until you receive detailed instructions from your instructor! Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving 3. Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. The remaining problem sets will be weighted equally. Tuesday sessions consist of prerecorded video lectures,released on YouTube and played live on the course website. 6.0001 Introduction to Computer Science and Programming in Python or permission of the instructor. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving. Model sequences as recurrence relations, 6. This course includes both an overview of Computational Thinking tools (Abstraction, Decomposition, Pattern Recognition, and Algorithm Design) and an Introduction to the Discrete Mathematical topics of Logic, Proof, Sets, Functions, Relations, Counting, and Graphs. Language-agnostic foundations focus on pseudo-code . Instead, we offer late days and the option of rolling at most 2 problem set grades into the final exam score. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits), STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. Introduction to Computational Thinking and Data Science. Ralph Hooper, Section 001 Modeling and visualizing real -world data sets in various science and engineering disciplines. Menu. ISBN: 9780262529624. 08/23/2021 - 12/12/2021, Section 001 2. In this course, students will use these computational tools to model and solve real-life problems that will develop their computational thinking and problem-solving skills. ICS 140 Computational Thinking with Programming An introduction to the formulation of problems and developing and implementing solutions for them using a computer. The exam is open book / notes but not open Internet and not open computer. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. Learn About and Develop Computational Thinking Skills Algorithms and Procedures Data Collection, Representation, and Analysis Problem Decomposition Abstraction Automation Simulation Parallelization Contents and Overview In over 4 1/2 hours of content including 57 lectures, this course covers core computational thinking concepts. Formulate and Solve problems using probability and counting techniques, 8. Pay close attention to your email and announcements on . 2nd ed. Students analyze user requirements, design algorithms to solve them and translate these designs to computer programs. 3 credit hours comprising of lectures and hands-on lab sessions. Resource Type: Lecture Notes . To develop problem solving skills, CSpathshala proposes a curriculum and provides sample teaching aids, created by the CSpathshala community, that are available to schools at no cost under a Creative Commons Attribution 4.0 International License .The draft curriculum guidelines as well as syllabus (with links to teaching aids) are presented below. This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. A focus on discrete mathematical tools for the working computer scientist. Apply correct mathematical terminology and notation to formulate problems, 5. Students should learn to recognize and describe number patterns and use appropriate instruments such as rulers . Computational Thinking is a set of specific problem solving processes and cognitive skills. 1. I am open t o i deas and proposal s i f you t ake t he t i me t o meet wi t h me Your best strategy is to do the problem sets early before work starts to pile up. Position students so that they can compete for research projects and excel in subjects with programming components. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results 2. Recitations give students a chance to ask questions about the lecture material or the problem set for the given week. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. An emphasis is placed on using logical notation to express rigorous mathematical arguments. We use the Julia programming language to approach real-world problems in varied areas applying data analysis and computational and mathematical modeling. It describes the way of thinking at multiple levels of abstraction in order to make a complex problem look simple by . A focus on discrete mathematical tools for the working computer scientist. Grades will be roughly computed as follows: Problem sets will be graded out of 10 points. Module 3: Climate Science. 1. A focus on discrete mathematical tools for the working computer scientist. Starting with Problem Set 1, additional late days can be accumulated for each assignment, one late day for each day the assignment is turned in ahead of the deadline. We strongly urge you to see the late days and dropping the problem sets as backup in case of an emergency. Course Information This subject is aimed at students with little or no programming experience. M 18:00 - 21:45 Distance Learning Her over 25 years of experience in education include serving as language, science and mathematics teacher for grades 6-8, as well as roles as a differentiation specialist, technology integration specialist, instructional coach, gifted and talented coordinator, elementary principal and K-8 director of curriculum. 1. It comes even before programming. The staff will keep track of late days and feedback for each problem set will include the number of late days the student has remaining. Students will apply their programming skills to a problem from their major or concentration. Ralph Hooper, Section 001 Lectures: 2 sessions / week, 1 hour / session, Recitations: 1 sessions / week, 1 hour / session. Computational thinking is a problem-solving process in which the last step is expressing the solution so that it can be executed on a computer. Please print whatever you may want to use during the quiz. 08/24/2020 - 12/13/2020, Section 001 Syllabus _____ General syllabus. Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. 3. Credit Spring 2021 Data science approaches for importing, manipulating, and analyzing data. MIT6_0002F16_lec2.pdf. Note: Finger exercises are not available on OCW. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results 2. It aims to provide students with an understanding of the role computation can play in solving problems. ISBN: 9780262529624. We use the Julia programming language to approach real-world problems in varied areas applying data analysis and computational and mathematical modeling. Syllabus - Free download as PDF File (.pdf), Text File (.txt) or read online for free. CSC 100 Class Information and Syllabus. Course Goals Computational Thinking for Problem Solving Anyone can learn to think like a computer scientist. Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. To complete the course, you will first need to install Julia and Pluto on your computer. ONL DIL Non-MIT students are encouraged tojoin the open discussion forum on Discord and find a cross-grading partner there. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits). MIT Press, 2016. Students from outside MIT are welcome to use the course materials and work their way through the lecture videos and homework assignments, though they do not have access to the MIT-only discussion forum on Piazza and may not submit homework for grading. I T LS 3550: Comput at i onal T hi nki ng F al l 2020 course i n a uni que way. 6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. A previous half-semester version of this course focused on the application of computational thinking to the Covid-19 pandemic. Students taking 6.00 will attend the 6.0001 and 6.0002 lectures and do the problem set for 6.0001 and 6.0002. If rolled, the percent that the problem sets are worth will be rolled into the final exam score. Ralph Hooper, 5 Discussion assignments average will be 10% of your grade, 5 Terminology assignments average will be 10% of your grade, 5 Quiz assignments average will be 20% of your grade, 5 Project assignments average will be 30% of your grade, 3 Competency Exams average will be 30% of your grade. The students lowest score of the 10 problem sets will be dropped. There will be 5 problem sets in the course. But computers think in binary - all 0's and 1's! Covering the concepts and techniques of variables, data types, algorithm, sequence, selection, iteration, classes, objects, methods and the processes ofrunning, testing and debugging computer programs. 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