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! Formulate and Solve problems using probability and counting techniques, 8. MIT6_0002F16_lec2.pdf. We do not grant any extensions. 3. 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. computational thinking for solving problems in Data Science. csc-131-computational-thinking. 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. [1] All assignments are due no later than 11:59 PM on the date specified. Syllabus - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Introduction to Computational Thinking and Data Science. DLS DIL Learning Progression for Mathematics and Computational Thinking . Let's take a look at the syllabus for Unit 1: Scope, parameter passing, mutable/immutable properties . Freely sharing knowledge with learners and educators around the world. Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. A focus on discrete mathematical tools for the working computer scientist. 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. The exam is open book / notes but not open Internet and not open computer. 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. This is an introductory course on computational thinking. Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. Apply correct mathematical terminology and notation to formulate problems, 5. Introduction to Computation and Programming Using Python: With Application to Understanding Data. At the beginning of the term, students are given two late days that they can use on problem sets. Laboratory The course is rigorous and rich in computational . Model sequences as recurrence relations, 6. Data science approaches for importing, manipulating, and analyzing data. Credit Fall 2021 Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. But computers think in binary - all 0's and 1's! This subject is aimed at students with little or no programming experience. MIT Press, 2016. The class will use the Python programming language. 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. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results 2. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results 2. 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. Apply correct mathematical terminology and notation to formulate problems, 5. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving 3. Elementary: Students should be encouraged to use mathematics and computational thinking in ALL areas of science. There will be 5 problem sets in the course. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits). Course Syllabus; Course Content Lecture Materials. Lectures: 2 sessions / week, 1 hour / session. We use the Julia programming language to approach real-world problems in varied areas applying data analysis and computational and mathematical modeling. Distance Learning 1. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. DLS DIL 6.00 satisfies all degree / minor requirements that can be satisfied by taking both 6.0001 and 6.0002. Ralph Hooper, Section 001 Computational Thinking & Block Programming in K-12 Education Specialization Beginner Level Approx. Provide an understanding of the role computation can play in solving problems. [Preview with Google Books] The book and the course lectures parallel each other, though there is more detail in the book about some topics. Formulate and Solve problems using probability and counting techniques, 8. Course Goals This course is designed for students who are serious about programming, and it requires both a strong algebraic background and strong problem-solving skills. W 20:45 - 21:45 Distance Learning 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 Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. 01/19/2021 - 05/16/2021, Section 001 An emphasis is placed on using logical notation to express rigorous mathematical arguments. Menu. Unit 1: Computational Thinking and Programming - II. Each problem set will involve programming in Python. 3 credit hours comprising of lectures and hands-on lab sessions. Language-agnostic foundations focus on pseudo-code . 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. [1] All assignments are due no later than 11:59 PM on the date specified. 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. 20012022 Massachusetts Institute of Technology, Electrical Engineering and Computer Science. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. We strongly urge you to see the late days and dropping the problem sets as backup in case of an emergency. Freely sharing knowledge with learners and educators around the world. Your best strategy is to do the problem sets early before work starts to pile up. There will be one final exam. 1. Instead, we offer late days and the option of rolling at most 2 problem set grades into the final exam score. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. This is an introductory course on computational thinking. DLS DIL If rolled, the percent that the problem sets are worth will be rolled into the final exam score. 6.0001 Introduction to Computer Science and Programming in Python or permission of the instructor. 08/24/2020 - 12/13/2020, Section 001 Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. 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. Course Materials. It aims to provide students with an understanding of the role computation can play in solving problems. Course Information This subject is aimed at students with little or no programming experience. This begins with an awareness of mathematics in science. to instill ideas and practices of computational thinking, and to have students engage in activities that show how computing changes the world. ONL DIL 11 hours to complete English Subtitles: English Could your company benefit from training employees on in-demand skills? 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. Students taking 6.00 will attend the 6.0001 and 6.0002 lectures and do the problem set for 6.0001 and 6.0002. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving, 3. More Info Syllabus Readings Lecture Videos Lecture Slides and Files Assignments Software Lecture Slides and Files. Please print whatever you may want to use during the quiz. Overview. 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. 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. Resource Type: Lecture Notes . Prerequisites: None. This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. 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. I T LS 3550: Comput at i onal T hi nki ng F al l 2020 course i n a uni que way. 1. 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. Computational Thinking is a set of specific problem solving processes and cognitive skills. Credit Spring 2021 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. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving 3. 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. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. ONL DIL 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. 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. 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. Credit Fall 2020 It comes even before programming. Meet the instructors for the course in the video. Lectures: 2 sessions / week, 1 hour / session, Recitations: 1 sessions / week, 1 hour / session. The students lowest score of the 10 problem sets will be dropped. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving. Description: This file contains the information regarding the Optimization Problems. 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. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits), STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. The remaining problem sets will be weighted equally. Sometimes, new material may be covered in recitation. A focus on discrete mathematical tools for the working computer scientist. 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. 1. An emphasis is placed on using logical notation to express rigorous mathematical arguments. To avoid surprises, we suggest that after you submit your problem set, you double check to make sure the submission was uploaded correctly. Demonstrate an understanding of Graphs and related topics (edges, vertices, walks, trails, paths, and circuits). dstfdsf An emphasis is placed on using logical notation to express rigorous mathematical arguments. Any additional late work beyond these late days will not be accepted. 2nd ed. STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. The 6.0002 final will serve as the 6.00 final. Students will apply their programming skills to a problem from their major or concentration. Ralph Hooper, Section 001 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. Ralph Hooper, Section 001 It describes the way of thinking at multiple levels of abstraction in order to make a complex problem look simple by . In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. 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. CSC 100 Class Information and Syllabus. 2. Computational Thinking for Problem Solving Anyone can learn to think like a computer scientist. Non-MIT students are encouraged tojoin the open discussion forum on Discord and find a cross-grading partner there. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Apply correct mathematical terminology and notation to formulate problems, 5. 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! Model sequences as recurrence relations, 6. ISBN: 9780262529624. Late days are discrete (a student cannot use half a late day). Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. In fact, we encourage students from any field of study to take this course. Modeling and visualizing real -world data sets in various science and engineering disciplines. 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. Laboratory 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. The 6.0001 final will serve as a 6.00 midterm. The videos linked below are also available in the form of a YouTube playlist. Model sequences as recurrence relations, 6. Students should learn to recognize and describe number patterns and use appropriate instruments such as rulers . Instructor: Stephen R. Tate (Steve) Lectures: Mon/Wed 10:00-10:50, Petty 223 Lab: Fri 10:00-11:50, Petty 222 . 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. Heidi Williams is a passionate coding and computational thinking advocate. This course provides a rigorous introduction to computational problem solving, thinking, and debugging, for those with little-to-no experience in computer science. Grades will be roughly computed as follows: Problem sets will be graded out of 10 points. Module 3: Climate Science. 2nd ed. It is available both in hard copy and as an e-book. Formulate and Solve problems using probability and counting techniques, 8. 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. ONL DIL Note: Finger exercises are not available on OCW. Recitations give students a chance to ask questions about the lecture material or the problem set for the given week. Distance Learning Students need to install the Julia programming language, as well as other tools and packages. 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. 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. 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. Construct proofs of assertions by choosing appropriate techniques from your proof toolset, 4. STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES. Introduction to Computation and Programming Using Python: With Application to Understanding Data. We use the Julia programming language to approach real-world problems in varied areas applying data analysis and computational and mathematical modeling. Tuesday sessions consist of prerecorded video lectures,released on YouTube and played live on the course website. Note: click on this, and actually read it; it's part of the syllabus: SyllabusGeneral . Homework consists of coding in the form of 10 problem sets, released on Thursdays and due before the following Thursdays class.. This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. Recitation attendance is encouraged but not required. Pay close attention to your email and announcements on . Syllabus _____ General syllabus. 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 . Discrete Math with Applications, Susanna Epp, 5th Edition, Cengage Learning, 2020. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results. Position students so that they can compete for research projects and excel in subjects with programming components. Laboratory A focus on discrete mathematical tools for the working computer scientist. Parameter Passing This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses. All of the Pluto notebook files for lecture sessions and homework are also available on the original GitHub site developed for the course. Try Coursera for Business Skills you will gain Education want hopefully Brainstorming Instructor Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. Topics include: ISBN: 9780262529624. 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. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. 08/23/2021 - 12/12/2021, Section 001 Thursday sessions consist of a half-hour prerecorded video lecture followed by a half-hour online discussion. 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! 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. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results, 2. ICS 140 Computational Thinking with Programming An introduction to the formulation of problems and developing and implementing solutions for them using a computer. Readings | Introduction to Computational Thinking and Data Science | Electrical Engineering and Computer Science | MIT OpenCourseWare Readings The textbook is Guttag, John. 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 Students analyze user requirements, design algorithms to solve them and translate these designs to computer programs. But you don't need to be a computer scientist to think like a computer scientist! A previous half-semester version of this course focused on the application of computational thinking to the Covid-19 pandemic. Syllabus, Lecture Materials, Assignments, and Labs. Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness. A significant portion of the material for this course will presented only in lecture, so students are expected to regularly attend lectures. M 18:00 - 21:45 To complete the course, you will first need to install Julia and Pluto on your computer. Submissions that do not run will receive at most 20% of the points. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations, 7. 20012022 Massachusetts Institute of Technology, Electrical Engineering and Computer Science, Introduction to Computational Thinking and Data Science. If you're comfortable in decimal, you could argue binary is easier; only 2 numbers, not 10 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! 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