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