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! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact.
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. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program.
This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.
Computational Thinking for Problem Solving
ofrecido por
Computational Thinking for Problem Solving
University of Pennsylvania
Programa - Qué aprenderás en este curso
Semana
1Pillars of Computational Thinking
Computational thinking is an approach to solving problems using concepts and ideas from computer science, and expressing solutions to those problems so that they can be run on a computer. As computing becomes more and more prevalent in all aspects of modern society -- not just in software development and engineering, but in business, the humanities, and even everyday life -- understanding how to use computational thinking to solve real-world problems is a key skill in the 21st century.
Computational thinking is built on four pillars: decomposition, pattern recognition, data representation and abstraction, and algorithms. This module introduces you to the four pillars of computational thinking and shows how they can be applied as part of the problem solving process.
6 videos (Total: 44 min), 6 quizzes
6 videos
1.2 Decomposition6m
1.3 Pattern Recognition5m
1.4 Data Representation and Abstraction7m
1.5 Algorithms8m
1.6 Case Studies11m
4 ejercicios de práctica
1.2 Decomposition10m
1.3 Pattern Recognition10m
1.4 Data Representation and Abstraction15m
1.5 Algorithms15m
Semana
2Expressing and Analyzing Algorithms
When we use computational thinking to solve a problem, what we’re really doing is developing an algorithm: a step-by-step series of instructions. Whether it’s a small task like scheduling meetings, or a large task like mapping the planet, the ability to develop and describe algorithms is crucial to the problem-solving process based on computational thinking. This module will introduce you to some common algorithms, as well as some general approaches to developing algorithms yourself. These approaches will be useful when you're looking not just for any answer to a problem, but the best answer. After completing this module, you will be able to evaluate an algorithm and analyze how its performance is affected by the size of the input so that you can choose the best algorithm for the problem you’re trying to solve.
7 videos (Total: 69 min), 10 quizzes
7 videos
2.2 Linear Search5m
2.3 Algorithmic Complexity8m
2.4 Binary Search11m
2.5 Brute Force Algorithms13m
2.6 Greedy Algorithms9m
2.7 Case Studies12m
6 ejercicios de práctica
2.1 Finding the Largest Value10m
2.2 Linear Search10m
2.3 Algorithmic Complexity10m
2.4 Binary Search10m
2.5 Brute Force Algorithms15m
2.6 Greedy Algorithms10m
Semana
3Fundamental Operations of a Modern Computer
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. 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. This module describes the inner workings of a modern computer and its fundamental operations. Then it introduces you to a way of expressing algorithms known as pseudocode, which will help you implement your solution using a programming language.
6 videos (Total: 46 min), 10 quizzes
6 videos
3.2 Intro to the von Neumann Architecture8m
3.3 von Neumann Architecture Data6m
3.4 von Neumann Architecture Control Flow5m
3.5 Expressing Algorithms in Pseudocode8m
3.6 Case Studies10m
5 ejercicios de práctica
3.1 A History of the Computer10m
3.2 Intro to the von Neumann Architecture10m
3.3 von Neumann Architecture Data10m
3.4 von Neumann Architecture Control Flow10m
3.5 Expressing Algorithms in Pseudocode10m
Semana
4Applied Computational Thinking Using Python
Writing a program is the last step of the computational thinking process. It’s the act of expressing an algorithm using a syntax that the computer can understand. This module introduces you to the Python programming language and its core features. Even if you have never written a program before -- or never even considered it -- after completing this module, you will be able to write simple Python programs that allow you to express your algorithms to a computer as part of a problem-solving process based on computational thinking.
9 videos (Total: 91 min), 12 readings, 12 quizzes
9 videos
4.2 Variables13m
4.3 Conditional Statements8m
4.4 Lists7m
4.5 Iteration14m
4.6 Functions10m
4.7 Classes and Objects9m
4.8 Case Studies11m
4.9 Course Conclusion8m
12 lecturas
Programming on the Coursera Platform10m
Python Playgroundm
Variables Programming Activity20m
Solution to Variables Programming Activity10m
Conditionals Programming Activity20m
Solution to Conditionals Programming Activity10m
Solution to Lists Programming Assignment5m
Solution to Loops Programming Assignment10m
Solution to Functions Programming Assignment10m
Solution to Challenge Programming Assignment10m
Solution to Classes and Objects Programming Assignment10m
Solution to Project Part 410m
12 ejercicios de práctica
4.2 Variables10m
4.3 Conditional Statements5m
4.4 Lists10m
Lists Programming Assignment15m
4.5 Iteration10m
Loops Programming Assignment30m
4.6 Functions10m
Functions Programming Assignment20m
(Optional) Challenge Programming Assignment20m
4.7 Classes and Objects10m
Classes and Objects Programming Assignment20m
Project Part 4: Implementing the Solution in Python25m
Instructores
Susan Davidson
Weiss ProfessorComputer & Information Science
Chris Murphy
Associate Professor of PracticeComputer & Information Science
Acerca de University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
Do I need to know how to program or have studied computer science in order to take this course?
No, definitely not! This course is intended for anyone who has an interest in approaching problems more systematically, developing more efficient solutions, and understanding how computers can be used in the problem solving process. No prior computer science or programming experience is required.
How much math do I need to know to take this course?
Some parts of the course assume familiarity with basic algebra, trigonometry, mathematical functions, exponents, and logarithms. If you don’t remember those concepts or never learned them, don’t worry! As long as you’re comfortable with multiplication, you should still be able to follow along. For everything else, we’ll provide links to references that you can use as a refresher or as supplemental material.
Does this course prepare me for the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?
This course will help you discover whether you have an aptitude for computational thinking. This is a useful predictor of success in the Master of Computer and Information Technology program at the University of Pennsylvania, which is offered both on-campus and online. In this course you will learn from MCIT instructors and become familiar with the quality and style of MCIT Online courses.
If you have a bachelor's degree and are interested in learning more about computational thinking, we encourage you to apply to MCIT On-campus (http://www.cis.upenn.edu/prospective-students/graduate/mcit.php) or MCIT Online (https://onlinelearning.seas.upenn.edu/mcit/). Please mention that you have completed this course in the application.
Where can I find more information about the Master of Computer and Information Technology (MCIT) degree program at the University of Pennsylvania?
Use these links to learn more about MCIT:
MCIT On-campus: http://www.cis.upenn.edu/prospective-students/graduate/mcit.php
MCIT Online: https://onlinelearning.seas.upenn.edu/mcit/
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