- No suggestions for

##### At MIT and Georgia Tech, MOOCs Are Showing Up On Campus

For the first time, on-campus students can earn credit from a MOOC.”

via MOOC Report

##### Massive List of MOOC Providers Around The World

Where to Find MOOCs: The Definitive Guide to MOOC Providers

via MOOC Report

##### Coursera Experiments With A Single Subscription Price for the Entire Catalog

For $49/month learners can get full access to Coursera's entire catalog

via MOOC Report

- Specialization via Coursera and Rice University
- $343 for 6-8 months
- 7-10 hours a week of effort
- 5 courses + capstone project

Rating based on 0 student reviews.

Title

Fundamentals of Computing

Rating

☆☆☆☆☆ (0 Reviews)

Overview

To be an effective programmer

Credential Type

Provider

Institution

Cost

$343

Effort

7-10 hours a week

Duration

6-8 months

This series introduces students to the basics of analyzing and solving computational problems. It will provide students with a solid foundation in the mathematical and programming techniques that support creating efficient and well-designed solutions for such problems. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses.

1

An Introduction to Interactive Programming in Python (Part 1)
★★★★★ (3008)
5 weeks
11th Dec, 2017

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse.
In part 1 of this course, we will introduce the basic elements of programming (such as expressions, conditionals, and functions) and then use these elements to create simple interactive applications such as a digital stopwatch. Part 1 of this class will culminate in building a version of the classic arcade game "Pong".

2

An Introduction to Interactive Programming in Python (Part 2)
★★★★★ (52)
4 weeks
11th Dec, 2017

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse.
In part 2 of this course, we will introduce more elements of programming (such as list, dictionaries, and loops) and then use these elements to create games such as Blackjack. Part 1 of this class will culminate in building a version of the classic arcade game "Asteroids". Upon completing this course, you will be able to write small, but interesting Python programs. The next course in the specialization will begin to introduce a more principled approach to writing programs and solving computational problems that will allow you to write larger and more complex programs.

3

Principles of Computing (Part 1)
★★★★★ (29)
5 weeks
11th Dec, 2017

This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games.
In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science.
Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.

4

Principles of Computing (Part 2)
★★★★☆ (16)
11th Dec, 2017

This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Understanding these principles is crucial to the process of creating efficient and well-structured solutions for computational problems. To get hands-on experience working with these concepts, we will use the Python programming language. The main focus of the class will be weekly mini-projects that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games.
In part 2 of this course, the programming portion of the class will focus on concepts such as recursion, assertions, and invariants. The mathematical portion of the class will focus on searching, sorting, and recursive data structures. Upon completing this course, you will have a solid foundation in the principles of computation and programming. This will prepare you for the next course in the specialization, which will begin to introduce a structured approach to developing and analyzing algorithms. Developing such algorithmic thinking skills will be critical to writing large scale software and solving real world computational problems.

5

Algorithmic Thinking (Part 1)
★★★★☆ (14)
4 weeks
11th Dec, 2017

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems.
In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".

6

Algorithmic Thinking (Part 2)
★★★★☆ (9)
11th Dec, 2017

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Once students have completed this class, they will have both the mathematical and programming skills to analyze, design, and program solutions to a wide range of computational problems. While this class will use Python as its vehicle of choice to practice Algorithmic Thinking, the concepts that you will learn in this class transcend any particular programming language.