An Introduction to Interactive Programming in Python (Part 2)

Start Date: 02/23/2020

Course Type: Common Course

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About Course

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.

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Course Introduction

An Introduction to Interactive Programming in Python (Part 2) This is the second and last course in the specialization Introduction to Interactive Programming in Python. This course covers the second and third week of the six-week Python programming course. This course will take you from beginner to advanced Python programmer with everything covered in the first and second courses. You will learn more advanced concepts and techniques than you learned in the first course. This course is the last before the specialization which will introduce the concepts and techniques into more advanced Python programming. This is the second and last course in the specialization. As you will see, we use the Python programming toolkit to get you up and running quickly. We use Pandas as our data lab to make sure we have all the necessary tools to work with real data. We use the Numpy library to work with the data efficiently. We also use the Scipy library to make our data analysis easier. We use the IPython notebook to work with the code directly. We use Pandas to pull in all the necessary tools for the specific task at hand. We use Pandas to visualize the data so that you can see what is happening on the screen. We use the IPython console to work with the code directly. We use Pandas to pull in all the necessary tools for the specific task at hand. We use the IPython notebook to work with the code directly. We use Pandas to visualize the data so that you can see what is going on in the screen. An introduction to Python programming and

Course Tag

Python Syntax And Semantics Logic Programming Python Programming Object-Oriented Programming (OOP)

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