Python Methodologies
Python Methodologies was the most enjoyable and also the most useful. This was one of the few classes where I spent extra time on the work simply because it was fun. I had some prior programming experience from my undergraduate program but never applied it outside of the classroom. What the Python course did was help me realize how many different tasks I could improve and automate not just in an academic setting, but also at work and home. While we learned the basics in the course, I spent a lot of time on my own outside of class learning from online resources. This made an immediate impact in my career as I was able to use these new skills to modernize many of our older calculation routines and even develop new automated programs for my colleagues to use. (May 2019)
COURSE DESCRIPTION:
Hybrid course (Mostly online, 5-7 in person lectures, Lab hours as needed)
Python is becoming one of the most popular programming languages in the world. Used to teach programming at six of the top ten computer science programs in the U.S., Python has a reputation for being a well-supported language that is ideal for education. This support and quick learning curve has also made it popular among scientists. This course acts as an introduction to computer programming with the Python programming language. The basics of imperative programming will be covered as well as selected areas of computer science, object oriented programming and data structures. Computer programming is about problem solving so we will begin to think about how to solve problems in discrete steps like computers do. After the beginning of the course, when we have our sea legs, we will begin to introduce ideas from Data Science and use what we have learned about computer programming and problem solving in this area.
COURSE OBJECTIVES:
Upon completion of this course, candidates can expect to:
- Understand problem solving with computer programming, computational thinking and discrete algorithms.
- Demonstrate experience with the Python programming language and its design environments.
- Have the ability to create well documented computer programs that uses logical constructs and the syntax of the Python programming language.
COURSE OUTLINE:
Unit 1: Basics of a program, variables, assignments, conditionals,controls, programming environments.
Unit 2: Strings, lists, dictionaries, loops.
Unit 3: Functions, modularity, libraries, file i/0, exception handling.
Unit 4: Graphics, data handling, CS topics: sorting, searching, recursion, Big O
Unit 5: Object Oriented programming: classes, methods, constructors, inheritance, polymorphism
Unit 6: Simple Data Structures: stacks, queues, linked lists, trees
EXPERIENTIAL LEARNING:
This course has a lab component.
SKILLS ACQUIRED:
- Python
- Designing an application from scratch
- Data exploration and analysis
- Using programming concepts to analyze data
- Application of data structure concepts for big data
[this class] had some of the most interesting material I’ve ever learned and was taught in a manageable manner for computer programming beginners… I don’t hesitate in stating Python for Data Science is the best class I’ve taken in all of my years at Rutgers (May 2018)
