Introduction to Computer Science and Computational Thinking

In the first module, you researched different careers in computing and the skills needed to be successful. People in the field of computer science use problem solving skills and creativity to develop new technologies and solve problems that affect the way we live, work and play. Think of the new inventions within the past two years created by computer science.

Today, practically everyone is a computer user, and many people even program. The core of computer science is problem solving. Computer Science uses computational thinking to formulate problems in a way to use computers to help solve them. It involves such skills as critical thinking, algorithmic thinking and logical thinking. However, those that major in computer science are not the only ones that need to use computational thinking to solve problems. Everyone can benefit from learning how to become a better problem solver.

Computational thinking involves strategies for solving problems.

Identify and understand the problem by decomposition: Decomposition is analyzing the problem and breaking it up into smaller problems. You need to break a problem down to the details so you can clearly explain the process to another person or to a computer.

The task of cleaning the house or apartment may seem overwhelming. When you break it down into rooms it seems more manageable. Start with the living room, then the dining room, then the kitchen, then the bedrooms, and then finally the bathroom. This breaks the task into more manageable pieces. This principle is especially relevant to computing.

Analyze the problem to find patterns: Look to find the patterns that are common in all the problems. Patterns are frequently the basis for solving most problems. Techniques to find patterns might include, make a table, draw a diagram or model, work backwards, or use logical reasoning or trial and error.

Don't reinvent the wheel. A good programmer or problem solver recognizes that the problem is similar to others he has solved before and applies the same solution or logic. For example, finding the daily high and low temperatures in a list of temperatures is exactly the same problem as finding the highest and lowest grades in a list of test scores.

Use abstraction to hide unnecessary details: When you solve a problem you need to recognize the parts that do not need to be solved and generalize the information that is necessary. See what is different between all the problems. Take out all differences and you are left with a common framework that will work for all the problems.

A perfect example of abstraction is a map. If you want to find the state of Georgia on a map you do not need to see all the other detailed information. You only need to see where the state of Georgia is located relative to other states in the United States. If you want to find Atlanta, you would then need to look at more details to find where it is located within the state of Georgia. Abstraction involves ignoring the unnecessary details so you can focus on the information you need.

It also lets you generalize the information that is necessary. For example, when you order a pizza, you only tell the pizza company what toppings you want on your pizza. You never have to say a want a crust, sauce, etc. Everyone knows that a pizza starts with a crust and has sauce. Of course there are always specialty orders.

Create algorithms for the solution: Algorithms are a sequence of step by step instructions. It uses a common abstraction that allows for variable differences.

An algorithm with the abstraction and generalization to prepare a pepperoni and beef pizza might look like the following. I do not have to list the ingredients for the crust and sauce in the algorithm. That information has been abstracted from the algorithm. A separate procedure for creating the crust and sauce would be created and used in other algorithms.

Pepperoni_Pizza
Put crust in pan
Put 1/4 cup of sauce over crust
Put 2 cups of cheese on top of the sauce.
Put 30 pepperonis on top pf the sauce.
Cover with 1 cup of extra cheese
Bake for 20 minutes 350 Degree oven Beef Pizza
Put crust in pan
Put 1/4 cup sauce over the crust
Put 2 cups of cheese on top of sauce
Put 21/2 cups of ground beef on top
Cover with 1 cup of extra cheese
bake for 20 minutes 350 degrees

The variable in the algorithms above is in red. This is the information that would change.

During this course you will be using computational thinking strategies to create solutions to solve problems using the computer.

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