Succeeding In This Course¶

Everyone can succeed in this course. Prepare for each class by dong the assigned reading and taking the reading quiz for that day. Each class also has exercises and assignments, some of which will be done in class and others that will be done as homework. These assignments and exercises are small, focused activities, all of which can be completed with a modest amount of time and effort. However, it is important to be actively engaged in every class and to do the homework in a timely way. Falling behind will jeopardize your opportunity for learning and success.

The course is organized in three spiral progressions through the major topics in the course as shown in the figure below. Each spiral deepens the course concepts and broadens the ability to engage in computational thinking. Each spiral ends with one of the projects - the last spiral ends with two projects

Course Organization

Each spiral ends with a project intended to demonstrate mastery of the material encountered in the that and previous spirals. The projects are progressively more mature and longer in time. The final spiral ends with two projects, the mini project and the final project. The mini project is a collaborative task for each cohort. The final project is an individual effort.

Be persistent. The basic concepts of the course are repeated three times during the term - once during each spiral. It is expected that the first time you see the concepts, you will develop some insight and understanding of them. However, you may completely miss the point of one or more of these concepts. This is not a problem because they will come around again. The goal of the course is not that you grasp these ideas immediately, but that you grasp them eventually. Competence at the end of the course is the measure of success.

Working in Cohorts¶

Being an active and engaged member of your assigned “cohort” is important to your learning. A cohort is a group of four or five students who are from different majors. The cohort serves two purposes:

First, it allows discussions within the cohort to be informed by the perspectives of different disciplines. For example, how a biologist understands and applies the concepts within the course will be different from how a historian does so. Sharing these different perspectives within the cohort enriches the learning of each cohort member.
Second, the cohort is a support network for peer learning. Everyone in the cohort is working to learn the same tools, languages, and skills. Getting help from members of your cohort is encouraged. Of course, you only get out of the cohort what you put into it. If you do not help others when they are having trouble, would you expect them to help you? There is no formal grading of your participation in the cohort. It is an opportunity and a valuable resource that you can gain or lose.

Individuals working in a group can behave in ways that foster or hinder effective group operation. Here is a description of constructive and destructive group behaviors that can profoundly affect the group’s success. You may have participated in groups before and seen these positive and negative behaviors at work. Your cohort will be asked to discuss these types of behaviors and, considering the needs and contributions of each cohort member, write and sign a contract that you create for your cohort. This contract should serve as a guide for how the members of the cohort agree to interact with each other and support one other’s learning.

Multiple Languages, Same Concepts¶

Computational “thinking” is a mode of thought that can only be developed in parallel with a corresponding skill in applying computation. Developing a skill in computation requires actually working with and building computations. Computations are constructed using a programming language. The course involves the use of two programming languages and the tools that support the execution of these languages.

While we will explore two different languages, each one has the same underlying set of concepts. In each language, we will focus on how abstractions are captured and manipulated by an algorithm written in that language. The core language elements are: action, sequence, decision, iteration, and state.

The two programming languages are:

BlockPy: a visual and safe programming environment
Python: a popular textual programming language

Each language has a different role in the course.

BlockPy is used to build small programs. BlockPy is a visual or graphical programming language whose name comes from the fact that algorithms in BlockPy are created by customizing and snapping together blocks. Each block represents (part of) a computational element. The shapes of the blocks only allow them to be assembled in ways that prevent “meaningless” computational statements from being formed.

Python is used to implement the major course project. Python is a general purpose programming language that software engineers use to build “real world” applications. We will learn “just enough” of the core elements of Python to complete a meaningful project. There is much more to Python than we will see in this class.

Big Data¶

We are awash in data. The World Wide Web facilitates the easy distribution of data that comes from human and automated sources. Individuals generate huge quantities of data via social networks and photo and video sharing sites for personal communication. Human activity generates data as a by-product that governments and organizations collect and store. Examples include records of financial transactions, reports on the movement of goods, histories of access to on-line media, stock market information, and documents from the provision of services. Analyzing this data is useful to citizens and customers who benefit from improvements in planning, regulation, and marketing that the analysis enables. Electronic medical records are repositories of valuable data that can be used to assess efficacy of drugs and performance of hospitals. Scientific instruments on earth and in space sense and report conditions in the oceans, atmosphere, and on other worlds. Traffic cameras, building sensors, GPS data... the list goes on.

The rise of “big data” applications is driven by the need to use these mountains of data to answer important questions. These applications are possible because of the decline in the costs of data storage, increased computing processor power, and high-speed networking. Three characteristics—the three Vs—are often used to define what Big Data means:

Volume: The data is of significant size, often measured in terabytes. The data stored by online retailers like Amazon illustrates this aspect of “big data.”
Velocity: The data are coming at a high rate and must be processed quickly. Stock market trading data is an example of this kind of data.
Variety: The data contain many different kinds of data elements in different formats and in different organizations. A patient medical record is of this kind; it may have personal contact information in text, x-ray images, coded medical tests that were performed, prescription information, etc.

The Dimensions of Big Data

In this course, we will use “big data” for the major course project. You will be able to select, from a gallery of big data streams, one data stream that is relevant to your major. You will use this data stream to answer some interesting questions in your field of study.

Linda Lawrey, “What does 1 Terabyte of storage really mean.” , June 1, 2012