CSC 105 Introduction to Computer Science: Data Analytics and Data Visualization
Instructor: Peggy Batchelor Office: Riley 200A Phone: 864-294-3222
Lecture Tues & Thursday 8:30- 9:45, Riley Hall, Room 106Lab Thursday 02:30PM - 04:30PM, Riley Hall, Room 201
Office Hours: Office Hours: Tuesday, Thursday 11:30-12:30, or by scheduled appointment
E-mail: Course Web site: cs.furman.edu/~pbatchelor/csc105
COURSE DESCRIPTION
The purpose of this course is to introduce you to data analytics and data visualization. Together these comprise business intelligence (BI) which refers to the use of the computer to analyze complex information about an organization and its competitors for use in planning and decision making. Data analytics is a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions. Although business is a natural application for data analytics, it is not the only one. Data analytics are used extensively in medicine, the life sciences, and the social sciences.
Data analytics is one of the current “hot topics”. It is an area which is growing as organizations realize that they must improve their understanding of their capabilities and those of the competition if the quality of their decisions is to be competitive. The vast amount of data now available to organizations adds to the complexity of analytics.
Additionally, students in this course will learn basic programming principles using the Python programming language
SOFTWARE
Hands-on experience is provided through labs that use several leading-edge technologies, including:
1. Descriptive and predictive analytics using Excel 2013
a) “What-if” analysis
b) Regression analysis
2. Visualization of data using Tableau
3. Relational data base technology using Access
a) Relational database design – normalization, referential integrity
b) Querying a database
4. Python programming
5. Prescriptive analytics using Solver
6. Decision tree and probability analysis using TreePlan (if time allows)
COURSE LEARNING OBJECTIVES
1. To become familiar with the processes needed to develop, report, and analyze data.
2. To learn how to use and apply selected data analytics software.
3. To become familiar with basic structured programming constructs and module design.
METHOD:
This course stresses the factors that impact the performance of decision makers and the data management and analysis methods that have value to them. This course includes lectures and demonstrations that emphasize discussion and illustration of methods, as well as hands-on, practical lab exercises that provide both a sound basis of learning and an opportunity to test and develop skill. The use of data analytics software supports the presentation of the material. Students complete assigned readings, lab exercises, and participate in discussions. Teams of about 2-3 students will complete a final software project.
SOFTWARE PROJECT
The purpose of the software project is to make you, your teammates, and the class familiar with a particular type of software and apply it to analyzing data of some complexity. The project should take the knowledge and background that you are learning this semester about data analysis and visualization and put it to good use in a new, creative effort. A real key to the project, however, is to select a data set that people will find interesting and intriguing. I will provide you with some web sites where you can find data sets (or you can create or find your own).
It will be up to your team to select the particular software product to work with. You may use software that we used in lab or choose from software available on the web. On many of the vendors’ websites, you will find downloadable, trial or evaluation copies of their software. You will normally need to register to use the software (a very simple process). The software that you'll download will typically be “crippled” in some way. For example, it may expire in 30 days or the amount of data that can be stored is limited. This should not pose a problem for your team. The final design of the class presentation will be up to your team, but it should include:
1. An overview of the particular software product that your team has selected if it is not software we have used in lab.
2. A description of the data set(s) you are using and the methods you used for attaining, cleansing and/or formatting the data.
3. What analysis of the data were you able to achieve? What new information were you able to discover from your analysis of the data – trends etc.
The project presentation should cover the motivation behind your project, a description of your system, problems/things you learned.
The main part of the presentation is a live demo of the system.
All members of the group must participate in the presentation.
I will evaluate the overall quality of your project, including all milestones and components. The following questions will be important during that evaluation process:
1. Does the system work- does it read in the data and present a visualization/analysis of the data?
2. Does your project support different analytical questions about the data? Simply presenting visualizations of the data is not enough – you must explain your conclusions and analysis of the data
3. Is the analysis creative and does it illustrate some new ideas? (This isn't absolutely crucial, but simply re-implementing a well-known tool or technique is not so appealing.)
4. Was your presentation an effective discussion and portrayal of the project?
5. Does your report help someone understand the problem and how your system addresses that problem?
COURSE GRADING
Student performance will be evaluated on the following basis:
Major software projects 20%
Case Studies/labs and attendance 5%
Average of 2 - 3 Midterm exams 40%
Final exam 35%
CLASS ATTENDANCE, PARTICIPATION, AND ASSIGNMENTS
Lecture: Your involvement in class discussions and activities are crucial for your development as a professional.It is important for you to be prompt and regular in attendance and current with the assigned readings.I will expect you to ask questions during class, state your viewpoints and opinions, and be prepared to answer questions from the readings and discuss the case studies.
Lab: You are expected to attend every class and lab, and to be ON TIME. Lab activities often begin with a demo. When you are late it is your responsibility to catch up (without bothering other students).
With appropriate documentation, I will excuse absences due to illness and/or emergencies (such as death in the family.)An example of documentation is a note from your physician, or the Health Services physician or Health Services nurse indicating that you were ill and the illness prevented you from attending class.Lack of documentation will result in an unexcused absence.Students with more than three hours of unexcused absence do not qualify for the grade of A, these absences, in fact, will result in a letter-grade reduction in the final grade.The University policy will be observed in cases of excessive absences.Regardless of attendance, you are responsible for all material, assignments, and changes in assignments.
If you miss a lab or lecture do not expect me to bring you up to date. However, I will be happy to help you if you have an excused absence OR if you do not understand the lecture or lab material.
Ignorance of an announcement made in class is no excuse for failure to meet an assignment.
EXAMS
The 2 midterm exams will be conducted during the regular class period. The final exam will be given during the scheduled final exam schedule. All exams contain both objective (e.g., T/F, multiple choice) and essay questions.
GRADING
A+ 98 or above
A 92-97
A- 90-91
B+ 88-89
B 82-87
B- 80-81
etc.