Graduatehttps://www.youtube.com/watch?v=SBru73rwAGA1.5 Year LoopAnalytics for Decision SupportThis course focuses on practical methods to extract meaningful analytical insights from data and to communicate those findings to stakeholders, so they can be incorporated in actionable work processes to achieve organizational results.
Students will use Python libraries and programming to perform data manipulation, statistical analysis, predictive modeling, and visualization in small projects to comprehend the analytical method and its limitations more fully. Students are not expected to be proficient in Python, but they should have done introductory courses in computer programming and basic statistics.
Since software development in any procedural language follows similar principles, students with previous experience in programming using Java, C#, C/C++, or MATLAB will easily learn how to develop good data analytics programs in Python. At the very least, a student should be able to do the following in one language as a prerequisite.
• Understand variables, data types, and work with basic classes of data
• Develop and assign data structures and data tables
• Read data sets from file and output analysis results to files of different formats
• Understand the basic arithmetic, logical, and assignment operators
• Work with loop and conditional control statements
• Comprehend and utilize library functions to perform analytical processing
• Develop data visualizations in the form of graphs and charts of various types
All exercises include a written report that clearly, and concisely presents the results of the assignment. The goal of the reports is to build confidence in reporting of data analysis efforts. All assignments are to be presented in a format that follows the standard academic paper structure of abstract, introduction, methodology, results, and discussion. The final project is to be presented as a more in-depth report that follows the same structure as assignments, with an accompanying slide presentation to further bolster the students’ ability to communicate results of data analytics problem solving.
As a simple preparatory exercise, students should be able create a Python script that reads in a data set and perform some basic list operations on the data. Such data sets can be found at sites such as https://www.kaggle.com/datasets. A suitable environment for Python is the Anaconda distribution of tools that can be found at https://www.anaconda.com/products/individual.CybersecurityElectrical & ComputerEngineering Artificial IntelligenceSoftware
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