Course Title: Data Science and Big Data
Course Section and Number: IS 5213.7D1
Term and Year: Spring 2025
Course Description: This course introduces the latest data analytics tools and platforms, explores the rapidly developing field of Data Science. You will learn how best to gain actionable insights from big data, as well as to develop data solutions and data transformation road maps for businesses of varying sizes and complexity levels. The goal of this course is to maximize the utilization of available data and optimize the efficiency of decision-making. Previous experience with Hadoop, Spark or distributed computing is not required.
Learning Outcomes: Upon completion of this course, the student should be able to:
1. Configure library packages formatted for their target environment.
2. Prepare data using modeling techniques to ensure quality results.
3. Develop predictive models using machine learning and statistical techniques.
4. Recommend business solutions to stakeholders based on big data insights.
Prerequisite: None
Required Text:
• R for Absolute Beginners – Hands – on R Tutorial
Free Online Version:
https://www.researchgate.net/publication/331209857_R_for_Absolute_Beginners_-_Hands- on_R_Tutorial
Author: Duarte and Magno
Published Date: 2018
• Additional material online or provided by instructor videos and notes
Course Requirements:
Attendance/Participation: All students are expected to log in to their courses regularly throughout the week to receive instruction, materials, and updates from the instructor. It is your responsibility to check in and submit your assignments, complete your discussion board postings, and finish quizzes and exams by the due dates.
If you do not participate in the course, you will be counted absent. Simply logging in is not enough; you must submit/complete an assignment, post to a discussion board, or other similar assignment tasks to avoid being counted absent. Instructors are required to submit attendance the Monday following each week of class.
This attendance is reported to the Financial Aid Department and may result in the loss of any financial aid refund you are expecting if you have not been participating in your courses. In addition, you will be administratively dropped from the course if you are reported absent a total of three weeks.
Content:
Week 1: Install “R”, Exploratory Data Analysis (EDA)
Week 2: Data Scrubbing
Week 3: Decision Trees
Week 4: Model Validation
Week 5: Random Forests and Gradient Boosting Models
Week 6: Linear and Logistic Regression
Week 7: Principal Component Analysis (PCA) and tSNE analysis
Week 8: Clustering and Segmentation
Grading/Evaluation:
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Assignments
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:
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500 Points
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Quizzes
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:
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350 Points
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Discussions
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:
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150 Points ========
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Total
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:
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1000
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