CIS1863 - DATA MINING
3 Credit Hours
Student Level:
This course is open to students on the college level in either the Freshman or Sophomore year.
Catalog Description:
CIS1863 - Data Mining (3 hrs.)
This course introduces students to fundamental machine learning concepts and techniques for identifying patterns within data sets. Students will explore various machine learning models, including supervised and unsupervised learning, and apply industry-standard tools to analyze data. Through hands-on projects, students will evaluate data, train models, and generate data-driven recommendations. Emphasis is placed on practical applications, ethical considerations, and the interpretation of machine learning results for decision-making across various domains.
KRSN: n/a
Course Classification: Lecture (List if Lecture/Lab, Physical Activity, etc.)
Prerequisites:
None
Co-requisites:
None
Controlling Purpose:
This course equips students with an understanding of machine learning concepts to identify patterns within data sets. Students will use machine learning tools to evaluate data and provide recommendations based on their analyses.
Learner Outcomes:
Upon completion of the course, the student will:
- Understand Data Mining Concepts: Students will define data mining and differentiate between descriptive, predictive, and prescriptive analytics, gaining foundational knowledge of analytical methods and models.
- Apply Descriptive Statistics: Students will classify types of data, including population vs. sample, quantitative vs. categorical, and cross-sectional vs. time series, and compute key statistical measurements such as location, variability, and distributions.
- Perform Descriptive Analyses: Students will conduct descriptive analysis using techniques such as cluster analysis, association rules, sampling distributions, interval estimation, and hypothesis testing to summarize and interpret data.
- Explore Prescriptive Analysis Methods: Students will apply prescriptive analytics techniques, including simple and multiple linear regression, the least squares method, model fitting, and time series forecasting using moving averages and exponential smoothing.
- Utilize Predictive Data Mining Techniques: Students will implement predictive models such as k-nearest neighbors, regression trees, and Monte Carlo simulations to assess risk, simulate outcomes, and perform linear optimization, including what-if and sensitivity analyses.
- Integrate Analytical Methods in Decision-Making: Students will combine descriptive, prescriptive, and predictive analytics to analyze data, identify patterns, and support data-driven decision-making in real-world scenarios.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content not including the final examination period. Instructors may add other material as time allows.
UNIT 1: Understanding Data Mining Concepts
Outcomes: Upon completion of this unit, students will be able to:
- Define data mining and explain its significance in data analysis.
- Differentiate between descriptive, predictive, and prescriptive analytics.
- Identify and describe common analytical methods and models used in data mining.
UNIT 2: Applying Descriptive Statistics
Outcomes: Upon completion of this unit, students will be able to:
- Classify data types, including population vs. sample, quantitative vs. categorical, and cross-sectional vs. time series.
- Compute key statistical measurements such as mean, median, mode, variance, and standard deviation.
- Interpret data distributions and variability in different datasets.
UNIT 3: Performing Descriptive Analyses
Outcomes: Upon completion of this unit, students will be able to:
- Conduct cluster analysis to group data based on similarity.
- Apply association rule mining to discover relationships between variables.
- Utilize sampling distributions and hypothesis testing to draw data-driven conclusions.
- Perform interval estimation to assess confidence levels in predictions.
UNIT 4: Exploring Prescriptive Analysis Methods
Outcomes: Upon completion of this unit, students will be able to:
- Implement regression models, including simple and multiple linear regression.
- Apply the least squares method to fit regression models.
- Use time series forecasting techniques, such as moving averages and exponential smoothing.
- Evaluate model fit and effectiveness in predictive analytics.
UNIT 5: Utilizing Predictive Data Mining Techniques
Outcomes: Upon completion of this unit, students will be able to:
- Implement predictive modeling techniques such as k-nearest neighbors (k-NN) and regression trees.
- Conduct Monte Carlo simulations to assess risk and simulate possible outcomes.
- Perform linear optimization, including what-if and sensitivity analyses, to evaluate decision alternatives.
UNIT 6: Integrating Analytical Methods in Decision-Making
Outcomes: Upon completion of this unit, students will be able to:
- Combine descriptive, prescriptive, and predictive analytics to analyze data trends.
- Identify patterns in large datasets to support data-driven decision-making.
- Apply analytics techniques to real-world business, finance, healthcare, or operational scenarios.
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
None
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies:
402.00 Academic Code of Conduct
263.00 Student Appeal of Course Grades
403.00 Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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