Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Data analysis software is introduced. Student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
- Secrets of Analytical Leaders: Insights from Information Insiders by Wayne Eckerson (ISBN – 978-1935504344)
- Exploratory Data Analysis with R (https://leanpub.com/exdata) – Free download
- rStudio software (https://www.rstudio.com/products/rstudio/download/#download) – Free download
● R software (https://cran.r-project.org/) – Free download
● Journal of Statistical Software ( https://www.jstatsoft.org/index)
Program Learning Outcomes (PLOs)
PLO1: Demonstrate the foundational knowledge and skills critical to pursue data analytics as a profession in relation to statistics and math.
PLO2: Articulate and effectively defend the significance and implications of the work in data analytics in terms of challenges and trends in a local, national or global context.
PLO3: Demonstrate the knowledge of advanced tools in data analytics.
PLO5: Apply the principles, tools and methods of analytics to a comprehensive real-world problem or project related to data analyses for tactical and/or strategic decision making.
PLO6: Integrate the major theories, tools, and approaches in data analytics to identify data-driven insights for informed business process management.
SAIL Baseline Mapping
|Enter 5 for Central, 4 for Significant, 3 for Moderate, 2 for Minimal, 1 for Potential, or 0 for None||Enter 1 for Passive Engagement, 2 for Active Engagement, or 3 for Generative Engagement|
|Social Consciousness & Commitment||Global Mindset||Intellectual Agility||Personal & Professional Effectiveness||Well-Being||Level of Engagement|
Refer to SAIL for Web at https://sail.northeastern.edu/about/
Course Learning Outcomes
Based on satisfactory completion of this course, a student should be able to:
CLO1: Advocate for the use of data mining techniques to the leadership of an organization to achieve stakeholder alignment and business objectives
CLO2: Use the business objectives to discern “sound data” from noise with which to begin modeling to answer the relevant business questions
CLO3: Use data mining, EDA, and storytelling in to address a business problem in a way that conveys actionable insights to your peers and leadership
CLO4: Apply skills and knowledge of the analytics field to promote a data driven culture within an organization
CLO5: Choose, with appropriate reasoning, models and algorithms to complete a data modeling project
- One (1) academic credit requires 50 minutes a week of classroom or faculty instruction and about two hours of out of class student work for a 15-week course; 100 minutes a week of classroom or direct faculty instruction and about 3.5 hours of out of class student work for a 7.5-week course.
- For a three-credit course, students should expect 2.5 hours a week of classroom or faculty instruction and a minimum of 5 hours of out of class student work for a 15-week course; 5 hours of classroom or direct faculty instruction and a minimum of 10 hours of out of class student work for a 7.5-week course.
- APA citations
As the weekly class session is a vital part of the learning experience, all students are expected to attend every week, be on time for the start of class, and stay until the end of class.
However, in the event of extraordinary, legitimate and unavoidable situations, students may be excused for lateness or absence. Extraordinary, legitimate and unavoidable situations include personal illness, urgent family business, work-related issues, transportation-related issues, religious requirements. If at all possible, students should let the instructor know by e-mail about the excused absence or lateness before class.
Policy on late work
Each assignment is due on the date indicated – late assignments will not receive any points. There are no make-up dates, extensions, or re-works for the assignments after they are handed in, except for documented personal emergencies or special permission granted by the instructor in writing. Special permission must be requested in writing to the instructor at least two days prior to the due date of the assignment.
Each week, you will be expected to:
- Review the week’s learning objectives
- Complete all assigned readings (approximate time spent: 2 hours)
- Complete all lecture materials for the week (approximate time spent: 1.5 hours)
- Participate in the Discussion Board (approximate time spent:1.5 hours)
- Complete and submit all assignments and tests by the due dates (approximate time spent: 5-8 hours)
Participation/Discussion Board [ALL STUDENTS – ALL COURSE DELIVERY MODES]
- At least one primary response is due by 11:59 pm PST on the Wednesdayof each week.
- At least two secondary responses are due by 11:59 pm PST on Sundayof each week.
- Note: Week 6
- (One primary response is due by 11:59 pm PST on the Tuesday)
- (Two secondary responses are due by 11:59 pm PST on Friday.)
To facilitate interaction, students are expected to review the online postings on a regular basis even after they have posted their own minimum required postings. Please treat your classmates and the instructors with the utmost respect. Inappropriate posts will be removed immediately. The instructor reserves the right to penalize students for repeated violations of the participation policy (and/or Academic Integrity Policy) within a course. In the discussion board and in class, high quality contributions advance the class discussions and do not simply summarize the material that was assigned. Quality contributions take into account not only the instructor’s questions but also your classmates’ contributions. Please be mindful that the Discussion Board is a space for academic exchanges. As a result, students are accountable for using proper and exacting punctuation, spelling, and grammar. In addition, you may be required to reference all outside sources in correct citation format. It is crucial that all participants maintain a high regard for proper decorum in the Discussion Board.
Rubrics and assignments points are outlined in each assignment in Canvas.
Graduate Programs Final Grading Scale
|95-100% A||87-89.9% B+||77-79.9% C+||69.9% or below F|
|84-86.9% B||74-76.9% C|
|90-94.9% A-||80-83.9% B-||70-73.9% C-|
|Activity/Assignment Type||# Activities||Points Per Activity||This group’s % of final grade|
|Final Project and milestones
● Project Draft
● Final Project Report
|Total Course Points||100%|
|–||Exploratory Data Analysis||Secrets of Analytical Leaders, Chapters 1-4||Discussion
Assignment 1: EDA assigned
Final Project Proposal: Group assignments and data selection.
|–||Introduction to Data Mining Techniques||Secrets of Analytical Leaders, Chapters 5-7||Discussion
Assignment 1: EDA due
Assignment 2: Technique Practice
Final Project: EDA assigned
|Clusters, Association Mining, and Linear Discriminant Analysis||Secrets of Analytical Leaders, Chapters 8-10||Discussion
Assignment 3: Technique Practice
Final Project: EDA due
|Text Mining||Secrets of Analytical Leaders, Chapters 11-13||Discussion
Assignment 4: Technique Practice
Final Project: Draft Report assigned
|Support Vector Machines||Secrets of Analytical Leaders, Chapters 14-17||Discussion
Assignment 5: Technique Practice
Final Project: Draft Report due
|Final Project & Wrap-up||Secrets of Analytical Leaders, Chapters 18-20||Final Project Report submitted
Final Project Presentation
End-of-Course Evaluation Surveys
Your feedback regarding your educational experience in this class is very important to the College of Professional Studies. Your comments will make a difference in the future planning and presentation of our curriculum.
At the end of this course, please take the time to complete the evaluation survey at https://neu.evaluationkit.com. Your survey responses are completely anonymous and confidential. For courses 6 weeks in length or shorter, surveys will be open one week prior to the end of the courses; for courses greater than 6 weeks in length, surveys will be open for two weeks. An email will be sent to your HuskyMail account notifying you when surveys are available.
A commitment to the principles of academic integrity is essential to the mission of Northeastern University. The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge. Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University.
As members of the academic community, students must become familiar with their rights and responsibilities. In each course, they are responsible for knowing the requirements and restrictions regarding research and writing, examinations of whatever kind, collaborative work, the use of study aids, the appropriateness of assistance, and other issues. Students are responsible for learning the conventions of documentation and acknowledgment of sources in their fields. Northeastern University expects students to complete all examinations, tests, papers, creative projects, and assignments of any kind according to the highest ethical standards, as set forth either explicitly or implicitly in this Code or by the direction of instructors.
Go to http://www.northeastern.edu/osccr/academic-integrity-policy/ to access the full academic integrity policy.
CPS expects independent and original work. Dishonesty and/or carelessness violates fundamental values of an intellectual and professional community and will be reported to the Office of Student Conduct and Conflict Resolution (OSCCR) as a violation of the Academic Integrity Policy. To safeguard the integrity of assignments and programs, your course may use systems such as TurnItIn, which checks written work, and Examity, an online exam proctoring system. Work that contains academic integrity violations (AIV) will be graded on a case by case basis. Work displaying AIV may earn a failing grade, a zero, or even result in the instructor assigning the student a failing grade for the class. Students may not withdraw from a class to avoid the grade penalty for serious academic integrity violations.
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