DT320:
A survey of major data science tools and concepts. Students will learn the process of acquiring, cleaning, managing, and analyzing data sets to produce insights and make data-driven decisions. They will also gain experience with narrative building and visual storytelling using data.
Meeting Times, Location, & Course Delivery Details
Contact Information
TEXTBOOK 1: Python for Data Analysis, 3rd Edition, Wes McKinney, O’reilly. The book is now available on the author’s website for free (https://wesmckinney.com/book) and for sale on Amazon.
TEXTBOOK 2: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd Edition, Aurelien Geron, 2019, O’reilly.
IMPORTANT DATES:
Last Add Date January 17
Last Drop Date March 26
Final Exam May 7, 2025, 10:15 A.M. – 12:15 P.M.
PURPOSE AND OBJECTIVE: The course aims to equip students with fundamental data science skills, focusing on tools and concepts essential for acquiring, cleaning, managing, and analyzing datasets. Through practical applications, students will develop proficiency in narrative building and visual storytelling using data. The objectives include fostering critical thinking, promoting collaborative work, preparing for advanced studies and careers, and instilling ethical data practices. By the course's end, students will be well-versed in data-driven decision-making, poised for success in both academic and professional contexts.
MATERIAL TO BE LEARNED: Throughout the course, students will grasp foundational data science concepts, mastering techniques for acquiring, cleaning, and analyzing datasets using tools like Numpy, Pandas, Matplotlib, Scikit-Learn, and StatModels. Practical skills will be honed through hands-on experience, emphasizing narrative building and visual storytelling. Critical thinking in data science, collaboration on projects, and ethical considerations will be integral components. The curriculum prepares students for advanced studies and careers while fostering a continuous learning mindset, encouraging exploration of additional resources and advanced topics beyond the standard curriculum.
COURSE DELIVERY: This course is designed to be delivered online in a synchronous format. Communication will be facilitated through Zoom, and all sessions will be recorded to accommodate students with limited internet connectivity. Quizzes and tests will shift to take-home formats, ensuring flexibility, and any presentations required will be conducted through zoom or recorded videos submitted to me. This adaptive approach aims to maintain the integrity of the learning experience regardless of the mode of delivery.
PLATFORM AND ACCESS: Classes will be held via Zoom.
COURSE WORKLOAD AND EXPECTATIONS: To support your success in this course, please be aware that a minimum of two hours of out-of-class student work is expected for each hour of in-class time. This means that for every hour spent in class, you should plan to dedicate approximately two additional hours outside of class to complete assignments, study, and engage with course materials. This expectation ensures that you have ample time to grasp the concepts, complete homework, and prepare for assessments.
SKILLS: This course aims to cultivate a diverse set of skills essential for success in the field of data science. Students will acquire proficiency in data acquisition from various sources, master the art of data cleaning and preprocessing, and apply data analysis techniques using tools like Scikit-Learn, Pandas, and Numpy. The curriculum emphasizes the development of narrative-building skills, enabling students to communicate data insights effectively. Additionally, students will gain expertise in visual storytelling through the creation of compelling visualizations using libraries such as matplotlib and Statmodels. Critical thinking is fostered, encouraging a discerning approach to data science problems. Collaborative teamwork is emphasized through group projects, and ethical considerations in data practices are highlighted, including privacy and responsible data use. The course also instills project management skills, continuous learning mindset, and prepares students for advanced studies and careers in the dynamic realm of data science.
Learning Outcomes:
- Students are familiar with a variety of data sources and types of data
- Students can create and manage databases, dataframes, and other relevant data storage formats
- Students can perform simple queries, joins, and other data management techniques
- Students are familiar with the landscape of Data Science and Analytics tools
- Students are familiar with common sources of algorithmic bias, and best practices for minimizing it
- Students can apply simple statistical analysis tools to data, and interpret the results (e.g., summary statistics, smoothing, correlation analysis, ANOVA, outlier analysis, simple regression, etc.)
- Students can use visualization tools to produce data-driven visual communications in a variety of formats, including infographics, written reports, presentations, and web applications
- Students can complete a simple data analytics task from beginning to end, including data acquisition and management, modeling, analysis, and communicating results
HOMEWORK POLICY: Homework assignments play a crucial role in reinforcing concepts learned in class and promoting individual understanding. In this course, the following policies govern homework submissions and assessments:
- Timely Submission: All homework assignments are expected to be submitted by the specified deadline. Late submissions may result in a deduction of points, with the severity of the penalty increasing the longer the delay.
- Minimum Homework Score: The lowest homework score will be dropped from the total, which allows for flexibility and accommodates any challenges you may face.
- Quality and Originality: Homework solutions should reflect individual effort and understanding. Plagiarism or copying from external sources is strictly prohibited and will result in academic consequences.
- Clarity and Organization: Clear presentation of solutions and organized work are essential. Use proper formatting, labeling, and explanations to ensure that your responses are easily understandable.
- Collaboration: Unless explicitly stated otherwise, homework assignments are to be completed individually. Unauthorized collaboration may lead to academic penalties.
- Grading and Feedback: Assignments will be graded based on correctness, completeness, and adherence to instructions. Constructive feedback will be provided to aid in your understanding and improvement.
- Resubmission: In certain cases, resubmission of corrected assignments may be allowed after receiving feedback. However, this is at the discretion of the instructor and may be subject to specific guidelines.
PROJECTS: Projects are integral components of this course, providing opportunities to apply data science concepts to real-world scenarios. To ensure a successful and collaborative project experience, the following policies are in place:
- Team Formation: Projects may be conducted individually or in teams, as specified by the instructor. Teams should be formed early, and collaborative dynamics should be maintained throughout the project duration.
- Project Proposal: A project proposal outlining the objectives, scope, methodology, and expected outcomes must be submitted and approved by the instructor before the project commences. This ensures alignment with course objectives and provides guidance for successful project execution.
- Progress Updates: Regular progress updates, including milestones achieved and challenges faced, are required. These updates foster transparency, allow for timely feedback, and ensure that projects are on track.
- Collaboration and Communication: Effective communication within teams is crucial. Utilize designated channels for team communication, and promptly address any issues or concerns that may arise during the project.
- Documentation: Thorough documentation of the project, including data sources, methodologies, and code, is essential. Clear documentation enables understanding, transparency, and reproducibility.
- Final Presentation: Each team is required to deliver a final presentation showcasing the project's objectives, methodology, findings, and visualizations. This presentation provides an opportunity to communicate results effectively.
- Individual Contribution: In team projects, individual contributions should be clearly delineated. Each team member is expected to actively participate and contribute to the project's success.
- Ethical Considerations: Projects must adhere to ethical standards, respecting privacy, confidentiality, and legal requirements. Any ethical concerns should be addressed promptly.
- Submission Deadline: Projects must be submitted by the specified deadline. Late submissions may be subject to grade deductions.
EVALUATION: GRADING SCALE:
Homework 50% A 93-100% A- 90-92% B+ 87-89%
Final Project 25% B 83-86% B- 80-82% C+ 77-79%
Final Presentation 10% C 73-76% C- 70-72% D+ 67-69%
Class Participation 15% D 60-66% F < 60%
PREREQUISITES: DT 230 “Intro to Computer Programming”, and (MT 131 "Intro to Statistics" or EC334 “Business Stats I” or EC335 “Business Stats II”)
Instructor Course Policies
ACADEMIC INTEGRITY AND ATTENDANCE POLICY: Upholding academic integrity is paramount in this course, with severe consequences for violations. Plagiarism, cheating, and unauthorized collaboration can lead to failing grades for assignments or exams and referral for judicial review. Additionally, more than three unexcused absences will significantly impact your grade, and excessive absences may lower it naturally. It is essential to communicate in advance about any absence, providing a valid reason and documentation for excused absences. Quizzes and exams require students to show their work for full credit, emphasizing clarity in expressing calculator processes if used extensively. Cell phone use, including texting, is strictly prohibited. Familiarizing yourself with the current Student Handbook is crucial for understanding academic integrity policies, examination procedures, and the attendance policy, especially regarding excused absences, classroom behavior, and the process for handling academic misconduct charges. Adhering to these policies ensures a fair and enriching educational experience for all.
Institutional and Program-Level Policies
All exams will follow the Final Exam Schedule. Students scheduled to take three or more final examinations on one day may request to arrange their examination schedule, so no more than two exams occur on one day.
Requests for early or late exams are considered only under extreme circumstances. Prior to the exam period, the student must file a written request on the Early/Late Exam Form available in the Student One Stop Center, Academic Records, and on the WC portal. The form must be signed by the Instructor and the Academic Dean, approving the alternate exam time. This process must be completed prior to the scheduled exam period.
Out-of-class Work Expectation A minimum of 2 hours of out-of-class student work is expected for each hour of in-class time for traditional face-to-face courses. For online and hybrid courses, the combination of face-to-face time and out-of-class work should be equal to 3 hours per credit hour per week. |
Instructional Course Delivery |
Academic Integrity Policy The use of generative AI is prohibited except where expressly allowed in assignment instructions. |
Academic Misconduct - Examples [10/24]
ACADEMIC CODE OF CONDUCT
This policy is directly related to the first Testimony, which is part of the Student Code of Conduct. “I will practice personal and academic integrity.” The initial responsibility for dealing with academic misconduct lies with the individual faculty member in whose classroom or course of study the offense occurs. The responsibility includes determination of the consequences for the offense. The goal is for faculty to confront cheating and plagiarism, to teach ethical behavior, and to provide an appropriate consequence based on the nature of the incident. Faculty are encouraged to explicitly address academic misconduct and its consequences in the course syllabus.
EXAMPLES OF ACADEMIC MISCONDUCT
A. Examination offenses include, but are not limited to, the following:
1.Taking unauthorized materials into or out of the examination room.
2.Leaving the examination room without authorization before completing an examination.
3.Talking in the examination room without authorization.
4.Discussing the examination outside the examination room during the course of the examination.
5.Attempting to observe the work of another student.
6.Taking an examination for another person or permitting someone else to do so.
7.Collaborating improperly by discussion, joint research, or joint effort in any way expressly prohibited by the instructor. This includes using a cell phone or other device to access information from another source or another student.
8.Improper knowledge of contents of an examination - No student shall knowingly acquire unauthorized knowledge of an examination or any part of an examination, or solicit, offer, or give information about any part of an examination.
B. Student work offenses include, but are not limited to, the following, which are expressly prohibited in the absence of prior written approval of the instructor or instructors involved:
1.Resubmission of work - Submitting work which has been previously submitted for credit.
2. Plagiarism - Submitting work done wholly or partly by another, including the unattributed copying of all or parts of a published work or internet document. Using generative Artificial Intelligence (AI) sources to produce work (when not expressly permitted) is also a form of plagiarism. Some instances of plagiarism are the result of ignorance rather than dishonesty. When plagiarism is encountered, the instructor should be sure that the student knows proper procedures for attributing content.
3.Prohibited sources - Consulting material or persons contrary to the directions of the instructor.
4.Improper collaboration - Engaging in any discussion, joint research, or joint effort of any kind expressly prohibited by the instructor.
5.Deception - Misrepresenting the authenticity of sources, citations, or principles in any written work.
6. Sharing work – Students who share their work with others are responsible for how that work is used. For example, if a student shares a paper with another student to help him or her understand an assignment, and that student submits the work as their own, the author of the paper shares responsibility for the plagiarism committed by the other student.
D. Other misconduct - Engaging in any other improper conduct as specified by the instructor.
E. Lying – deliberately providing false information relevant to academic matters, such as misrepresenting the inability to take an examination because of illness.
F. Disruptive or disrespectful classroom behavior – causing a disturbance in the classroom, interrupting instruction, speaking rudely or threatening students or faculty.
Class Attendance Policy |
ADA (Americans with Disabilities Act)
Students with Disabilities
In accordance with ADA (Americans with Disabilities Act Amendments Act of 2008 (ADA), Section 504 of the Rehabilitation Act of 1973), Wilmington College provides access through reasonable accommodations to students with documented physical and psychological disabilities.
Students who wish to access Services need to meet with the Director of Accessibility and Disability Services and provide verification of their disability. To register with Accessibility and Disability Services, students submit an Application for Services. In addition, the student must provide the Disability Verification form accompanied by current disability documentation from a licensed professional. For more information, contact the Director of Accessibility and Disability Services at accessibility@wilmington.edu or 937.481.2444, 114 Robinson Communcation Center.