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
This course is designed to be delivered in person through regularly scheduled, face-to-face class meetings. Instruction, discussions, and collaborative activities will take place in the classroom to support active engagement and real-time interaction. Presentations will be delivered in person during class sessions. Course materials and announcements will be shared through the designated learning management system (Blackboard) as needed.
Contact Information
- 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.
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd Edition, Aurelien Geron, 2019, O’reilly.
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.
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.
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 50%
- Final Project 25%
- Final Presentation 10%
- Class Participation 15%
Tentative course schedule:
| Week | Dates | Topics / Activities | Textbook Chapters | Assignment | Project Milestones |
|---|---|---|---|---|---|
| 1 | Jan 12–16 | Syllabus, Course Introduction; Python Basics Review: Data types, control flow, functions | Ch. 3 | – | – |
| 2 | Jan 19–23 | NumPy Fundamentals: Arrays, vectorization, broadcasting | Ch. 4 | HW1 | – |
| 3 | Jan 26–30 | – | |||
| 4 | Feb 2–6 | Pandas Introduction: Series, DataFrames, indexing | Ch. 5 | HW2 | – |
| 5 | Feb 9–13 | Introduce course project | |||
| 6 | Feb 16–20 | Data Cleaning and Preparation: Missing values, filtering, transformations | Ch. 7 | HW3 | – |
| 7 | Feb 23–27 | Project Proposal Due | |||
| 8 | Mar 2–6 | Data Wrangling and Joining: Merge, join, concatenate | Ch. 8 | HW4 | – |
| 9 | Mar 9–13 | Spring Break | – | ||
| 10 | Mar 16–20 | Data Wrangling and Joining: Merge, join, concatenate | Ch. 8 | Project Progress Check 1 | |
| 11 | Mar 23–27 | Exploratory Data Analysis (EDA): Summary statistics, understanding data | Ch. 9 | HW5 | – |
| 12 | Mar 30–Apr 3 | Project Progress Check 2 | |||
| 13 | Apr 6–10 | Data Aggregation and Group Operations | Ch. 10 | HW6 | – |
| 14 | Apr 13–17 | – | |||
| 15 | Apr 20–24 | Data Analysis Examples | Ch. 13 | – | – |
| 16 | Apr 27–May 1 | Project Work Sessions; Instructor feedback on analysis and visuals | – | – | Project Progress Check 3 |
| – | May 5 | Final Project Presentation Day | – | – | All students present final projects |
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.
- A 94-100% A- 90-93% B+ 87-89%
- B 83-86% B- 80-82% C+ 77-79%
- C 73-76% C- 70-72% D+ 67-69%
- D 60-66% F < 60%
Instructor Course Policies
Regular attendance is expected and strongly encouraged in this course, as in-class lectures, demonstrations, and hands-on activities are essential for building foundational data science skills. Students are responsible for all material covered during class sessions, including examples, discussions, and guided exercises.
More than three unexcused absences may negatively affect the final course grade. Students should notify the instructor in advance of any anticipated absence and provide appropriate documentation for excused absences when required. Because this course includes in-class activities and a final presentation instead of traditional exams or quizzes, attendance on scheduled activity and presentation days is particularly important. Missed in-class work or presentations may not be made up without prior approval.
Consistent attendance supports steady learning progress and contributes to a productive classroom environment.
Academic integrity is a core expectation in this introductory course. Students are expected to complete all assignments, projects, and presentations honestly and to properly acknowledge any sources of data, code, examples, or external materials used. Plagiarism, copying work from others, unauthorized collaboration, or misrepresentation of results are violations of academic integrity and will not be tolerated.
As an introduction to data science, this course emphasizes learning fundamental concepts, developing basic analytical skills, and understanding best practices. Students may discuss concepts with classmates; however, all submitted work must represent their own understanding and effort unless collaboration is explicitly permitted. Any use of external resources, including code examples or AI-assisted tools, must be appropriately cited.
Violations of academic integrity may result in penalties ranging from a reduced grade on the assignment to failure of the course, and may be reported in accordance with institutional policies. Students are responsible for reviewing and adhering to the academic integrity guidelines outlined in the Student Handbook.
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.
Undergraduate: SP25 Final Exam Schedule Graduate:
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. |
Class Attendance Policy |
Accessibility and Disability Services
Accessibility and Disability Services
Wilmington College provides accommodations and services for student with a variety of disabilities, including chronic illnesses, psychological, physical, medical, learning, and sensory disability amongst others. If you anticipate or experience barriers based on disability and feel you may need a reasonable accommodation to fulfill the essential functions of this course, you are encouraged to contact:
Spencer Izor, Associate Vice President of Compliance - Title IX/ADA Coordinator at spencer.izor@wilmington.edu or 937-481-2365 or Nathan Flack, Academic Resource Manager at 937-481-2208 to learn more about the process and procedures for requesting accommodations, or by visiting College Hall Room 306a or the Robinson Communication Center, Room 103.
Religious Accommodations
Wilmington College strives for an inclusive climate and welcomes students from all backgrounds, faiths, and experiences. If religious observance impedes your ability to participate fully in classroom activities or a principal holiday from your religious tradition occurs during the semester and conflicts with class meetings or activities, please make the professor aware of this immediately to determine if a reasonable accommodation is possible.