Spring 2025 | CIS-1280-VO01 - Introduction to Data Analytics
Online Class
Online courses take place 100% online via Canvas, without required in-person or Zoom meetings.
Location: Online
Credits: 3 (45 hours)
Day/Times: Meets online
Semester Dates: 01-21-2025 to 05-05-2025
Last day to drop without a grade: 02-03-2025 - Refund Policy
Last day to withdraw (W grade): 03-24-2025 - Refund Policy
This course has started, please contact the offering academic center about registration
Faculty
Hilary Ivy
View Faculty Credentials
View Faculty Statement
Hiring Coordinator for this course: Deb Grant
General Education Requirements
This section meets the following CCV General Education Requirement(s) for the current catalog year:
VSCS Digital and Technical Literacy
Note
- Many degree programs have specific general education recommendations. In order to avoid taking unnecessary classes, please consult with additional resources like your program evaluation, your academic program catalog year page, and your academic advisor.
- Courses may only be used to meet one General Education Requirement.
Course Description
This course introduces foundational skills, concepts, applications, and techniques used in the field of data analytics. Students explore a variety of applied business scenarios and examine how analysts structure, present, communicate and make data-driven decisions. Topics include data types and formats, structured thinking, bias and the ethical use of data, the role of databases and spreadsheets, and best practices for organizing and securing data.
Essential Objectives
1. Define and explain key concepts, practices, analytical skills, tools, and processes used in data analytics.
2. Demonstrate effective questioning techniques and strategies that help guide data analysis.
3. Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics.
4. Explain how each step of the problem-solving process contributes to common data analysis scenarios.
5. Examine how analysts decide which data to collect for analysis.
6. Discuss the use of data in the decision-making process including how data analysts present and communicate findings with teams and stakeholders.
7. Demonstrate the use of spreadsheets in the data life cycle including organization, formulas, functions, and custom data tables.
8. Describe how analysts use spreadsheets and SQL with databases and data sets.
9. Examine how assumptions, decisions, and data sources can lead to biased data sets, skewed outcomes, inaccuracy, lack of credibility, and/or systemic prejudice.
10. Explore open data and the relationship between and importance of data ethics and data privacy.
11. Identify best practices for organizing and securing data.
12. Explore the scope and diversity of career opportunities in the field of data analytics through assignments such as informational interviews, job shadows, or other career-exploration activities.
Required Technology
More information on general computer and internet recommendations is available on the CCV computer recommendations Support page.
Please see CCV's Digital Equity Statement (pg. 45) to learn more about CCV's commitment to supporting all students access the technology they need to successfully finish their courses.
Required Textbooks and Resources
This course uses one or more textbooks/books/simulations, along with free Open Educational Resources (OER) and/or library materials.
Spring 2025 textbook/book details will be available on 2024-12-06. On that date a link will be available below that will take you to eCampus, CCV's bookstore. The information provided there will be specific to this class. Please see this page for more information regarding the purchase of textbooks/books.
CIS-1280-VO01
Link to Textbooks for this course in eCampus.
For Open Educational Resources (OER) and/or library materials details,
see the Canvas Site for this class.
The last day to use a Financial Aid Advance to purchase textbooks/books is the 3rd Tuesday of the semester. See your financial aid counselor at your academic center if you have any questions.
Artificial Intelligence(AI) Policy Statement
CCV recognizes that artificial intelligence (AI) and generative AI tools are widely available and becoming embedded in many online writing and creative applications.
Integrated: This course's generative AI policy acknowledges the use of AI is an essential skill in today's world. By using genAI for specific purposes, students become equipped with relevant skills and tools necessary to thrive in a technology-driven society. Emphasizing the mastery of generative AI should empower you to harness its potential, enhancing your problem-solving abilities and preparing you for future challenges and opportunities. Be aware, however, that any time generative AI is used at any point in the assignment without attribution it may be considered a violation of CCV's Academic Integrity Policy.
Methods
- Hands-on Practice: Provide students with sample data sets and guide them through creating spreadsheets, formatting cells, entering formulas, and using various functions. Encourage them to follow along and practice as you demonstrate.
- Real-world Examples: Use practical examples from different industries or scenarios to illustrate how spreadsheets are used in the real world. This helps students understand the relevance and applicability of the skills they are learning.
- Step-by-Step Tutorials: Break down complex tasks into smaller, manageable steps and provide detailed tutorials or worksheets for students to follow. This approach ensures that no one gets left behind and everyone can work at their own pace.
- Collaborative Learning: Encourage students to work in pairs or small groups on spreadsheet projects or assignments. This fosters peer learning, teamwork, and the ability to troubleshoot problems together.
- Visual Aids: Use screencasts, video tutorials, or live demonstrations to visually explain concepts and techniques. Visual aids can help reinforce understanding and provide a reference for students to review later.
- Quizzes and Lab Assessments: Incorporate quizzes, practice exercises, or mini-projects to assess students' understanding and provide feedback on their progress.
Evaluation Criteria
Grade Weights & Categories
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Discussions
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10%
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Assignments
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25%
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Labs
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25%
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Final Projects
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25%
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Quizes
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10%
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| Mid-Term |
5% |
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Total
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100%
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Grading Criteria
CCV Letter Grades as outlined in the Evaluation System Policy are assigned according to the following chart:
| | High | Low |
| A+ | 100 | 98 |
| A | Less than 98 | 93 |
| A- | Less than 93 | 90 |
| B+ | Less than 90 | 88 |
| B | Less than 88 | 83 |
| B- | Less than 83 | 80 |
| C+ | Less than 80 | 78 |
| C | Less than 78 | 73 |
| C- | Less than 73 | 70 |
| D+ | Less than 70 | 68 |
| D | Less than 68 | 63 |
| D- | Less than 63 | 60 |
| F | Less than 60 | |
| P | 100 | 60 |
| NP | Less than 60 | 0 |
Weekly Schedule
| Week/Module | Topic | | Readings | | Assignments |
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1 | Introduction to Data Analytics
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2 | Building Analytics Skills
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https://youtu.be/txNvZ3Zndak?si=o_BCf8zRm5vbymD7
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- Quiz
- Weekly Coursera Challenge
- Excel Like a Boss Pivot Charts & Data Imports
- Data Science Salaries assignment
- Final Project Topic Selection – Literature Review assignment
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3 | Data Wrangling Tools & Techniques
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- Quiz
- Lab Pre-Work: Device configurations machine
- Create and install accounts (Google & Github)
- Lab Assignment: Utilize Excel to find key statistics on a variable from InterstellarTravel dataset and create a graph
- Discussion Post: Share your graph with the class histogram and discuss role of spreadsheets in your analytics process
- Extra Credit Lab Assignment: Utilize Colab to run Pandas code on InterstellarTravel sample data to find key measures (mean, mode, etc.) & descriptive statistics
- Weekly Coursera Challenge
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4 | The Data Analysis Life Cycle
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- Quiz
- Weekly Coursera Challenge
- Lad Assignment: Draft your Data Life Cycle plan and submit for comment
- Discussion Post: Share your draft Data Life Cycle plan for comment
- Final Project Selection – Submit project proposal
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5 | Data Collection Essentials
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- Coursera Course 3: Prepare Data For Exploration
- Crash Course # 5 : Charts are Like Pasta Data Visualization Part 1
- Census Data API Discovery
- 2020 DHC: Using the Census Data API to Get Individual Data points
- 2020 DHC: Using the Census Data API to Get a Table
- Data Equity Concepts
- Responsible Data Practices
- Extra credit: Guide to Web Scraping with Python – All You Need to Know
https://youtu.be/QhD015WUMxE?si=c6VRyNQ4YIsUX9eq
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- Lab Assignment – Source Census Data tables and generate Map using Census Data Mapping tools
- Discussion Post – Share your map & some statistics you found interesting on the Geography you filtered to
- Extra Credit Applied Data Collection Lab: Querying the Census API - 2020 Decennial Census Demographic and Housing Characteristics
- Identify a geography level and table of choice
- Query API using HTML
- Describe the data being pulled
- Reflect on issues of data accuracy and how recent changes to survey questions impact data equity and ethics
- Submit a 200-250 word response that answers the following questions: 1. What data you sampled from the census and what you learned when you visualized it. 2. How does the Census bureau address ethical considerations in the data collection and sampling?
- Weekly Coursera Challenge
- Extra Credit Web Scraping submission
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6 | Data Driven Decision Making
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Polzer_CaseStudyShouldAnAlgorithmTellYouWhoToPromote_2018.pdf
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- Quiz
- Weekly Coursera Challenge
- Harvard Business Case Study on data driven decision making.
- Discussion Post reflection on summarizing findings from HBC assignment and how organization implemented recommendations
- Final Project Folder Collection No. 1 – Literature Review
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7 | Spreadsjeets for Data Analysis
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- Coursera Course 3: Prepare Data for Exploration
- The Shape of Data: Distributions: Crash Course Statistics #7
- Correlation Doesn’t Equal Causation: Crash Course Statistics #8
- Wienclaw, Ruth A. 2021. “Descriptive Statistics (Sociology).” Salem Press Encyclopedia, April.
- Quizlet Flash Cards on Descriptive Statistics:
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- Midterm Exam
- Coursera Weekly Challenge
- Lab Assignment:
- Excel Formulas and Functions – Workbook Submission
- Discussion Post
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8 | Spreadsheets for Data Analysis Continued
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- Coursera Course 3: Prepare Data for Exploration
- What is SQL? [in 4 minutes for beginners
- Roadmap for Learning SQL
- Data Analyst Explains When to Use VLOOKUP vs XLOOKUP vs. Index
- VLOOKUP
- Recorded Demonstration – Connecting to remote databases and querying data
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- Lab Assignment:
- Advanced formulas and data manipulation
- SQL Query structures
- Quiz
- Coursera Weekly Challenge
- Final Project Portfolio Collection No. 2
- Conduct exploratory data analysis using a spreadsheet or data visualization program
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9 | Addressing Bias in Big Data and How Machines Learn
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- Quiz
- Coursera Weekly Challenge
- Lab Assignment – Critical Review of Individual Project Dataset
- Discussion Post: Share a summary of your critical data review with your peers for feedback
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10 | Open Data & Data Ethics
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- Coursera Course 4 – Process Data from Dirty to Clean
- Video – Algorithmic Bias and Fairness: Crash Course AI #18 https://youtu.be/gV0_raKR2UQ?si=XKz2o-no_oDMJ-ie
- Floridi, Luciano, and Mariarosaria Taddeo. “Introduction: What Is Data Ethics?” Philosophical Transactions: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2083, 2016, pp. 1–5. JSTOR, http://www.jstor.org/stable/26115816. Accessed 30 May 2024.
- Video – How Biased Are Our Algorithms? | Safiya Umoja Noble | TEDxUIUC
- Creative Commons Attributions
- The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation: https://research.google/blog/the-data-cards-playbook-a-toolkit-for-transparency-in-dataset-documentation/#:~:text=Data%20Cards%20are%20transparency%20artifacts%20that%20provide%20structured,may%20be%20used%20to%20train%20or%20evaluate%20models.
- Stokel-Walker, Chris. Study: Common Image Search Results are Overwhelmingly white, a new study finds: https://www.fastcompany.com/91131154/image-search-results-bias-research
- Vincent, James. Google ‘fixed’ its racist algorithm by removing gorillas from its image-labeling tech. The Verge. January 12th, 2018. https://www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognition-algorithm-ai
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- Quiz
- Coursera Weekly Challenge
- Lab Assignment – Identify best practices from case study reviews of corporate transparency reporting.
- Final Project Assignment - Create a Data Card for the data set you identified. Import the data to Colab
- Discussion Post – Share your DataCard for comment
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11 | Data Security & Data Privacy
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- Coursera Course #4 – Process Data from Dirty to Clean
- Video – Secrets in Your Data | Full Documentary | NOVA | PBS
- Video – GDPR Explained: How the New Data Protection Act Could Change Your Life
- Reading - Lisowski, Jena N. “California Data Privacy Law and Automated Decision-Making.” Journal of Corporation Law, vol. 49, no. 3, Mar. 2024, pp. 701–26. EBSCOhost, research.ebsco.com/linkprocessor/plink?id=9abc2d41-8af0-3832-8752-a37729758800
- Video – Data Security: Protect Your Critical Data (or Else)
- Video – Cloud Storage in a Minute
- Google Cloud Storage: Managing Billions of Objects
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- Quiz
- Coursera Weekly Challenge
- Lab Assignment: Data Life Cycle of Final Project Datasets
- Final Project Portfolio Collection Point No. 3
- Milestone: submit revisions to the EDA including updates with recent lab artifacts
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12 | Career Exploration in Data Analytics
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- Coursera Course 4: Process Data from Dirty to Clean
- Tableau Video Resources (20mins total): https://public.tableau.com/app/learn/how-to-videos
- Get Started (0:22)
- Connect to Data (2:06)
- Navigate the Workspace Area (2:17)
- Map: Profit Ratio by Geography (2:22)
- Area Charts: Sales by Category and Sales by Segment (5:05)
- Text Table: Key Performance Indicators (2:23)
- Bar Chart: Profit Ratio by City (2:06)
- Build a Dashboard (3:39)
- Sample Data: https://public.tableau.com/app/learn/sample-data
- Atlas of Sustainable Development Goals: https://datatopics.worldbank.org/sdgatlas?lang=en
- Informational Interviews (Coffee Chats) | Everything You Need to Know!
- An Introvert’s Guide to Networking | Rick Turoczy | TEDxPortland
- Questions to Ask at the End of an Interview
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- Quiz
- Coursera Weekly Challenge
- Career Exploration Assignment – Investigate and draft informational interview requests
- Lab Assignment: Create a Tableau Dashboard visualizing World Development Indicators
- Discussion: Share your Dashboard with the class. You discussion post should include a PNG picture and link to the live Dashboard. Your post should include key information on the indicator(s) chosen and your process for mapping
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13 | Hands on Data Analysis with SQL
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- Coursera Course #4: Process Data from Dirty to Clean
- Lecture – End-to-End Database creation, Data ETL, and Query
- Learn SQL Basics in Just 15 Minutes | @KenjiExplains
- How I use SQL as a Data Analyst
- SQL Joins Explained |:| Joins in SQL |:| SQL Tutorial
- 6 SQL Joins you MUST know! (Animated + Practice)
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- Quiz
- Coursera Weekly Challenge
- Challenge Lab Assignment: Query relational tables using Online SQL Editor to complete assignment.
- Final Project/Discussion Post: Draft presentation for peer feedback
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14 | Beyond the Fundamentals
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- Coursera Course #5: Analyze Data to Answer Questions
- Five Key Trends in AI and Data Science for 2024
- The Tope Data Analytics Trends That Will Dominate 2024
- How I’d Learn to be a Data Analyst in 2024
- Data Scientist vs. Data Analyst vs. Data Engineer: What’s the difference?
- The Essential Tools for Data Science:
- What Every Data Scientist should know about Python Dependencies
- The Definitive Data Scientist Environment Setup
- Set-Up Guide for Mac Users:
- How to Setup Your Macbook for Data Science in 2024
- The Ultimate Data Science Setup for Mac (2022 update)
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- Written Assessment: Contrast debates and challenges in faced data analyst, highlight the dimensions of data ethics and equity as they relate to broader trends and conversations within the field
- Quiz
- Weekly Challenge
- Discussion Post – This week’s discussion post requires that you watch two of your classmates presentations and provide feedback and comments on their data analysis project to date.
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15 | Capstone Project
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- Coursera Course #5: Analyze Data to Answer Questions
- Everything About Capstone Projects: An In-Depth Guide
- Google Data Analytics Certificate Course 8 of 8 – Capstone Project + Full Impression
- Top 8 Mistakes Beginner Data Analyst’s Make | CareerFoundry Webinar
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- Discussion Post – What I learned (or didn’t) in this class.
- Final Project Due – Final Project folder containing all project files, recorded dashboard demonstration and entire codebase
- Post-Assessment
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Attendance Policy
Regular attendance and participation in classes are essential for success in and are completion requirements for courses at CCV. A student's failure to meet attendance requirements as specified in course descriptions will normally result in a non-satisfactory grade.
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In general, missing more than 20% of a course due to absences, lateness or early departures may jeopardize a student's ability to earn a satisfactory final grade.
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Attending an on-ground or synchronous course means a student appeared in the live classroom for at least a meaningful portion of a given class meeting. Attending an online course means a student posted a discussion forum response, completed a quiz or attempted some other academically required activity. Simply viewing a course item or module does not count as attendance.
- Meeting the minimum attendance requirement for a course does not mean a student has satisfied the academic requirements for participation, which require students to go above and beyond simply attending a portion of the class. Faculty members will individually determine what constitutes participation in each course they teach and explain in their course descriptions how participation factors into a student's final grade.
Participation Expectations
Participation in discussion forums is an integral part of building a community for this remote class. There are multiple discussion post assignments throughout the semester.
For discussion post assignments student's initial submission must be received the due date (usually Saturday by 11:59 pm) and your response post to peers must be recieved by the following Monday at 11:59 pm). Response post to your classmates must substantive and constructive to recieve full credit.
Zero tolerance policy for bigoted, racist, msogynist or xneophobic statements in this class. Failure to engage respectfully with your peers will affect your final grade and ability to participate in this class.
Missing & Late Work Policy
Late work will recieve an automatic deduction of 5 points for each day late. Missing work will receive a zero. If you are having trouble keeping up with your work please reach out early.
Accessibility Services for Students with Disabilities:
CCV strives to mitigate barriers to course access for students with documented disabilities. To request accommodations, please
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Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/discover-resources/students-with-disabilities/
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Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.
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Once created, students will share the accommodation plan with faculty. Please note, faculty cannot make disability accommodations outside of this process.
Academic Integrity
CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.
Apply Now for this semester. Register for this semester: November 4, 2024 - January 17, 2025
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