This repository is where I am documenting my progress through the CERT x465-003 course. This README.md contains the course information as well as detailing the basic file structure and where my submissions for each assignment can be found.
CERT-x465-003_MAIN.ipynb: This jupyter notebook is the main file I will be using for the course. I will put my assignment submissions here, as well as the main code for data analysis, visualization, and and transformation.data/: This directory contains the datasets I will be using to complete the assignments.notebook_images/: This directory contains images I will use either in this markdown file or the jupyter notebook listed above.src/: This directory is where I will store helper files and notebooks used to assist in dataset gathering, analysis, visualizations, and whatever else I cook-up
In our data-filled world, it is important to be able to effectively interpret data and apply it to real-world problems. This six-week course will equip you with the skills to examine and understand data analyses and visualizations, and translate this into actionable insights that drive decision-making.
What You'll Learn:
- Identifying the Right Data: Master the art of pinpointing the data you need to solve complex problems or answer critical questions within your organization. Learn how to formulate effective research questions and translate them into actionable data requests.
- Data Quality Assessment: Develop the critical skills to evaluate data for accuracy, completeness, and consistency. You'll learn to identify potential limitations and biases in data sets, ensuring your analysis is built on a solid foundation.
- Data-Diverse Viewpoints: Understand the importance of considering diverse perspectives when interpreting data. Learn strategies to mitigate bias and ensure your analysis reflects a well-rounded understanding of the information.
- Data-Driven Recommendations: Refine your ability to translate data insights into clear, actionable recommendations for organizational decision-making. You'll learn to communicate your findings in a way that is compelling and easy to understand for stakeholders.
- Business Intelligence (BI) Platforms: Learn about BI platforms commonly used to analyze and visualize data. This course will give you examples of how these tools are leveraged to communicate complex information to a broader audience.
- Collaboration with Experts: Develop strategies for collaborating effectively with IT and data professionals to access advanced analyses and visualizations. Learn to articulate your needs clearly and collaborate to maximize the value of data for your organization.
Course Learning Objectives:
- Determine the data that is needed to provide actionable insights for an organizational problem or question
- Evaluate data quality and limitations
- Assess data analyses using principles of quantitative and qualitative research
- Include diverse perspectives to improve data interpretation
- Develop data-informed recommendations for organizational decisions
- Identify ways that BI platforms may be used to effectively communicate data
- Collaborate with IT and data professionals to get more technical analysis and visualizations
- Sometimes you’ll start with a question and then need to find data to answer it. Other times, you will have data and need to understand how to use it to make decisions.
- Regardless of the starting point, it’s important to be really clear what you’re setting out to answer.
- In this module, we will learn how to identify and clearly frame problems and questions that can be addressed through quantitative data.
- You will think about the downstream value of the data investigation and the uses it can have for different audiences and decision-makers
- Question identification: Think of a topic area that interests you (i.e., something you do for work, a hobby, something you enjoy). Now think about a problem or question about this topic that can be answered using data.
- We will learn how to identify potential data sources to help answer a question or problem of interest.
- There are a lot of places to look for data. Some might contain raw data, and some might have data that’s already been tabulated or analyzed.
- In addition to locating data, we will discuss how to evaluate data quality and how to vet data sources to interpret what they can and cannot tell you.
- Data source identification and assessment: Identify at least one data source that can help answer the question you developed in Module 1.
- First, brainstorm a bit on your own. Then, try using AI to identify data sources.
- In this module, we will focus on how to find key points and draw conclusions from data - the translation of information into knowledge.
- We will also learn how diverse perspectives can enrich and add nuance to data interpretation.
- Data interpretation video: Using the data source(s) that you identified, produce a 5-minute video describing what you are seeing in the data and what conclusions the data is pointing towards. Make sure you tie it back to the original question you set out to address.
- Data is sometimes presented in tables or as raw numbers, but more commonly, it is displayed as visuals.
- We will learn how to interpret what a data visualization is trying to convey, and how well it does at conveying that information.
- We will draw on principles of data visualization best practices to determine how to construct an effective visual that can be interpreted easily by different audiences.
- Data visualization analysis: Find two data visualizations online - one that you think is a good, effective visual and one that you think is less effective. Note that these do not need to be about the same topic your other assignments have focused on.
- You’ve started to glean insights from your data in Module 3. Now, we’re going to move from knowledge to wisdom by formulating strong data-driven recommendations or takeaways. We will learn about how to present these takeaways to different audiences and will apply this by drafting a brief report and oral presentation of your recommendations that are tied to your original problem or question.
- Report for stakeholders:
- Part 1: You’ve developed your question, found your data, and interpreted it. Now, we’re going to put it all together. Write a brief report to a clearly-specified audience that contains at least three recommendations or takeaways that are justified by data.
- Part 2: Next, record a 5-min video where you present your recommendations in a mock professional setting. This is your “elevator speech” version of your key takeaways. Provide feedback on two of your peers’ videos.
- Business intelligence is a combination of tools and methods that allow businesses to harness data for decision making. -We will learn how business intelligence might be used in an organization and what players, including IT, might be involved. We will also focus on a commonly used tool in business intelligence: dashboards, or tools that allow people to interact with a large amount of data or track changes in real time.
- Reflection: Discussion board reflecting on what you learned and what you are going to take away from this class.
- You will receive a grade of incomplete or complete based on your demonstration of course learning objectives. Learning objectives are demonstrated by completing assessed projects.
- The instructor will assess each learning outcome as “not demonstrated,” or “demonstrated.” Completion of a course means that you have demonstrated achievement of all learning outcomes. Continuing Education Units (CEUs) and the certificate of completion for the course will be awarded only if you receive a grade of ‘complete’ for the course.
- While participation in Zoom sessions and engagement with other non-assessed learning activities do not count towards the final grade, these activities are designed to be an integral part of achieving the course learning outcomes.
- If you receive a grade of ‘incomplete’ after turning in an assessed project, you will have the opportunity to resubmit your project once.
- Please note, receiving a grade of “Incomplete” and/or failure to demonstrate a learning outcome will not result in a refund.
- To officially complete the course, you must complete all 6 modules and submit your acknowledgement of course completion in the final module. This will ensure your work will be graded.
- Once you receive confirmation you have successfully met the learning objectives for your project, you will receive your certificate of completion. This typically happens one week after the course has ended to allow time for grading.
- You will receive an email inviting you to download your certificate and you can view/print your certificate of completion.
- Along with the course completion certificate, students will be awarded 3.0 Continuing Education Units (CEUs). Students will not receive University credit for completing the course as this is a non-credit course.
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Write a short essay that address the following questions:
- What problem or question are you trying to address?
- Why are you interested in addressing this?
- Why is this an important question or problem to address (in general)?
- Who might your audience(s) be for this investigation?
- How will the results of this investigation help your audience make decisions or draw conclusions?
- Identify at least one data source that can help answer the question you developed in Module 1.
- First, do some research on your own to try and find a data source(s).
- You might start with a simple Google search, you may ask someone who has knowledge in the area, or you might already know of some candidates.
- For example, if your question pertains to K-12 education in Minnesota, you might check out the Department of Education's data sources.
- After you have finished your search and identified good data source(s), please write a short essay that addresses to the following questions:
- Describe the data source(s) that you found.
- How is the data relevant to your question?
- Where does the data come from?
- How reliable is the source? Are there any signs of bias?
- Using the data source(s) that you identified, produce a 5-minute video describing what you are seeing in the data and what conclusions the data is pointing towards.
- Tie findings back to the original question.
- Discuss how groups with different perspectives might interpret the data differently.
- Post the video and provide feedback on at least two peers' videos.
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Find two data visualizations online
- one that you think is a good, effective visual
- one that you think is less effective.
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Note that these do not need to be about the same topic your other assignments have focused on.
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Write a reflection paper about the data visualizations that answer the following questions:
- What are the key points that each visualization is trying to make?
- What makes the good visualization good?
- What makes the bad visualization bad? How would you improve it to make it easier to interpret?
- Part 1: Report for Stakeholders: Write a brief report containing at least three recommendations justified by data.
- Part 2: Presentation: Record a 5-minute "elevator speech" presenting your recommendations.
- Provide feedback on two peers' videos.
- Submit a final reflection on the learning outcomes and takeaways from the course.
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