Introduction

Welcome independent, adaptable thinkers and doers who will make an impact on the world.

Your Quantitative Reasoning Foundation module will have grown your capacity for independent thought, helping identify potential blind spots in your thinking by aligning your personal perception to a wider empirical reality on some topic of interest. The next step is to take your ability to reason quantitatively on one topic, expand the scope to many, and further develop your ability to share data driven insights to a broader audience (more than just your prof and classmates). We will accomplish this through practice – the hands on process of collecting, curating, analyzing, and visualizing data in this inquiry tier module, Developing Meaningful Indicators. It is my hope that by focusing on creating meaningful information from data, we will help grow your capacity not to just be an independent thinker but also an independent doer.

Workload

This does not mean that we will have to do a lot. The quality of the work matters more than the quantity. Here are the meaningful indicators for this class: ~6 charts ~6 written pages of work ~1 presentation/tutorial in 13 weeks. That’s not much, it has to be harder than that right? I’m asking you to add value independently of the workload, through your unique ability to create something new from basic numbers and text – a true 0 to 1. You may never have been asked to do such things in your past educational experience, you may never have had to rely on your curiosity, creativity, ingenuity, and own interests rather than your smarts, dedication, and discipline; so it may very well be the hardest thing you have done scholastically up until this point. My advice: rely on me, rely on each other, ask questions.

Adding Value

First question: how do I become a doer when working with data? You ask yourself a question: how do you add value? Without your added value, data is just numbers on a page. Adding value to data is easy and requires no technical ability. Can you Google new data? You can add value. Can you be critical of the numbers that a Google search turns up? You can add value. Can you ask unique questions borne from your own curiosity or from the perspective your major faculty of study provides? You can add value. Can you help someone learn through sharing your (existing or new found) abilities through collaboration? You can add value. Can you develop something meaningful to yourself and others that generates interest and understanding? You are a scholar, and you have added tremendous value to the world. See the add value spreadsheet for a handy guide and a way of keeping track of your value.   

Novel Insights

First look for interesting data. Most of the data we need can be accessed online and therefore the work we will need to do to collect it is minimal. But for that very reason, because online data is plentiful and accessible, collecting unique and interesting datasets is difficult. If we can’t find unique data it becomes harder to pry novel insights from more well-known datasets from places like the UN or the World Bank it when it has been poured over by thousands of eyes. One way you can add value to this class is by collecting unique data sets. Each week I will provide one data set for the class. If you are engaged in the topic you can find more data, and much more interesting and diverse data than I could ever provide by myself alone. Academics know this, unique and interesting data sets are a gold mine in research, because anything we ask of them adds to the body of knowledge. 

The second way you can add value is through your own critical thinking. Before you begin to analyze data several key issues must be addressed: where did that data originate (metadata), what does it represent, is it valid and reliable, and is it complete and error free? All these need to be known prior to any work being done with the data. Moreover, keep in mind the more unique the data is, higher the potential for you to add value, as the metadata is likely not well understood and data itself may not be error free. This step in the value added process is called cleaning and together with collecting (step 1) and analyzing and visualizing (step 3), it roughly translates into what we call data curation.

The final step in data curation is to analyze and visualize the data in a way that makes sense to people, to make meaning out of the data. This often requires the development of an indicator, one key piece of information that tells us something about the baseline or progress of the thing we are trying to measure. In this class we will utilize a chart or graph that generates the interest of a broad audience, a data visualization. The world is filled with people of different perspectives, knowledge and abilities, and we are flooded constantly with things vying for our limited attention. Therefore it will be hard to make a chart that get’s noticed and generates discussion. Experiencing the difficulty of making such a real world impact, one comment on a chart from a broad audience, should help you better understand just how hard it is to make an impact in the world at all. I think that you will find over the course of this class that your impact on the world comes not from your technical ability to process and analyze the data, but your own unique ability to add value through your unique contributions in thought and action.

Class Structure

Each week we will tackle a new subject. These subjects will be tied to things that are hard to measure, either due to the inherent difficulty of operationalizing the concept (happiness, luck, etc.) or the complexity of the topic (environment, sustenance, population). Once a week I will provide a short lecture on the background information you need to get started, including some of the ways that the topic has been measured in the past, my (and subsequently) your thoughts on perhaps the best approach to measuring such complexities, and importantly where to find data (class 1 of the week).

We will keep this discussion going offline. This discussion will be based on your choice of readings for the topic. Your Slack channel and the various channels created around your readings and datasets, allowing us to share research and data as well as feedback and rough analysis and visualizations. Once a week in a subsequent lecture we will discuss our progress, collaborate with each other in person, and attempt to finalize our submission to our public forum in class (class 2 of the week).

Finally, after you have submitted your post online, it is your job to collect and respond to feedback, make any necessary improvements to your data visualization and conduct supplemental research based on new insights. This final portion is to be done in consultation with me and the final product and record of the process is captured your a blog post.

We will repeat this process for several weeks. The topics (barring an introductory topic and knowing our audience: Redditers), are subject to change and will be chosen together influenced by class interest or current events. This is a key word together. I look forward to working with you all! Good luck!

Syllabus

Workload

Curious – Undirected search/discovery; driven by genuine interest in the topic

Assessed by:

1. Adding novel data through collection, sample, and/or subtopic focus
2. Adding novel perspectives to existing data through categorization or analysis
3. Asking questions of the topic

Primary Continuous Assessment –Plan (Pre-processing) 30%

Critical – Of the data, method of analysis, or visualization choice

Assessed by:

1. Data cleaning and linking
2. Asking questions about the data (triangulating validity and reliability, within or with additional data and/or research)
3. Asking questions of the data (statistical analysis)
4. Appropriate choice of visual (chart type/aesthetics)

Primary Continuous Assessment – Report (Data visual product) 30%

Engaged – Directed search/feedback; driven by genuine interest in the community

Assessed by:

1. Tutorials
2. Medium post detailing the overall process (from question to visualization to additional questions)
3. Additional research into the topic to address unanswered or additional questions
4. Offering quality feedback on Slack channels
5. Consultations

Primary Continuous Assessment – Tutorial (instruction and feedback) 20%

Courageous – making your work public, meaningful; reflecting on your response to success/failure

Assessed by:

1. Creating meaningful information (public feedback from votes, comments)
2. Publically recognizing ways to improve (medium post)
3. Incorporating feedback/tutorials into future projects

Primary Continuous Assessment – Online Participation 25%

Suggested Reading List

1. Enlightenment Now by Stephen Pinker 
2. Storytelling with Data by Cole Nussbaumer Knaflic