To require 'Data Literacy' for every university student, what does the syllabus include?

[LDuane]

With Data Literacy as the foundation of every great analytics and data science skill set, imagine when universities require data literacy as a core program for every university student, regardless of major!  Everyone becomes aware of data.  No one is afraid of data.  The value of data is realized across every company in every industry across the global!  It is our responsibility to make this happen.

What do you want the syllabus for 'Data Literacy' at every university to include?  I know @alexandramannerings along with Julie Burroughs and Radhika Nath have some ideas. 

Thanks for sharing!

Libby 

[LeahK]

What a great question Libby!

For a bit of context, last week at a Women in Data Science meet-up, a similar question was posed to the panelists. It was great to hear the different perspectives and I'd personally love to hear what our WoA Community thinks.

Tagging in a few ACEs to get the conversation going! @estherb47, @Deanna, @HeatherMHarris, @VizChic, @Nezrin 

What would your syllabus look like?

[alexandramannerings]

Do I ever have some ideas 🙂 Just off the top of my head:

1. A base understanding of probability and percentages, including:
   1. Understanding key measures of risk probabilities, like the difference between an increase in relative risk vs. absolute risk or number needed to treat vs number needed to harm.
   2. Conditional probabilities. According to a study by David Eddy, only 5 of 100 doctors surveyed understand conditional probabilities, which is terrifying since they play a huge role in determining what you should do based on a test result. If the doctors don't get it, we better learn it!
   3. Simpson's paradox and the effect of aggregation/disaggregation on proportions.
2. A basic understanding of what makes a data set or data-driven finding good or bad, such as:
   1. sampling bias or survivor bias
   2. confounding variables
   3. proxy measures vs actual outcome
   4. what it takes to actually prove "causation"
   5. repeatability
   6. study vs. experiment
3. A review of pertinent examples of what happens when you draw conclusions based on bad data, like:
   1. AI/ML issues with facial recognition and racial bias
   2. Bad or ineffective medical practices
4. Common abuses of data, like:
   1. cherry picking
   2. post hoc data mining, esp. without corrective statistics (i.e. if you ask more than 20 questions of your data, at p<0.05 it's very likely to get "significant" finding!)
   3. non-random or inconsistent elimination of outliers, or bad error "correction" methodologies
5. A review of our cognitive "quirks" around interpreting and understand data that make us think faster than the best machine but can mislead us, including:
   1. confirmation bias
   2. misunderstanding correlation vs. causation
   3. what "random" actually looks like
6. Asking good questions of data, and understanding the vital importance of values, business needs, etc. to drive the data process
7. How important it is to track results with pre-defined measures of success. 
8. Gary Smith's Standard Deviations and How to Lie with Statistics by Darrell Huff would be required reading! 

This would definitely have to be a full semester's class 🙂 

[stefwork]

I love this idea as well as would love to hear ideas on what would be included on the syllabus-- I could teach a billion classes on "how to read a chart" or "how to analyse data" but feel that "data literacy" is such a relevant topic that would help so many people from understanding their job to enhancing business and even applicable to personal finances and retirement

[damc]

Good question! Here are some aspects of data analytics that I believe this  program should cover: ethics, privacy and best practices in visualization.

[Nezrin]

Few points that I always tend to cover when I interact with students - that Data Literacy is universal and will be crucial irrespective of their specializations in their university or what line of job they will be in.

Some topics that I did cover during my masters in BI, which I feel have been very handy at work and even during basic data conversations:

Data Structures, Data Integrity, Database concepts

Studying one or two different tools will definitely give an experience on how dealing logically with data is a transferable skill. 

Also focusing on what an analytics career path looks like - will be really insightful.

I so wish I had Alteryx on my curriculum - life would have been easier a long time back itself!

Totally appreciate all data initiatives as part of university program - a great contribution we can do for our future generations 🙂

[tessaenns]

@alexandramannerings sign me up for that class! It sounds like you have thought about this before! Amazing response! Especially the psychology element to bring out the deeper thinking or dare I say... the 'analytics' of 'analytics'?

With teaching, you get to discover what energizes people and pull on those strings. For instance, I am energized by optimizing resources which requires data fluency and accuracy to make the best allocation decisions.

Others are energized by having clarity, and data is needed to gain insights in to the past, present and future.

Others are energized by developing people for success, and data is vital to track progress.

Others are energized by not making the same mistake twice, and data can help pin point where those "critical mistakes" occurred.

The list could go on and on, but the point is no matter what it is that you are trying to solve, data plays a key role. And with Alteryx, there is assurance that you can slice and dice and clean and hypothesis and test data to inform decision making without having to choose, "data person" or "supply chain professional", or "data person" or "doctor", or "data person" or "educator", or "data person" or "business owner", instead data skills can be a common denominator in everyone's paths. I think that is the direction we are headed and I love this conversation! (I am already trying to introduce my 7 year old to Alteryx because I know data and statistics will be a much more common and intuitive language in her future)

Key Take-a-way: I agree with @alexandramannerings's point to include asking the right business questions in the curriculum and to add to that, I think the syllabus could include a piece on asking what energizes each student and then ask, how might data be used to kick butt in pursuing those passionate goals? Perhaps the answer to this question could even fuel a 'capstone' project. Because when you boil data usage down to its nuts and bolts, it requires the human element just as much as the machine element.

[Radicalrad]

Such great responses. Currently, we are faced with a data culture that monetizes our information to sell it to others or to use our data to sell things to us. Ethics are truly important as one respondent here said. We need to embrace a value system that doesn't just think about data as a product, that teaches us the dark uses of data and the missteps that have come to light more recently- such as the manipulation by Cambridge Analytica. I said this during my talk and I say it again- Data should allow us to gain deeper insights and understanding of the world we live in and the problems we face. Those insights should help us to create a better system, process or product. That should underlie data literacy and be the first and last module of every data science course. 

[terry10]

Libby,

This is a great question! Here's what I put together for my company, but I think it is applicable to everyone - University students, K-12 students, really every member of society should have understand the fundamentals of how to understand data. I gave a "Critical Thinking" lesson to my son's 5th grade class and they really took to it. I'll watch this thread for new ideas. 🙂   - Terry

[LDuane]

@terry10 

Thanks for sharing your experience.  I love the fact that you have 5th graders engaging in these lessons.  We know that anyone can learn more about data and analytics when they start developing those skills early in their learning journey.

Speaking of learning journeys, we all have learned a lot over the past 3 months or so around CoVID-19 with our new working and learning environments.  I wanted to share more about our ADAPT program, free for any person who was displace/furloughed and now has an interest in learning more about data analytics.  Check out ADAPT:  https://www.alteryx.com/why-alteryx/alteryx-for-good/adapt-program and let me know what you think.

Best,

Libby