 Well, I'm gonna try to be a punctual person for once and We will get this thing started and I get to introduce myself, but two things Before we get started first off I'm gonna imagine that some of you have taken a class from me before or taking the class right now Are any of you enrolled in my class right now? Just one I have 430 students in my class right now one of them showed up you get an a Any of you take my class previously? Oh I'm so flattered. Thank you So I'm gonna introduce myself a little more thoroughly in a moment here, but most I want to explain to you what this Series is what the speaker series is. It's the degrees to anywhere Am I supposed to explain why we have that particular phrase? It's because one of our crappy state senators called a degree in the liberal arts a degree to know where um And so we're trying to say which was funny because he had one And anyhow But the idea degrees to anywhere. Yeah, it's true that if you're in the humanities the social sciences the liberal arts very often That degree in and of itself will not give you a direct professional qualification for something But really the whole point of coming to college is not to get your professional credential I mean, that's what that's what a technical school is for. It's to become educated and When you get your degree in the humanities the social sciences the liberal arts What it does is it prepares you for a huge number of things and so The point of the speaker series is to learn from people who have a background in one of these fields And have gone on to something that maybe you wouldn't have expected at first To see that it does prepare you for a lot of things now I know it's a little weird hearing that from a psychology professor, but I will explain why I'm Why I am here talking today So this is a Stephen, this is a monthly event, correct? Three times each semester. Okay So If nothing else it's free lunch three times a semester. So just remember this is where all the food is All right, it's like my daughter all three of my kids are students here and But I have one daughter who has said like they give out free fresh Fruit and vegetables on Fridays. All right, so she's like goes and stalks up and she knows where all the free food is I mean, she's not a starving student. We support her But there's a lot of free food to be had on campus and we've got a lot in this room So I will not be offended if you get up and go back and get some food while I'm talking Okay, also just a quick note this talk will is being recorded So you can share it with all of your friends and watch it again and again. I actually um I make videos and I had somebody who was from Pakistan tell me that She puts my data videos on repeat to help her child fall asleep I take it as a compliment um anyhow Very soothing very soothing um, you know I'm a psychology professor, but I teach statistics and um Some of the comments I've gotten on my videos are that I am either the mr. Rogers of data science or the bob ross of data science Very calm Anyhow, let me talk about this I'm going to talk about data work and liberal arts and my little secret clue here that this is a Data presentation is the double ampersand, which is the logical and in most programming languages So both of these things are true at the same time Let me start however by saying who is bart aside from me A few things number one. I have a phd in social psychology. I also have an mville and an ma and a bs In the field. I was in college for a long time um I've been a uv u full-time professor for over 20 years I've been teaching for about 28 years And uh, I have I have a one-class rotation. I teach psychology 31 10 statistics for the behavioral sciences Which I have now taught 116 times And the other 18 courses I've taught are 118 which means that in the spring I will have taught stats more than everything else put together um A little more is I also And this is why I'm here today why I'm a speaker here I also am a frequent author for linkedin learning now That's a service that all of you guys have free access to because there's a university site license And I make courses on them for statistics and data science. I've been doing this for about 13 years um And I talk about programming. I talk about how data science is used in healthcare and finance and sports and A lot of that stuff. So I do a lot of professional work there One of the interesting things about that is I also am very active on linkedin. I post lots of short videos on there My daughter got me doing just tiktok videos, you know holding up my phone and making short data videos and um including while I was riding my bike and didn't crash but um I also do twice a month. I do what are called live office hours on linkedin. I did one last night On various topics and I get an audience from around the world um And it is Really a fabulous way of connecting with people it gives me an opportunity. I also have a company called data lab dot cc where similar to the video courses I make for linkedin which are on Uh statistics and data science. I do the same thing at data lab, but with linkedin's blessing Um, I don't sell them. I don't make any money through data lab dot cc I give that stuff away for free But there's also a way that I connect with a huge number of people and has led to a lot of paid work for what I do also Um, there are courses there that well If you don't know how to use a spreadsheet because I am going to talk about spreadsheets You can learn how to use it there and they're the downloadable files. It's it's all there I also sometimes work as a data consultant. I have worked for Tiny nonprofits. I have worked for local business consultants. I have worked for Several global corporations and I've worked for the u.s federal government Um doing data for them two other things about me I actually studied design Um from the time I was a tiny little kid until I was a senior in college I was going to be a designer And I did an informal internship at general motors which served one of the important purposes of internships and tell you what you don't want to do And so I kind of ran away screaming after that switched over to psychology and still graduated in you know, five years total, um Design is still very important to me. I did have a drafting table and I did use a french curve Also dance That's unexpected, but my wife is here. She's a modern dance choreographer and several years ago. I took a sabbatical And I spent a year at the u of u to study Some of the elements of data visualization. It's an important part of data work And I had most of it already, but there were some things that I still needed But while I was there I was enrolled in the Certificate program in arts technology in the department of art and art history And I had to do a capstone project and I just got a microsoft connect I hooked it up to my computer and I had dancers do short improv's which we did uh motion capture On them and they turned that into this giant three projector gallery piece turned it into some printed pieces to show it another gallery And then that led to My wife and me getting commissioned by repertory dance theater one of the major modern dance companies in salt lake to produce a piece for them This is a still shot from that performance And then I got a presidential fellowship for faculty scholarship here at uvu And I spent two years working on live video looping and manipulation for modern dance performances on stage Fabulous work and it is because of my interest in data that I learned how to do those things um So But why am I talking about data work? And what does it have to do with you? Well a few things Number one when we talk about data You by the way data is the british pronunciation In america we say data Telling you that also data is technically a plural noun Because it's the it's the latin plural of datum But it is in english it's a mass noun like air or water and so you can say the data is Anyhow when we talk about data the first thing it comes up for a lot of people is statistical analysis And that's something I do a lot. It suggests number crunching. I do that. I know how to do it I teach people how to do some of it and it also just like random factoids Here are some statistics about you know this year's graduating class boom boom boom boom That's all useful. It's good, but there's much more to it than that Now there's a closely related field of data analysis And it's broader than statistics It includes visualization, which is a huge part of I mean just and truthfully just simple graphs bar charts line graphs um Are gonna get you maybe 90 of what you need And then it also includes the interpretation of the data which it turns out is the hard part And that's something I'm going to talk about a fair amount today And then if you want to go a step beyond that there's data science Now data science is a new field really Came into its own about 10 years ago And data science adds computer programming because it works with a lot of Data that doesn't fit into the rows and columns of a spreadsheet And it also has a heavy emphasis on an applied context. So in a business setting And so the people for instance who develop the what are called recommendation engines if you're on netflix or if you're on amazon It says you might like this That is a data scientist who created that Based on your browsing history to make these other recommendations now These days those people are more often referred to as machine learning engineers But it is still the same general field as data science But why am I talking to you about this? Well for a couple of reasons number one is You know you're in college, but you're gonna not be in college one of these days We hope and you're gonna be looking for a job. And one thing is there are a surprising number of people with degrees in the humanity social sciences and liberal arts who have ended up being full-time data specialists Degrees in data science are very recent I'd say only within the last five years is it possible to get a degree in data science Previous to that everybody came from somewhere else And often you would think it would be computer science or mathematics. It wasn't Some of the most influential people in data science came from political science and came from A shocking number came from astrophysics um But they came from many many different fields And it is possible to be a specialist in this now obviously if you're going to be a full-time Specialist in the field like this you're going to need some additional training need to go get a master's degree In the field But you may also end up working in a field that is adjacent to data. So you might become a researcher And so Data isn't your full-time job, but it is an important element of what you do So for instance me I I am not a full-time data scientist what I do however is a lot of work that connects with it And I can and also I I spend a lot of time talking with data scientists about things And so you may end up in an adjacent field and truthfully There are massively more opportunities in adjacent fields if you want to be the development director for a non-profit That's the person who raises money You're in spreadsheets all day every day if you want to be A human resources director for a company same thing if you want to manage a Uh an online social media business same thing Those are ones that you have to use the data in order to do the job well So I think of those as data adjacent fields You got to have it And then this last one MYO be mind your own business a lot of you Um if you're for instance Most of the students in my class Because my class is in psychology and it's mostly students from I'd say the single most common goal of my students is to become a therapist either a licensed clinical social worker or a Marriage and family therapist or a clinical mental health counselor You're going to be a therapist and for a lot of these people the moment you become a therapist you also become a small business owner And it turns out That smart people who have specific professional training in their field don't necessarily know how to run a business This is true of lawyers and dentists and doctors and accountants as well And so the ability to work with data Enough to take care of your own business is going to make a huge difference for you. I have made Important business decisions based off A single bar chart When I finally took the time to get some data together put it all together So like this activity pays really well. This one doesn't so we'll do more of this and less of that But the real reason I want to talk to you guys about this is because data is human data is a human activity Now I have an event. We call the data charrette. I'll say more about it later But this is our we had stickers for this and it says data is love and community Driven data for good Now there's a little bit of hyperbole in that it makes a cute sticker. But um But here's the deal Data is not something that is naturally occurring in the world Data doesn't exist on its own the same way that like the height or the density of a tree exists data Requires humans humans design the methods to find data to store it and to analyze it and it doesn't exist until that happens And it happens because data is used by humans to reach human goals. Okay, so ready Again data is not something that exists naturally in the world. It is created by humans for human tasks Even if it's machine I mean most of the data gathered these days is like in warehouses As a box goes by and it gets scanned automatically and it goes on but you know what yeah machines doing that But somebody created that machine. They created that technology. They created it to serve their purposes And so it is fundamentally a human activity And it gets us to the idea You know the little saying if a tree falls in the forest and there's no one there to hear it Does it make a sound well as a psychologist who can teach you the difference between sensation and perception? I will say you there is a correct answer to that question falling trees do not make sounds Falling trees make waves of air pressure But for that to become a sound you have to have a sense organ that is designed to receive it You have to have a transduction process that converts it into a neural impulse and you have to have a brain That is adapted to perceive and interpret sound So unless you have those three things around there is no sound. There's just variations in the air pressure The same thing is true with data. You can say if a tree falls in the forest and there's no one there to measure it Does it make data? No, it doesn't because data It it'll do stuff that could be interpreted as data But there has to be a process somebody has to be there to measure it one way or another I mean you could just go in to say like how many trees fell down So the trees are there, but it wasn't data until it got counted And so that's the important thing. It's not something that exists out there. It is the result of a human activity Now there is a need Data needs a story so it can take form and fulfill its purpose It is nothing until a human looks at it And interprets it Okay, it's nothing until it is perceived and interpreted And so the fulfillment of that need Is we need humans who are story makers and the humans who study stories do it best And I believe that people and the humanities the social sciences and liberal arts Are actually best situated to do that Not just people in my field psychology Or the broader social sciences anthropology and social work But people who write fiction People who create films People who study history All of these things are important. I actually have more books of poetry in my office than I have on statistics Um, and it's because I have gotten some of the best insights in my life into human nature from poetry Um or from movies or from I remember I have a I have a I have a poach. I have a poem on my door Um, I suddenly can't remember the poet's name But it's called um having it out with melancholy And it's about a person writing first person about their experience with um A bipolar that manifests itself mostly as a soul crushing depression And I think it is the best example. I know of How it feels and how people should try to understand it. I remember seeing an opera A few years ago called the long walk, which was about a soldier from the war in the mid-east suffering from um post-traumatic stress disorder I felt that opera was the best illustration of the principle I had ever seen and so People who know how to tell stories people who know how to take information and find the story in it Are truly the ones who are in the best situation to make sense of data And to fulfill its potential um, I'm gonna have a little brief interlude here on this to talk about um dumpster fires Or really horrible research that I have seen and I had to be selective Okay, because I've seen a lot of really crappy research Let's talk about dumpster fire number one the ai gaydar So a few years ago some people got the brilliant idea that they would develop a machine learning algorithm That could predict a person's sexuality from a single profile photo on a dating site Why they thought that was a good idea is not clear to me and whether they ever thought that this could potentially be used to harm people Again never seemed to cross their mind. They thought we're doing something. We're building something By the way, this was the same people behind the Cambridge Analytica scandal Where they were scraping huge amounts of data from facebook for political candidates many years ago. Yeah, whoops And yeah, so we have some people who don't consider the implications of their actions But predicting sexuality from a photo. Okay ready? They did in fact develop a method that was able to predict above chance Whether a person was gay or straight And so they saw that as success And they claimed if for instance in the case of men They said well the reason we can do that is because gay men have been subject to in utero feminization And they've caused their faces to have different features gave them smaller chins larger eyes a bigger forehead And that's what our algorithm is picking up and they're inventing this very elaborate Theory about and said and that's what makes them gay well, um, some other people came by and Looked at the same data and they didn't actually say hey dumbass, but what they did say It's the angle of the selfie. It's how high are they holding their camera? Many women know If you hold your phone here when you take the picture you're getting like the double chin Or in the in your nostrils, but if you hold it up here, it looks better Well, when you hold it up here, you have a bigger forehead. You have a smaller chin And so they invented this entire theory about hormonal Hormones changing the structure of the face like It's where you held your camera. You guys are familiar with Occam's razor The simplest explanation is usually the correct one and holding your phone a little higher is a much simpler explanation than This whole thing about changing the structure of the face They also concluded that they had all these things about you know predicting Lesbian women and they they also came back saying like it appears to be whether they are wearing glasses in the photo And have short hair and they're not wearing makeup Yeah, surprise. So They were the shockingly easy things and you say like you did not need an algorithm to do any of this Okay, so let's talk about dumpster fire number two low birth weight and I got this from a textbook on machine learning And again, I I do not cite the textbook because I'm trying to protect this person But they wanted to run through this whole giant example of how to use machine learning to predict um When a fetus was at risk of being born with a low birth weight Okay, that's a really important thing to know Because if you can predict that ahead of time if there's anything you can do about it that can I mean that's an actual life and death situation That's important And after their very long extended conclusion, they said if they're twins or triplets, they're going to be smaller Once again you go But yes, you simultaneously wasted, you know half an hour of my time gave me a completely obvious solution And it's useless because there's nothing you can do about it. Okay ready here. I go don't have triplets Now that's true. There is something you can do if you're doing fertility if you're doing in vitro Yeah, you have a higher risk of twins or triplets, but There's nothing you can do about it and I'm going to come back to that theme your analysis When you get your conclusion, there should be something that you can do about it Simply stating the obvious if twins and triplets are smaller than singletons is not helpful Okay dumpster fire number three The goal here was to develop an algorithm to recommend sentences for people who have been convicted of crimes um Again by people who didn't necessarily take the time to think whether that was even a good idea in the first place Well, they claimed that their super secret proprietary advanced, you know Method was ideal by what criterion we don't know um It was predicting risk of recidivism, maybe but it it was it was really problematic anyhow, um The public service agency pro-publica came out and said hey, guess what we got the same dataset and we could recreate the Accuracy of that thing with five sentences of if this then that if this then that Five lines as opposed to this super secret proprietary machine learning mojo um also While i'm a big fan of ai and machine learning and data science in general One of the most successful courses i have on linkedin is the one that talks about all the things that can go wrong And how social biases tend to just get replicated and exaggerated One of the big problems that happens and again, this is why I think that people in the The liberal arts the humanities and social sciences are important here Is because when people develop an algorithm other people just go along with it going to go Oh, the computer said so the computer knows the computer's full of crap If you gave it biased data to start with and all it does is reproduce the bias just In a much faster more scalable manner. You have simply taken the problem and made it bigger And that is true for so much um I have a news feed and I get two or three stories every single day About machine learning going haywire um You know they The most amusing one it's not machine learning per se, but it is technology going bad is uh There's a company that makes electric bicycles called van move And they're very fancy And you use your phone to unlock them and it's it's all very cool. Well van move went bankrupt and suddenly The servers that unlocked the bicycles were not available And so everybody who owned a van move could not ride their bike And so they were calling the police to say basically that van move had stolen their bikes from them Even though it's right here. I can't ride it Um Van move got bought out by somebody else. I think they have maybe fixed that problem But the idea is tech is not always the solution and you have to think about some of the consequences of your actions And again people and this is one especially with people with a background in history political science are gonna say We've been through this before there's a problem. You need to be aware of it Okay, and then let's do dumpster fires ad nauseam Where somebody gives you a data sense and says just analyze the data find something And so you go through it and you find random significant findings and I put I put significant in quotes there Because you may those of you who have statistics know that that word has a very special meaning in statistics It means a finding Where the probability is less than five percent of getting that finding if the null hypothesis is true And you have to say that whole thing Um But people don't pay attention to that they get a significant finding They think that means that it's big or it's important or it's meaningful None of that is necessarily the case So they say well, I got the data and I found like four correlations that are have a p less than o five here They are And then the crash is nobody knows what to do with it and it ends up just being junked You know this happens, right? I have Recently meaning in the last month Read a report by a professional researcher that fell exactly into this category Where I don't know how many thousands of dollars of resources has been used in gathering the data and doing the analysis for that report But when it was all said and done It was completely useless They kind of highlighted. Well, here's this and here's that but yeah, okay, there you go And you say what what what do I do with it? So this gets to the point why this is relevant to you and that's because You can work in data and you can build on your strengths Here's here's a few things. I mean I'm going to show you three activities ready to from not so hard. It's a harder to hardest Not so hard is the technical skills. Anybody can learn to program in python It's the simplest language in the world. Anybody can do it Okay, so when people say that they're part python programmers sort of like saying hey, man I know how to write a complete sentence, you know, it's not that hard. You can learn how to write python in a week You can learn how to use a spreadsheet in three hours The technical skills are not hard now I understand that there is a craft to this and there are people who take pride in it But I would again I would say 90 of the work You can learn how to do it in a few weeks It's really not that hard what is harder Is understanding the organizational culture of the group that this came from the and what sorts of answers work well for them and what's hardest of all is developing critical thinking so you can avoid the dumpster fires The people who developed the ai gator were technically extremely competent people but functionally they were idiots And they did not take the time to realize that they had gone straight off the deep end and their conclusions Develop the algorithm great. That's wonderful. You know what? It's really easy for you to get online and hire somebody to develop the algorithm for you But interpreting it and using it responsibly It requires the kind of critical thinking that I believe that the liberal arts the humanities and social sciences That's their specialty That's the most important part um There is a saying and I have to break this up into four sections because it's a long saying But this is a well-known truism in the data world It is easier to teach technical skills to someone who understands your organization your business model Then it is to integrate a person with technical skills into your organizational culture Take somebody who understands how the world works teach them how to program Much easier than taking somebody who knows how to program and teach them how the world works okay You guys are in a situation where you have special training in how the world works That's going to be the critical part the technical skills are not difficult It took me years of graduate school to develop the way of thinking That has stayed with me ever since then I picked up the programming on my own Because it's not the hard part Understanding the culture the organization you're working in and understanding human nature and where things fit And what makes a useful response and what makes a useless response? That's the hard part And that's why being educated is often much more important than having the technical skills you can acquire the technical skills Okay The next thing is In terms of what your skills should specifically be focusing on when you're working with data is number one What are the goals of the project? So often people get started on a project or somebody hire somebody to do a project without ever specifying what their goals are and what this leads to is weeks or months of Back and forth or waiting or wasted effort One of the most important things you can do before you get started on a project is to know exactly what the goals are So you know when you've reached them Um and to avoid extra effort The other one is can you interpret it? Can you make sense of it? Can you tell a story about the data now? I'm not saying like once upon a time But simply being able to draw a thread We want to know this and so we got this data and that connects with here And it means this except for when it breaks down this way and this is what we do as a result It's a simple story, but the ability to thread it through I recently Was paid a huge amount of money to develop a course for practicing business analysts And I taught the course three times with people who worked for one of the largest corporations in the world And they were professional analysts in that company I would say that probably Three out of the 50 people I worked with knew how to walk through the data So that you knew what they were saying And you knew what to do as a result of it Many of the others eventually got it some of them. It was just it was just bouncing off And it never got there. These are people who are employed professionally as data analysts And the last one here is when you work with data, you're trying to give people a roadmap You were trying to say do this the phrase is actionable insights And I know that that's a very jargony phrase, but it's an important one. In fact, I will say more about that Actionable insights is the key term put that on your resume your letter app Letter of application if you say, you know how to design An analysis for actionable insights You're good. You got the job. Here's what we mean What is somebody actually going to start doing stop doing or continue doing but perhaps with more confidence now As a result of your analysis if you can't tell what changes as a result of your analysis You have not accomplished your goal What are they going to do? That's why it's called actionable insights and insight is something that you find in the data Oh, we found a pattern here or we found a difference between two groups. Fine. That's nice But what are people going to do? That's the actionable part and until you can say what people will do as a result of your insight you have not Done your job Okay Now let's answer the obvious question You know doesn't chat gpt do that Can't we hire chat gpt for free to do everything? Can ai do it? I'm a big fan of ai. I have the paid professional account to chat gpt and I use it frequently I like it, but there are issues Actually, let me just back up You guys are aware of the hallucination issue with ai Are you aware of the lawsuits Where loy first the best one is the is the law firm that submitted a brief that they had chat gpt right and it totally made up non-existent non-existent cases and created citations for those they submitted to the judge and Not only did the judge call bs on them. They got severely reprimanded for Submitting a work of fiction As their brief Chat gpt is very useful except for when it isn't And the problem is you usually have to know well enough You don't have to know what the right answer should look like So for instance, I I have people who have asked for chat gpt to write some code in c++ And that it was doing fine until it stuck some javascript in there, which is a different programming language Um, I have had it I've had it Try to come up with some statistical examples and it uses the wrong terms and it gives impossible calculations You always have to check chat gpt doesn't do math It does something that sounds like math it resembles math If you have the pre if you have the paid version you can hook it up to wolfram alpha And wolfram alpha can do math But you're gonna have to pay 20 bucks a month to get that um Otherwise it makes stuff that looks it chat gpt is great at generating plausible answers That they look that they're correct. Unfortunately, it is not ready to take over the world um this right here is a report from linkedin is it's uh They released a frequent one on the future of work report And this one was specifically about ai and this one was released in august of 2003, but it was all done in response to the november 2022 Is when the ai world changed because that's when chat gpt came out ai existed before that I mean ai goes back to the fifties Um, but the modern version of ai which is based on deep neural networks and large language models That's been around for a few years, but it wasn't till chat gpt got um Released in november, which by the way was the exact same day That linkedin published my most recent course on ai accountability. So like oh great now. It's all immediately obsolete but um The future of work report by the way That'll get you the pdf um Let me tell you a few things 92 92 of us executives agree that people skills are more important than ever And this is a survey done right after This is subsequent to chat gpt coming out. They're saying we still need people skills And then here is linkedin reporting on the growth in demand for various job skills since november of 22 Which again is when chat gpt came out These are the four areas that have shown the most growth in job ads on linkedin Look at this flexibility Professional ethics social perceptiveness and self management. None of which a computer can do These are the things that people in the humanities the social sciences and the literal arts Specifically develop skills in these are the most in These are the ones that have shown the biggest growth and if you want to know In the us this again, this is according to the same report by linkedin communication is the Number one top skills sought across all job postings Any of you communications majors way to go you win But the ability to communicate clearly with other people Is going to be the single most important thing. I have a sister who is um Who actually has a degree in communication and She has worked in public relations For years and years and years and But she has worked in the tech industry For the last 30 years And It started out with her doing legal pr for tech lawsuits But now she has many clients in silicon valley. She lives in san francisco And she she is training them how to talk on stage They have to be able to explain their product or their service or their company to other people and again These are all very very technically smart people Um, and she is making something along the lines of half a million dollars a year teaching them how to communicate So Good job All right, so now what do I do? What do you do now? Okay, here's a few things. I'm gonna recommend number one Think about it. Think about the possibilities that are out there. You might like it When I started teaching in graduate school Um, I had this fellowship that was paying for grad school But I I was I was sort of a useless Fourth member of a research team And I just kind of sit there and say like I I think you've got the period in the wrong place You know eventually my my advisor said, you know bart, I think you should go do something To earn your fellowship And she said maybe teaching now I was at a grad school that had no undergrads the city university of new york graduate center And so I had to shotgun Paper letters out because this was like pre email I had to shotgun out paper letters to all the undergraduate campuses that That were part of the city university of new york system And I said, okay, I can teach like the following eight courses and I can do it for free because I'm already paid And I was crossing my fingers and saying dear lord Let it not be statistics or research methods, which of course is exactly what I was offered And I was panicked And when I was teaching statistics, I was barely ahead of the students for the first semester The strangest thing is I have come to love it Keep in mind. I'm a person with a background in design. I spent more time taking classes in color theory and drawing And 2d design And I ended up loving statistics Even the calculation part of it. You never know until you're actually there You have to try it So think about it. You might like it think about some of the possibilities. Look what's out there Also, pay attention. Just look around you and see places where maybe a little bit of data would help Where things look like they're not going very smoothly. Things look like they're kind of silly See places where you think data might help them make decisions. This is They're all over the place Maybe they could do something with the parking lot. I don't know But there's a lot of things you could do also a little bit of statistics is nice I keep saying that I tell my students I will keep saying that You don't have to have an enormous amount But if you know what a correlation is If you understand a basic regression model If you know the effect of outliers You've got a great beginning The statistics again is not the hard part, but you have to have at least some to go with it And then spreadsheets. I'm going to give you a tool Microsoft Excel or Google sheets. No seriously really If you can work a spreadsheet a couple of things happen number one you suddenly have an employable job skill There are people out there doesn't need to be you but there are people out there Whose jobs consist of working in spreadsheets all day every day And if you end up one of those people you can learn how to do what's called vba Which is visual basic for applications That's a programming language that works within excel And then you can get your eight hours of excel work done in one hour and go watch movies for the rest of the day I know people who have done this They specifically got a job that where they were in the building by themselves at night They would launch their vba script and literally go down the street and watch movies But they had they got all the work done Um Any a little bit of spreadsheets. In fact, I'm going to quote the wall street journal on this one now This is from 2015, but I do not think that this is changed appreciably The key to a good paying job is Microsoft excel to which the article says the answer is yes A little bit of Spreadsheets can make a big difference. The nice thing about spreadsheets though is they're really easy to learn And you use them all the time. I had four spreadsheets open while I was preparing this presentation I use spreadsheets every single day And My favorite one I got one of my kids is here Several years ago my wife and I um, we took our family on a trip around the world And involved 16 airplane flights And our kids were young and we were going to places that were important to them But also because kids are kids. I didn't want them arguing about who got to sit at the window seat I created a spreadsheet That calculated how much time each kid would have At the window and making sure that they were there when we landed in a place It was important to them and I showed it to them. I did my best to balance the time We had some other constraints on it too But you know, it really was sort of thing like I've thought about this and this is the best I can do So please don't argue Um I use them all the time Um also I have an event we call it the data charrette now charrette Which literally means a small cart in french But it's a term that's used to revert to creative activity under extreme time pressure And this is something that I've done several times in the past And what it is is in the past we would do this over two days We would partner with two local nonprofits who had data and they had a question But they didn't know how to use the data to get their answer And um Over two days we would take their data. We would get it all cleaned up which usually took a day and a half And then we would go through the data and come up with an answer for them As often as not it reduced to making what's called a pivot table in in a spreadsheet A pivot table is a very employable skill It's a way of collapsing the data in a way that makes it very easy to understand what's going on We're going to be relaunching the charrette In november so that is in about six weeks And we're going to have two local nonprofits. We're going to be here on campus It'll be a one day event just a saturday And we'll have food and it will be an opportunity for you to come and work with some data again The data that we're working with is not going to be complicated It's the making sense of things and telling a story about it. That's the part that we really need help with And so when we do this event on saturday november 4th, and then again in the spring on saturday march 2nd I would love to have you there. You can put it on your resume One of the conditions i'm giving to the nonprofits is that they will also give a linkedin endorsement for anybody who contributes to their project This is a great thing to do. So if you don't have a linkedin profile now's the time to get one And so november 4th and march 2nd We're doing these free events on saturday. You get to work with data. It's it's one of the most gratifying projects i've ever worked on And with that I am done and here's my little Link tree that has the links to everything else, but folks. Thank you very much Now I realize that some of you have to go to class But I I am here for a few minutes if you have some specific questions If you have questions, just raise your hand and we'll bring you the mic so that we can get it on the the recording Oh, yes What do you study like? Where do you learn all the knowledge to know now? Say it again. What do you study? Like to know everything you know now um So my degrees are in psychology. I have four degrees in psychology. I taught myself statistics um, and one of the things I think has actually been the best in terms of my professional ability the reason I'm Is I have 30 years of experience teaching statistics to scared undergraduates who don't want to take my class And so I'm able to find a way to make it approachable And that has made a very very big difference in my In the work that I do I said Candida What would be some advice you would give to students who are looking to make a jump from something like Psychology into learning more about statistics Okay ready know how to work a spreadsheet I mean seriously start with a spreadsheet if you can make a bar chart a line chart a scatter plot and a pivot table You literally have 90 of what you need You can you can then use to learn you can then learn to use a um an application like spss Or I actually prefer jamovie, which is a free open source program that resembles spss And you know you can learn to use a programming language like r or python, but You don't need those to work with data Again start with the spreadsheet Again and just creating some data in your own life. I mean seriously count how often you have like, you know How often you have to feed the cat how often you know you've got to change the temperature Just start with data sets that you can get any of them just get started again If you can make if you can sort the data if you can make a bar chart a line chart and a pivot table You're great Okay, um over I'm in my mid 40s over the course of my life. I've taken the Myers-Briggs personality assessment three different times And something that kept kept coming up was counselor some kind of counselor counseling though So that's why I'm here in humanities. Um, but I've heard criticisms of that as like a pseudoscientific kind of like the botched AI Like how how valid or credible or viable is that? You asked me about the mbti. Yeah I'm gonna give you my take on most tests. Okay. They're great for starting a conversation Okay And that's where most tests should end. Okay. Thank you. They're great for starting a conversation One one add on to that real quick though and it does ties in with what you talked about earlier being like the complementary to the tech world like People need us this people skills. Um, I had career counselor Interpret the results for me That's all like community college. I think they scrapped the program now. They don't offer that anymore, but She gave me some really good insight and that was You could go into a company with your people skills and be like the missing little link that they don't have And do just great even though you're not like profoundly, you know data driven or tech driven They need you so like if you can get in there you could you could have that niche kind of job No, I believe that's true and I believe if you have just a small amount of technical skills It it works wonderfully, you know There are so many examples of people who have who have risen to prominence In fields that require, you know specific skills. It turns out that that person doesn't really have that much But they are often I don't want to even say his name, but Elon Musk I don't know how smart that guy actually is I don't know how much of this stuff. He actually does and I do think he's a total psychopath Um, and so I don't want to hold him up as a model But I think what it is is He makes things happen And he connects things and makes them happen again I don't know what his actual contribution beyond being the motivator is to any of these things But there are lots of others one of the great american architects philip johnson was never a very great architect in and of his own He's graded like two buildings that are well known But he was the mentor to An entire generation of america's most influential architects. He made those things happen. That was his contribution Okay, we have time for one more question anybody Why did you become so passionate and moving from psychology to data? It was it was a totally accidental change um, not something that I anticipated and It's it's hard to explain. You know, what's funny is my career has actually changed several different times Even while I've been in psychology the whole time I spent several years studying, uh, the legal system and restorative justice Then I spent several years working on creative coding and art Then I've been doing all the research and this is all well employed here As a psychology professor. That's a nice one of the nice things about being a data person There's a there's a there's a great quote from one of the guy named john tookie And he says the great thing about being a statistician is you get to play in everybody else's backyard Uh, you get to do all this other stuff. Um and see whatever other people are working on And I love doing that and truthfully it's because I actually get a lot of gratification from being helpful And a lot of people get very panicky bad statistics And I feel that You know, I sometimes joke that teaching statistics is like, you know, given colonoscopies and the worst, you know The best you can hope for is that people go, okay, it wasn't as horrifying as I thought it would be But I like being in a situation where I can help people make sense of something And and I know for instance a lot of people they're worked They're just working on their academic papers and they go I got data I don't know what I'm doing and be able to help them out and walk them through it It's enormously gratifying All right, thank you so much And we hope y'all enjoyed today's session and that you'll join us next month For our next degrees daniela speaker. So watch your emails for that