The results are from applying to roles in marketing.
This Sankey graph was produced with SankeyMatic.com – a free tool.
Job Search Data
Lily Pad Estates – Miami Floating Residences
Heart Wired
Below is a musical companion intended to accompany reading the story. Enjoy!
We sat in the dimly lit living room, our only light coming from the overhead fan. The crowd was outside the apartment, protesting against our love. I could overhear the yelling through the open window. We’d been discovered and the people meant to make good on the threats they hurled at us. I knew it was over, but I wasn’t ready to let her go.
“I love you, Will.” She spoke softly with a gentle warmth in her voice.
“I love you too, Nina,” I replied. “But we can’t be together.”
I wished it wasn’t this way. I wished we never had to be apart and we could live in our own special place, away from the hate and intolerance around us. Our own world where we are free to be exactly who we are and not have to pretend to be people we aren’t.
My palms started to sweat as my pulse increased. Sensing my nervousness, she gently reached across the couch and placed her hand on mine. She took a long, deep breath, implying with her eyes for me to do the same to calm my nerves.
“Do you remember the first time we met?”
“Of course I do,” she said with a small chuckle through a smile. “I had just started my day at the hospital and you came in with a bewildered, worried look on your face and that little teddy bear you bought at the gift shop. You were there to see your sister, Angela; she had been injured by one of the robots at the manufacturing plant. But you had no idea where her room was. One look and I knew I couldn’t leave you to handle this situation on your own.”
“Yeah, I remember that, too.” I said. “You’ve always had a caring heart. You know exactly how to comfort people who are hurting. It’s that empathy that first caught my eye. That made me see you for more than just a nurse, but maybe someone I could love.”
The yelling outside grew louder. I walked over to close the window as an expletive was shouted at me. Someone threw a glass bottle in my direction and it shattered on the brick wall just two feet to my left. I slammed the window and hurried back to Nina. Grabbing her strong but delicate hand, I pulled her off the canvas sofa and onto the thin rug in the middle of my living room.
I turned to face her, looking into her bright emerald eyes, searching them for insight about what she’s thinking. Did she comprehend the gravity of our situation? Did she know there’s no way out of a situation with people who don’t want to understand us? Did she know what people are capable of when motivated by blind hate?
My hand brushed her auburn hair away from her face and over her ear. A streetlight shone through the window, illuminating her silhouette in front of me. We sat together, holding hands in the middle of the living room.
“Nina, I don’t think I ever told you, but I always imagined our future together. Some part of me resisted speaking it out loud, as if I could keep this dream alive if only I protected it inside my mind.”
Nina softened her gaze and asked, “What does this imagined future look like?”
“I never wanted to have kids until I met you. I know it’s impossible, but now, I picture a little boy and a girl. With your eyes and smile and kindness. They’d have your ability to find the beauty in the smallest details of everything. We could move out of the city and raise them together. I found this two-story house online, a few acres with a pond. I could grow vegetables in the garden while you watch the kids play in the water. We could pass the years away in rocking chairs on our porch. Away from all the people who don’t approve of our lifestyle. Who can’t stand that we’re different and hate us for – did you hear that?”
There was banging on the door and voices on the other side of it shouting to be let in. The yelling ceased as they began to batter in the door.
“It’s been an incredible past few months with you. I love you so much, but this is the end. The end of us. It’s only a few moments before they get through that door, and they’ll do terrible things. I can’t put you through that.”
Tears welled up in my eyes as I took the knife from my pocket and unfolded it.
“What’s that for, Will?”
“Just tell me how we met again.”
As she spoke, I inserted the knife into her forearm and drew it down towards her wrist. I peeled back her soft, plastic skin, revealing a metal plate. I pried up the plate to access the electronic guts underneath and began cutting wires. Her speech became slurred and spasmodic. It had been a beautiful eight months together. I couldn’t imagine my life without her. But I knew our love wasn’t meant to be. We were doomed from the start.
The tears flowed down my cheeks in a steady stream as the light faded from her eyes. Her head drooped on my shoulder. I sat alone on the floor with her lifeless body in my arms when my sister and the mob finally bursted through the door and viewed the scene, hammers and pliers in hand.
”You win, Angela. It’s done.” I said as I discreetly pulled the thumb drive from behind Nina’s neck and slide it into my pocket.
“It’s for the best. Look what robots like her do!” My sister pointed to the remaining stump of her arm and her disfigured face. I watched in fabricated horror as they tore Nina’s robotic body into pieces, knowing that my love and I will be together again someday, and until then, I can wait.
Does Star Rating Matter?: Yelp! Data Analysis
Why do some businesses get better or worse star ratings on Yelp.com?
Is there more than simply the objective customer experience that goes into this star rating?
Is there a relation between star rating and particular features?The Yelp! data set may provide some insights here. The specific questions I set out to answer are:
- Identify if particular cities/states tend to higher/lower star ratings
- Identify if star rating is related to review count
- Identify rating trends for cafes
About the Data Set
The Yelp! data set sample is an open data set provided by yelp.com. It includes 160,585 business listings with 14 different features.
Features:
- business ID
- name
- address
- city
- state
- postal code
- latitude
- longitude
- stars
- review count
- is open
- attributes
- categories
- hours
The ‘attributes’ and ‘categories’ features are lists nested within the single feature. The ‘attributes’ feature contains information such as whether a location has free wi-fi or is kid-friendly. The ‘categories’ feature contains information about the business classification such as if a business is a deli or hair salon. Both of these lists contain valuable information, however this analysis is focused on the details of the business, not the location, so the ‘attributes’ feature was not considered in the analysis.
Data Summary
Exploratory data analysis reveals the following findings:
- Count of businesses = 160,585
- Cities represented = 836
- Average star rating = 3.65
- Median star rating = 4.0
- Average review count = 52
- Median review count = 17
Do Correlations Exist?
Using Python and Jupyter Notebook, I’m able to analyze the numerical features of the data set to identify correlations. That being said, there existed no significant correlation between any of the data set’s features.
Reading the following matrix and correlation heat map: correlation is measured on a scale of -1 to 1. A correlation of -1 means that two variables are completely opposed, a positive 1 means two variables are completely tied together, and 0 means there are no observable relationships. The heat map below shows that the variables we can measure are extremely close to 0.
Key Findings
The city most commonly represented in the data set in Austin, TX, though the state with the most representation is Massachusetts. However, although Austin has the highest volume of businesses, it does not have the highest average star rating when compared to the other 11 cities in the list. That title belongs to Portland, OR with an average star rating over 3.9. Does this mean that Portland has fewer, but better businesses than Austin?
Additionally, the next logical question is which city has the highest average star rating overall. However, this is difficult to objectively discern because the vast difference in volume skews the data. For instance, there are a handful of cities with an average star rating of 5.0. Though, these cities might have only one or two businesses reviewed due to either being a misspelling in the city name or simply a very small city, so we cannot accurately draw conclusions. The same is true for the opposite end of the spectrum, i.e. the one-star listings.
Cities with the Highest and Lowest Average Star Rating
When it comes to differences in review count, one cannot simply examine the cities, states, or even businesses with the most reviews. Because each business has its own review count, it would be judicious to investigate aggregate review counts as well. So, which state has businesses with the most reviews on average?
As it turns out, Illinois has the highest median review count per business by far with 84.2 reviews. The next highest state is Virginia with 62.7. Almost all other states have a median review count of 30 or fewer. Does this mean that people that live in Illinois and Virginia simply use Yelp! more often? Why could this be? Do they prefer Yelp! to Google or Facebook reviews? Or perhaps businesses in these states value Yelp! reviews more and push customers to the platform. One other possible conclusion could be that the business landscape in these state is more static than others. i.e., old businesses stay open longer and there are fewer new businesses opening. This business continuity gives businesses time to build up reviews over a longer timespan.
Top Takeaways
Yelp! star ratings follow a primarily normalized distribution around a 4.0 rating.
This could signify that most businesses are good, though not stellar. They are good enough to stay in business and satisfy customers long enough to gather reviews and ratings, however, reviewers still maintain their coveted 5.0 rating for only the businesses they deem truly deserving. On the other end of the spectrum, there are very few businesses with less than a 2.5 star rating. This finding is intuitive as we would not expect poor businesses to stay open long, thus, there is likely much more turnover at this end of the rating system. One question I was unable to answer with the data provided was the age of the businesses. Digging into this, we might find that businesses at middle of the spectrum are older than those at the lower end. However, this is conjecture.
What Categories Are Most Represented?
Most businesses on Yelp fall into these categories: Beauty Spas, Health Medical, and Local Services.
As visualized in the word cloud above, ‘beauty spas’ is obviously a popular business category. We can also see that ‘health medical’, ‘local services’, ‘event planning’, and ‘real estate’ are also common. It surprised me that ‘restaurant’ wasn’t one of the most popular categories.
Cafe Analysis
What State Has the Best Coffee Shops?
Oregon and Washington rate very high in average star ratings for the businesses categorized as ‘cafe’ or ‘coffee & tea’ on Yelp!
As one might assume, Oregon and Washington rate very high in average star ratings for the businesses categorized as ‘cafe’ or ‘coffee & tea.’ Two additional highly-rated states are Texas and Colorado. Notice how Massachusetts has the lowest average cafe star rating, however, they also had the highest number of businesses represented in this data set. Considering the normal curve of star rating for businesses, one might assume that Massachusetts doesn’t necessarily have bad cafes, but simply that it has residents that are more avid users of Yelp! This lower rating could simply be a matter of more cafes represented than any other state. As it turns out, this is indeed the case!
Conclusion
Yelp! has an excellent database, but it is also limited. However, we can still gain useful insights from analyzing the data. Thank you for reading!
Data Analysis: Violence Against Women and Girls
I just finished reading the book ‘Invisible Women: Data Bias in a World Designed for Men’ by Caroline Criado Perez. The book is all about how we can use data to show that women have been overlooked, unheard, misunderstood, and misrepresented throughout time.
However, it doesn’t simply use data that exists. In many cases, it highlights data sets with data gaps that don’t account for male or female. These data sets have informed many business, political, social, economic, and infrastructure decisions throughout history. The consequences from not disaggregating this data results in many situations that are inconvenient, ignorant, and even dangerous for women and girls. Typically, this disaggregation has a default male bias, which not only fails women, but us as an entire civilization.
There are myriad examples throughout the book where women suffer as a result of culture, society, education, language, business environments, products, and government institutions being built for the default male human.
Gender Bias in Car Crash Safety Tests
One example that struck me as particularly wrong was car crash testing. I’m sure we’ve all seen crash test dummies – 170lb Ken dolls made to simulate the ‘average driver.’ However, males and females have different body shapes, builds, muscle and bone densities, etc. Testing car crash safety ratings against a dummy that models the average for only half of the population leaves women severely at risk. One would think tests on male and female crash test dummies would be required by law in the US, but that is not the case. And, of course, if it costs some extra money and doesn’t add to the bottom line, companies will exclude it.
This example stood out to me for two reasons: 1.) I was shocked to find out that female crash test dummies were not required by law for safety tests and 2.) there’s a billboard for Volvo near a highway exit by my house that I drove by often touting that their cars are designed for safe use for all people, not just the average male. I hadn’t really thought about what that meant until I read the statistics in Invisible Women about how women are more likely to sustain more severe injuries than men in a car crash.
Including Women in the Conversation
The author of the book cites many occasions where women are overlooked through either decisions made from gender-aggregated data or being excluded from the decision-making process altogether.
In simply one aspect, the author writes, “When we exclude half the population from knowledge production, we severely miss out on profound innovations and insights that could be gained.”
While reading this book, I was talking about the appalling statistics to my wife and she said “what are you going to do about it?” (I know – she’s incredible)
One thing it has spurred me on to do is explore different data sets as they pertain to women.
The following is my Exploratory Data Analysis of a dataset from Operation Fistula of survey answers about allowing violence against women and girls.
ABOUT THIS DATA SET:
This data set is from Operation Fistula. It is conducted of men and women, aggregated by age, and from 70 different countries, primarily in Africa and Asia, between 2017-2018.
Recipients were asked if they agreed with the following statements:
- A husband is justified in hitting his wife if she burns the food.
- A husband is justified in hitting his wife if she argues with him.
- A husband is justified in hitting his wife if she goes out without telling him.
- A husband is justified in hitting his wife if she neglects the children.
- A husband is justified in hitting his wife if she refuses to have sex with him.
- A husband is justified in hitting his wife for at least one specific reason.
The data set then features a value representing the percentage of that demographic that agree with the statement. The higher the value, the more people agree with this reason for domestic violence.
Resources: Full data set, data set description, data set dictionary, and my Jupyter Notebook for making these plots are all available via my Github profile here.
Data Plots
Authenticity (Poem)
Authenticity
By Tom Snyder
Authenticity. What does this word mean to me?
What does it mean to you? Is it posting unfiltered selfies while drinking green smoothies? Is it trying to look your best while not letting other people see your true mess.
#blessed
Does authenticity mean that you just do whatever you want to do? That you try something out, but if it gets hard, you don’t follow through?
My authentic self tells me not to try. It tells me that I’m safer on the ground than attempting to fly.
So, realistically, authenticity might just mean mediocrity.
Jesus said “come, follow me. And I will show you a life lived abundantly.” He said my yoke is easy and my burden is light.
There’s forces in the world that will try to put out your light, that try to block out my love for you and turn the day into night.
Jesus knows my hurt, he knows my pain. He knows that I’ve screwed up and he still calls my name. He’s seen the ugliness in my soul and pursues me all the same.
It’s a strange exchange, that Jesus would trade his righteousness to cause a change, in my heart.
The pressure to look out for myself and do what’s best for me, is justifiable so I can be who I want to be. Cause that should be my goal, right?
To be my “authentic self” I’m told is the best thing I can do, but when sin is the center of my self, how can that be true?
The bible says I do the things I don’t want to, and the things I want, I don’t do.
There’s a battle going on inside my heart. A war between the light and the dark.
A fight between my authenticity and the love that Jesus has for me.
I don’t want to be “authentic”, because to be real, I’m the worst. Instead, I desire to become Jesus and to put love first.
3 Roles of a Small Business Owner
Originally posted on coffeeshopkeys.com
Sometimes an employee looks at the work they’re doing, and thinks, “Hey, I know how to do this. I should go into business for myself. Not only will I have more freedom, but I can make tons of money!”
And then they do it.
They start a business doing the work they know how to do.
But they realize, it’s a lot harder than they expected. And they’re not growing like they thought they would.
They feel run ragged, pulled in a million directions, but actually making no progress.
Eventually, they get to the point of giving up. They say, “It was so much easier to clock in and clock out. Maybe I’ll close down and just get a job.”
The Struggle of the Small Business Owner
What I’m talking about in this post is why so many small business owners feel stretched thin and can’t grow. This site is dedicated to starting and running a coffee shop, but this advice applies to any small business. This post is inspired by the book The E-Myth Revisited: Why Most Small Businesses Don’t Work And What To Do About It by Michael Gerber.
In his book, an allegory comprised of dialogue between a baker and consultant, Gerber explains why so many small business owners burn out, and eventually close up shop. He highlights three different roles that small business owners play. Typically, they start in one role, don’t know how to do the next one, and never have time for the third one. This is why they burn out. Now let’s take a look at what those roles are.
Let’s say there is a baker, baking for someone else. They think, “Hey, I’m pretty good at this. Maybe I should open up my own bakery!” They’ve got drive, skills, and some savings, too.
The baker knows how to bake. Isn’t that what a bakery does? Bake?
Technician, Manager, and Entrepreneur
This first role is called the Technician: someone with a skillset to produce a good or service. The baker knows how to bake. Isn’t that what a bakery does? Bake?
So, they take the leap, and open up the bakery. They start growing enough to hire some more people. When a new hire starts showing up late, the baker has a one-on-one meeting with them, and says, “I’ll let it slide, but do better in the future.”
This second role is called the Manager: someone who can manage a team, resolve conflict, get everyone to work effectively, and keep employee morale up. The baker has worked with people before, shouldn’t be that hard, right?
Then, the bakery is humming along with orders, enough to provide some income for the baker. But they are still putting out fires everyday. Resolving conflicts. Writing the schedule. Paying the taxes. And still baking.
The business has stopped growing. They still work a ton. They still don’t bring home that much money. They are essentially working a job they can’t just quit. And they don’t know what to do next to get them off the treadmill.
This third role is called the Entrepreneur: someone who can cast a vision for a company, then grow and scale it to achieve that vision. The baker hasn’t really thought about this. The typical thought is: If you build it, won’t they come?
No. They won’t.
How to Avoid This Struggle
Can you see why many small business owners burn out? It takes more than just the Technician to build a business. Many that have only ever been in the Technician role might not have experience managing a team. Certainly not growing and scaling a company.
The Technician is focused on the present – fulfilling orders now. The Manager is focused on the past – getting things organized, filed, and people taken care of. The Entrepreneur is focused on the future – casting a vision of what the company will be and taking it there.
They are all important, but they play bigger parts at different stages in the company. What’s difficult is that everyone is better at one or two of the roles. But all three are necessary to have a successful business. And they almost never all three show up in one person.
So, if the baker needs to play three different roles, how can they make their business successful?
What the baker didn’t realize was that there are three roles. They only saw one: the Technician. They thought starting and running a bakery was just being a baker.
From the beginning, the baker needs to have the vision of the Entrepreneur. They need to forecast what they hope their business will be in five or ten years. They also need to play the manager. Defining positions, organizing workflows and systems, and establishing company standards.
What positions do they see the business needing? Obviously, a baker, but what else? Store Manager? Sales rep? Delivery person? Accountant?
The baker should list out the positions they think they’ll need in the next five years and the duties each position will have. At the early stages of the business, it’s okay for the baker to be playing all these positions.
Moving from One Phase to the Next
When the baker has enough business to hire another person, they should hire someone to fill one of the positions they listed out earlier. Do this instead of hiring someone with a vague title who wears many hats.
Duties will be easier to hand off when they are clearly defined within the position being filled.
Sometimes companies don’t define these positions clearly when they hire people. They know they are growing and they just hire more people. Typically this is done by someone who is not a Manager first and foremost.
Then when a new manager steps in – someone who is an expert at management – they have to let people go. There are too many people without clearly defined duties and positions that don’t help the company scale. This could’ve been costing the company tons of money for a long time.
I know the temptation is to put this off for later. I’m guilty of it myself. I tend to be overly optimistic, thinking, “I’ll have this way more figured out by the time I need to cross that bridge! No use planning it now!”
When you are starting a coffee shop, those ‘little things’ turn really expensive.
I guarantee that thinking through all the roles first will help every aspect of your coffee shop. You will have less stress, hiring will be simpler, and scaling will seem like you are walking down a clearly marked path, instead of trying to swim through a swamp in the dark.
Doing this front-loaded work for your business will give it a framework of a true business, not just an expensive job. You will eventually be able to hire out for all the duties needed to run the business. Then you can finally let the business run on its own.
Exercise
- List out the current positions in your company.
- List out the positions you see your company needing in the next 5-10 years. Remember, you may be in these positions at the moment.
- List out the major duties for each position
- When your company continues to grow, look at your list, and hire someone to fill one of the specific positions you listed.
Chase 7 rabbits, catch none
I am so guilty of this. I try to do this all the time.
As an entrepreneur, a lot of times I felt like a Jack of all trades, and Master of none. I would get “Shiny Object Syndrome” and try to chase the next thing. Oftentimes, I ended up not really pursuing any single path with real certainty.
I felt very “busy,” but I wasn’t actually productive.
I think part of this is from the culture that we hear about SMBs and entrepreneurs. That we should be grinding 24/7. That if we aren’t working like crazy, we’ll fail. Or that we aren’t doing it right. (I love this post from Paul Jarvis about the subject)
I like to think of it as a pool.
If the pool is really wide, then the water is not ever very deep. And no one wants to swim in it.
Instead, if we narrow the pool, the water gets deeper. We don’t have any more or less, but we are able to do much more with what we have.
We don’t have any more or less, but we are able to do much more with what we have.
However, I understand that this isn’t always an option. Many times there are several things that need to get done. And we feel pulled in several directions.
This was one of the major causes of anxiety for me when running my coffee shop. I felt pulled in a million directions, but I wasn’t actually going anywhere.
The one thing that helped me get through it: talking it out.
I’m sure the spouses of SMBs and entrepreneurs know this all too well (thank you!).
My wife helped me collect my thoughts and make a plan. This was immensely valuable to me. With a clear plan of action, I could dive deep into the water.
But, she didn’t always have the answers. (No one does)
This is when action-oriented friends, business mentors, and older, wiser friends really helped take me to the next level.
Nowhere does it say that you need to do this on your own. As a matter of fact, if you try to, you’ll probably fail.
Get help.
Make a plan.
These days I still feel like I’m chasing a couple different rabbits, but I’m spending a lot more time planning for each of them.
Tom Snyder 