Lessons Learned From the Election for Data Science and Programming
By: Drew Seewald
Tags: Data Science, Programming, Machine Learning, Election 2020

Opinion | Data Science & Programming

Lessons Learned From the Election for Data Science and Programming

Lessons we can take away from the recent election and to improve our data science and programming work

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The current presidential election has been a stressful time for many. Times are always uncertain during a presidential election. The stock market is uncertain, bitcoin is rocketing up in value. But when the dust settles, what tangible lessons can we take away from the election and apply to data science and programming work?

We saw an election this year where we didn’t have a clear idea of who would win the presidency on election night. We saw the process of how we vote change to some extent. We saw some assumptions about voters change from previous years. We can learn from all of these situations and apply them to data science and programming.

Waiting for the votes

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For those of us who are younger, we aren’t used to having to wait for things. We have the internet literally at our fingertips, with nearly anything we could want to learn just a search away. We aren’t happy to wait around for anything, because more often than not, we don’t have to.

Enter the 2020 presidential election. Voting for the president, by mail or otherwise, has been around for centuries, and the process still involves physical paper ballots in 2020. With the COVID-19 pandemic, multiple states expanded their access to mail in voting in an attempt to prevent the further spread of the virus. These ballots require people to open and process them. This takes a lot of time, and it contributes to the fact that days after election day, we still didn’t know who the winner was.

Put aside the fact that other countries have the capacity to vote online in elections, as opposed to physical paper ballots, that isn’t what we’re focusing on here. The process wasn’t something that we could complete overnight, and that is the first lesson we can apply to data science and programming.

Rome wasn’t built in a day. Same goes for your data science model or program

Now that we’ve pulled out the first lesson, it isn’t hard to come up with a few ways this immediately applies to data science and programming projects. Obviously they take time. These projects, whether created for your education or your employer, require large amounts of time to complete. You need to collect data, form hypothesis, conceptualize the programming, write pseudocode, debug actual code, the list goes on and on.

So what sort of peace can you derive from this lesson in your work? The processes of data science and programming are time intensive. Sometimes, like an election, you don’t know if a model can be built the first day looking at the problem. You might not know how long it will take to collect or clean enough data to use as an input. You might run into issues with the programming language you are using and spend an unexpectedly long period of time stuck debugging.

It is okay to wait for things. Remember to manage expectations. Election day is not when all the votes need to be counted, it is one of many steps on the way to electing the next president. Know your project may not be done exactly when everyone is expecting, and that might actually be okay.

Changing How We Vote

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In the 2020 election, a big focus was on how we voted. With the pandemic still gripping the nation, many states expanded their options to vote by mail. In a very short time we drastically changed how we vote in elections. For nearly 250 years people have thought about voting as something that you mainly do in person in the United States. Now, it is becoming easier to vote and have your voice be heard, even if there is a global pandemic threatening the safety of everyone who shows up in person.

So what is the lesson here? Sometimes things that we have come to expect as the status quo can change, and this change can also be good. Whoever you voted for or hoped would win, having easier access to vote by mail is a good thing for everyone. Change can be productive, even if it is something we never thought would change.

Change can be good, even if it is a major change to a process

There are tons of ways to apply this knowledge to your data science and programming projects. Maybe you only know one programming language and it is terrible at creating a frontend interface for your users. It could be time to switch. Sometimes companies feel stuck in their ways and married to a certain technology. Traditional databases are great, but don’t allow for the same sort of analysis as a properly modeled data mart will. Sometimes change is beneficial to an entire organization.

While thinking about the change, remember the first election lesson: things take time. If it is switching from an antiquated technology to something that is better supported and more feature packed, or creating new data marts to support an analytics strategy, these aren’t changes that will happen in a single day. These election lessons build upon and interact with each other.

Question What You Think You Know

Finally, during this election cycle we found ourselves questioning a lot of things that we thought were givens. I was talking to a coworker about the outcome of the election and he was sharing the fact that he had voted for Republicans for president for more than 30 years. I don’t think about it too much, but never guessed he would vote like that.

I’m sure there are some of you reading this who can relate. It could be a family member or coworker or friend, but sometimes you learn how someone votes and start to question some assumption about who they are or who you thought a Republican voter or Democratic voter should look like. Sometimes the things you assume are wrong.

Sometimes the things you assume are wrong

Another thing about this election that was particularly interesting was some Latin American voting patterns. Typically we have seen Latin American voters who support the candidate with the more open stance on immigration. With the constant campaign promises and some actual progress on building a wall and issues at the Mexico US border in recent years, it is pretty clear President Trump isn’t a fan of people from other countries coming to the United States.

Despite the fact that we might expect Latin American voters to vote for another candidate based on immigration policy alone, a surprisingly high number still voted for President Trump in the 2020 election. It seems that many of them are full fledged United States citizens and don’t need to worry about immigration. Some of Trump’s stances line up with the traditional Catholic views held by many Latin Americans. This takes us to the final lesson, challenge your assumptions and what you think you know.

Challenge your assumptions and what you think you know

It is true for data science and programming. In programming, you may think that something isn’t possible with the libraries you are most familiar with. Maybe you think a particular language is terribly inefficient at what you are trying to do. Don’t be afraid to do some research into something you think you already know about. You might be surprised to learn there is an updated version of your library that excels at the task at hand or that you just didn’t know how to use that programming language to achieve the level of efficiency you were hoping for. Challenge what you think you know.

In data science there are many times when we build models based on assumptions. A common one is linear regression. It is one of the most commonly known statistical models, and it is incredibly simple to create one. The reason it can be so simple is because it has so many assumptions. When you start to violate these assumptions, you start to introduce bias and inaccuracy into the model’s output. If you didn’t even think to question the assumptions that a linear regression model is built on, you could end up with poor results from your model.

Closing Remarks

We didn’t see a clear winner on election night, but we had patience. We changed the process for how we voted, but we avoided spreading the virus. We saw some groups vote in unexpected ways, but we learned more about how people feel about the issues. Each one of these events comes with its own lesson. All of these lessons can easily be applied to you work, whether that is in data science, programming, or something completely unrelated. The important part is that we saw things that we liked or disliked, and found ways to turn them into learning experiences.