11 Surprising Jobs That Use Data Science
From driving your car to work to choosing a book to read, the presence of data in our reality is undeniable. You see this in the increasing importance of data in almost any job, also in the ones that you wouldn't expect at first. As a result, many people are looking to gain data fluency both to analyze the data and understand it.
That's why we have compiled a list of 11 surprising jobs in which data science plays an increasingly important role.
Do you think it's time to build your data science skills? Check out DataCamp and start for free!
You might not immediately expect it, but farmers use data and data science to decide how much water, fertilizer, and other inputs are needed to grow and harvest the best crop. But even before actually growing the crops, farmers make use of solutions that help them to plant the right amount of seeds in the field so that they can get the most out of their seeds.
Similarly, farmers also depend on the weather forecast, just like you! While you use the information gathered from meteorologists' analysis of data to decide what you will wear tomorrow, farmers interpret the information to make decisions that affect the quality of their harvest. Think, for example, about winter times, where the frost might destroy all of the plants if farmers don't undertake any action.
If you want to read more about how you can use data science to analyze weather data, go here.
2. Professional Athletes
It's no joke when you hear that data is being used to win games and to make more money in sports. It's now even a requirement! To better understand how much of an impact data is making in sports, just know that retired NBA champion Shane Battier noted that data was “extremely significant” and “changed the course of his career”.
Yup, that much of an impact!
Professional athletes, their teams and the people that surround them make use of data and this for sports analytics, and injury prevention and prediction. With sports analytics, these people use and collect relevant, historical, statistics that they can apply to gain a competitive advantage to a team or an individual. With sports analytics, coaches can gauge opponents and their tendencies. For example, you can study the performance of individual players or a team and then adjust your team composition or your own performance to play into the weaknesses of the player(s) you're facing. In short, the whole playbook of a game can be changed with sports analytics! On top of all that, personal physicians can use these historical statistics to get a complete study of the player they're trying to guide.
Would you like to get started with sports analytics yourself? Check out this course.
3. Software Engineers
Many software developers and data scientists often work side by side to build out and technical products and services that work with lots of data and that need a lot of software engineering to build and maintain. But at the same time, software developers are also learning data science to analyze their customers' data to better understand how they can improve the product that they're working on. The decisions and direction that are then taken in the product are then data-driven decisions rather than intuitions.
At the same time, the techniques that software engineers use to analyze customer data can also be used to answer questions such as "When will this project be ready to ship?", or "Who should fix this bug?", which will ultimately help to increase developers' efficiency.
Your favorite actors like Jennifer Lawrence and George Clooney are also affected by data, and maybe to a higher degree than you can imagine! For them, getting cast or not for movies is now also determined by data of previous movies or TV shows that they starred in: reviews on sites such as IMDB or Rotten Tomatoes, combined with data from fan pages or people's viewing data will help entertainment companies in deciding whether or not to make a movie with a certain actor.
Bye, bye box office flops!
An excellent example of the way data has affected actors is the House of Cards series on Netflix. Before outbidding top television channels like HBO and AMC to earn the rights for a U.S. version of House of Cards, they knew the following things about their viewers:
- A lot of users watched the movie "The Social Network", directed by David Fincher, from beginning to end.
- The British version of "House of Cards" had been well watched.
- Those who watched the British version "House of Cards" also watched Kevin Spacey films and/or films directed by David Fincher.
Because Netflix had a lot of users in all three of these factors, they were able to invest big and make sure that they owned this TV show and enlist the director and actor in question. After the entertainment company had made the initial investment, they also promoted the series via a "personalized trailer" for each type of Netflix member; In the end, Netflix made 10 different cuts of the trailer for House of Cards, each geared toward different audiences.
Ready to explore movie data yourself? Check out this course.
5. Police Officers
Even police officers can't escape data! They make use of it on a daily basis, to either prevent or stop crimes. How this is made possible? Well, can you imagine all the data that is generated from arrests, police reports, and 911 calls, for example? When you visualize all this data, you might be able to distinguish neighborhoods where crime is more prevalent than others. In these cases, police offers might slightly adjust their patrol routes so that they can more easily keep an out on the areas where crime is more likely to occur than others.
On another note, there's also the application ShotSpotter, which is a network of sensors that detects gunshots. The information from these senors is made available on a laptop that is placed between the front seats so that the officers have easy access to it and can interpret the data.
Lastly, data is also used to evaluate police officer's performance. The data from police reports can also be used to identify police officers that are more likely shoot than others.
What insights can you gather from visualizing crime data? Check it out on this page.
6. Flight Attendants
Very soon, flight attendants will be expected to know frequent flyers' birthdays, how they like their coffee, what they’re likely to buy on board, what kind of food they might want, or what types of music they want. On some airlines, flight attendants will have tablets that will show data on customers, including their allergies, seat preferences and whether the carrier lost their bags last trip.
That sounds great, right?
It's not yet a reality, but it is a fact that airlines have a tremendous amount of customer data and that they will be using all of that data to their advantage in the near future to improve the customer experience. Besides customers, companies such as Airbus have already been occupied with gathering even more data on flights: in 2020, they will hopefully launch their A380-1000 plane with 10,000 sensors in each wing. In the meantime, the current A350 model has a total of close to 6,000 sensors across the entire plane. The data of all these sensors is used to monitor the aircraft’s performance; Accordingly, performance adjustments can be made that could save millions on fuel bills. But even more than that, the sensor data might save lives by improving safety.
With about 5,000 commercial aircraft in the sky at any one time over the US alone, and 35 million departures each year, you can imagine that all of this is a lot of data! That's why there are already expert service providers, such as masFlight, that try to help airlines and airports to make sense of all this data: they aggregate weather information, departure times, radar flight data and submitted flight plans, and monitor 100,000 flights every day, so that operators can plan and deliver their services more efficiently!
7. Museum Directors
Even museums use data on a daily basis! If you think about it, it's only normal: museums have tons of data on visitors. The numbers of people that visit the museum, their movements throughout the building, the searches on the museum website, etc. All this is data. And with all of this data, you can easily generate some recommendations and share insights: they can promote exhibition tickets at quieter times, implement time-dependent ticketing pricing, see where visitors stop frequently and adjust audio-guided tours accordingly, ...
With the help of data, museums are able to improve customer experience and discover new opportunities to generate revenue.
Lawyers, seriously?! Yes, data is changing the way even lawyers go about their work! It might be easier to understand how and why this is possible if you know that the legal system generates a huge and ever-increasing amount of data: each new case brought to court increases the body of knowledge that a lawyer has to get to grips with to do his or her job. For your reference, there are about 350,000 cases in the US alone each year.
That's a whole lot of data!
Just like you see in the movies or TV series, each judicial ruling, precedent or interpretation of legislature creates data and among all of the witness statements, court logs, judge's summaries, are all hidden facts and insights that could help legal arguments. Lawyers often need to look through these records manually, but recently, there are applications that help to do this in a more automated way.
On the one hand, you have databases such as LexisNexis and Westlaw, which have huge amounts of case details. These are often used by legal researchers and they're much like search engines. But if lawyers want to get insights and connections using advanced analytical algorithms, you need other applications, such as Judge Analytics, a service by Ravel Law. This lets lawyers search through all decisions that were ever made by a particular judge. That way, they can find those judges that are most likely to be sympathetic to their arguments.
Of course, besides these tools, which will already save a lot of paper and space in closets, there are more general ones that are generally focused on billing, time management, marketing and customer relations functions, which is much in line with what you'll typically find in other industries as well.
To help veterinarians, some high-tech startups are creating wearables for pets: that way, they can track vital signs that could indicate illnesses. What's more, vets will be able to diagnose quicker: if a pet's heart or respiration is abnormal, the wearables could help vets (and owners!) to recognize a problem sooner. On a more general note, the pet's daily movements and eating habits could lead to immediate insights for vets to help your pet get healthier.
You see that this solution is much like human wearables that track calories and measure activity levels, like the ones that are used by professional athletes! Data definitely helps to make animal's lives better and can even save owners some money.
Want to get started on tracking your pet's movements? Check out this course.
You might not have guessed it, but journalists are increasingly experiencing the influence of data on their jobs. There's a popular trend -called "Data Journalism" or "Data-Driven Journalism"- where stories and the entire workflow of journalists are driven by data, from data analysis to visualization and storytelling.
This data-driven approach to journalism also means that data is used to assess journalists' and articles' performance: click through ratings, likes, and shares on social media, ... It's all being used to incentivize writers to create content that really targets the readership of the magazine, newspaper or website for which journalists are writing.
Doctors easily benefit from data fluency, and this in several ways.
Firstly, data helps doctors to diagnose patients quicker and more accurately, and thus make faster decisions that can save lives. Something that helps in this quest is the emergence of wearables and applications that monitor humans on a daily basis, which help to prevent health problems. Some examples are Fitbit, Strava, etc. With all of the data
Data also helps to advance pharmaceutical research: it helps the search to find a cure for cancer and Ebola, for example. There are already startups such as BERG Health and Atomwise that have contributed tremendously to this search. The former, on the one hand, used machine learning algorithms to extract and analyze biological samples from over a thousand patients. As a result, they developed a drug that detects and triggers the natural death of cells damaged by cancer. The latter startup, on the other hand, used virtual models and neural networks to evaluate how 7,000 existing drugs interact with the Ebola virus.
It's clear that there are many more jobs where data science plays a role. In this list of 11 jobs, you have seen many other professions mentioned. How is data changing your job? Let us know your thoughts on Twitter via @DataCamp.