33 unusual problems that can be solved with data science

Here is a non-exhausting list of curious problems that could greatly benefit from data analysis. If you think you can't get a job as a data scientist (because you only apply to jobs at Facebook, LinkedIn, Twitter or Apple), here's a way to find or create new jobs, broaden your horizons, and make Earth a better world not just for human beings, but for all living creatures. Even beyond Earth indeed. Help us grow this list of 33 problems, to 100+.

The actual number is higher than 33, as I'm adding new entries.

Figure 1: related to problem #33

33 unusual problems that can be solved with data science

  1. Automated translation, including translating one programming language into another one (for instance, SQL to Python - the converse is not possible)
  2. Spell checks, especially for people writing in multiple languages - lot's of progress to be made here, including automatically recognizing the language when you type, and stop trying to correct the same word every single time (some browsers have tried to change Ning to Nong hundreds of times, and I have no idea why after 50 failures they continue to try - I call this machine unlearning
  3. Detection of earth-like planets - focus on planetary systems with many planets to increase odds of finding inhabitable planets, rather than stars and planets matching our Sun and Earth
  4. Distinguishing between noise and signal on millions of NASA pictures or videos, to identify patterns
  5. Automated piloting (drones, cars without pilots)
  6. Customized, patient-specific medications and diets
  7. Predicting and legally manipulating elections
  8. Sport bets
  9. Predicting oil demand, oil reserves, oil price, impact of coal usage
  10. Predicting chances that a container in a port contains a nuclear bomb
  11. Assessing the probability that a convict is really the culprit, especially when a chain of events resulted in a crime or accident (think about a civil airplane shot down by a missile)
  12. Computing correct average time-to-crime statistics for an average gun (using censored models to compensate for the bias caused by new guns not having a criminal history attached to them)
  13. Predicting iceberg paths: this occasionally requires icebergs to be towed to avoid collisions
  14. Oil wells drilling optimization: how to digg as few test wells as possible to detect the entire area where oil can be found 
  15. Predicting solar flares: timing, duration, intensity and localization
  16. Predicting Earthquakes
  17. Predicting very local weather (short-term) or global weather (long-term); reconstructing past weather (like 200 million years old)
  18. Predicting weather on Mars to identify best time and spots for a landing
  19. Predict riots based on tweets
  20. Designing metrics to predict student success, or employee attrition
  21. Predicting book sales, determining correct price, price elasticity and whether a specific book should be accepted or rejected by a publisher, based on projected ROI
  22. Predicting volcano risk, to evacuate populations or cancel flights, while minimizing expenses caused by these decisions
  23. Predicting 500-year floods, to build dams
  24. Actuarial science: predict your death, and health expenditures, to compute your premiums (based on which population segment you belong to)
  25. Predicting reproduction rate in animal populations
  26. Predicting food reserves each year (fish, meat, crops including crop failures caused by diseases or other problems). Same with electricity and water consumption, as well as rare metals or elements that are critical to build computers and other modern products.
  27. Predicting longevity of a product, or a customer
  28. Asteroid risks
  29. Predicting duration, extent and severity of draught or fires
  30. Predicting racial and religious mix in a population, detecting change point (e.g. when more people speak Spanish than English, in California) to adapt policies accordingly
  31. Attribution modeling to optimize advertising mix, branding efforts and organic traffic
  32. Predicting new flu viruses to design efficient vaccines each year
  33. Explaing hexagonal patterns in this Death Valley picture (see Figure 1)
  34. Road constructions, HOV lanes, and traffic lights designed to optimize highway traffic. Major bottlenecks are caused by 3-lanes highways suddenly narrowing down to 2-lanes on a short section and for no reasons, usually less than 100 yards long. No need for big data to understand and fix this, though if you don't know basic physics (fluids theory) and your job is traffic planning / optimization / engineering, then big data - if used smartly - will help you find the cause, and compensate for your lack of good judgement. These bottlenecks should be your top proprity, and not expensive to fix.
  35. Google algorithm to predict duration of a road trip, doing much better than GPS systems not connected to the Internet. Potential improvement: when Google tells me that I will arrive in Portland at 5pm when I'm currently in Seattle at 2pm, it should incorporate forecasted traffic in Portland at 5pm: that is, congestion due to peak telecommuting time, rather than making computations based on Portland traffic at 2pm. 

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Comment by Alexandre Rodichevski on September 30, 2016 at 12:32am

Vincent, you can rename your article in "33+ unusual problems that can be solved with data science".

I think the most of the problems in the list is already conducted by someone.

You can add to the list the nutrition analysis based on the supermarket bills accumulated by a person in one year. The data scientist should ask the supermarket administration to extract in the electronic form the bills (with details on acquired products) associated with his fidelity card.

Comment by J. Patrick McDonald on September 3, 2016 at 10:12am

Here are some that I've addressed over the course of my career.  Not all data science, but many were and all fall within advanced analytics. 

50 Business Problems I've Addressed with Advanced Analytics

Comment by Samson Sani Nzevela on September 5, 2014 at 2:51am

Great opportunities! Your line of thinking about data analysis and ...

Nagaraj Kulkarni, you are invoking an interesting science - politic...

Gary D. Miner, Ph.D. I am in a team almost 30% developing HMIS app taking in all that...genomics..

Comment by Nagaraj Kulkarni on September 1, 2014 at 7:34am

Thanks for exhaustive list for data science and made me think few the followings:

Predicting national coup in a country

Prediction for which country will win more medals in the Olympic/ film for the Oscar/ the Nobel prize

Comment by Gary D. Miner, Ph.D. on August 29, 2014 at 9:08am

Here is your Number 34:    Predicting, with high accuracy, personalized medical events, diagnoses, and treatments  -  tailored for the environmental and genetic factors of the INDIVIDUAL ........ (this is coming, and already happening in limited fashion with certain illnesses, certain forward thinking medical doctors, and under the right conditions .... but has 95% of the way yet to go to be done well .....)


Comment by Chris on August 29, 2014 at 8:28am

Love your works, this article is typical of yours. You make data overgrow the traditional computing four dimensions, perception, information, reasoning and machine learning ( your #2 gives me a chuckle!), #31 is more or less data merging and yes! #31 is also in this site http://www.livescience.com/47591-ibm-watson-science-discoveries.htm...

Shine on! Dr. Vincent!


Comment by Livan Alonso on August 29, 2014 at 4:20am

Thanks Vincent, great list to work on !

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