Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: The Amateur Data Scientist and Her Projects
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > The Amateur Data Scientist and Her Projects
AnalyticsBig DataCulture/LeadershipData Management

The Amateur Data Scientist and Her Projects

vincentg64
vincentg64
5 Min Read
SHARE

With so much data available for free everywhere, and so many open tools, I would expect to see the emergence of a new kind of analytic practitioner: the amateur data scientist.

With so much data available for free everywhere, and so many open tools, I would expect to see the emergence of a new kind of analytic practitioner: the amateur data scientist.

Just like the amateur astronomer, the amateur data scientist will significantly contribute to the art and science, and will eventually solve mysteries. Could the Boston bomber be found thanks to thousands of amateurs analyzing publicly available data (images, videos, tweets, etc.) with open source tools? After all, amateur astronomers have been able to detect exoplanets and much more.

Also, just like the amateur astronomer only needs one expensive tool (a good telescope with data recording capabilities), the amateur data scientist only needs one expensive tool (a good laptop and possibly subscription to some cloud storage/computing services).

More Read

Automated Valuation Models
Experts: Location Intelligence unlocks the power of your data
Using predictive analytics for fantasy football
Technical Analysis is Changing Quickly in the Era of Big Data
Is LinkedIn One Step Away from Becoming the World’s Largest Performance Management System?

Amateur data scientists might earn money from winning Kaggle contests, working on problems such as identifying a Bonet, explaining the stock market flash crash, defeating Google page-ranking algorithms, helping find new complex molecules to fight cancer (analytical chemistry), predicting solar flares and their intensity. Interested in becoming an amateur data scientist? Here’s a first project for you, to get started:

First project: Do large meteors cause multiple small craters or a big one?

If meteors usually break up into multiple fragments, or approach the solar system already broken down into several pieces, they might be less dangerous than if they hit with a single, huge punch. That’s the idea, although I’m not sure if this assumption is correct. Even if the opposite is true, it still worth asking the question about frequency of binary impact craters.

data science

About to hit Earth

Eventually, the knowledge that meteorites arrive in pieces rather than intact could change government policies and priorities, and maybe stop spending money on projects to detect and blow up meteors (or the other way around).

So how would you go about estimating the chance that a large meteor (hitting Earth) creates multiple small impacts? And how many impacts on average: 2, or 3? An idea consists in looking at Moon craters and checking how many of them are aligned. Yet what causes meteors to explode before hitting (and thus creating multiple craters) is Earth’s thick atmosphere. Thus, Moon would not provide good data. Yet Earth’s crust is so geologically active that all crater traces disappear after a few million years. Maybe Venus would be a good source of data? Nope, even worse than Earth. Maybe Mars? Nope, just like Moon. Maybe some moons from Jupiter or Saturn would be great candidates.

analytics

Double impact, seen million years later

Once a data source is identified and the questions answered, deeper questions can be asked, such as: when we see a binary crater (two craters, same meteor), what is the average distance between the two craters? This will also help better assess population risks, and how many billion dollars NASA should spend on meteor tracking programs.

In any case, as a starter, I did a bit of research and found the following data, with a map showing impact craters on Earth.

Visually, with the naked eye, it looks like multiple impacts (e.g. binary craters), and crater alignment, is the norm, not the exception. But the brain can be very lousy at detecting probabilities. So a statistical analysis is needed. Note that the first step consists in processing the image to detect craters and extract coordinates, using some software or writing your own code. But this is still something a good amateur data scientist could do, I’m sure you can find the right tools on the Internet.

A better idea might be to use some public data I published a while back: it is more comprehensive, already in Excel format, and also has dates attached to craters, so that binary craters created on the same year are likely to be true twins (that is, coming from a single piece of space rock that broke into two fragments sometimes in the past, before hitting Earth). 

Google ‘binary craters on Earth’ for additional info.

 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data migration risk prevention
Best Approach to Risk Management for Data Migration in Data-Driven Businesses
Big Data Data Management Exclusive Risk Management
AI in branding
How Data Analytics and Data Mining Strengthen Brand Identity Services
Big Data Exclusive
Hidden AI, a risk?
Hidden AI, Real Risk: A Governance Roadmap For Mid-Market Organizations
Artificial Intelligence Exclusive Infographic
unusual trading activity
Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
Analytics Exclusive Infographic

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Visualize Your Data

5 Min Read
big data transfer online
AnalyticsBig DataExclusive

Comparative Analysis of Two Top Big Data Transfer Services

7 Min Read

How Google Uses R to Make Online Advertising More Effective

4 Min Read
Image
Predictive Analytics

How to Boost Your Sales with Big Data

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?