Python for Decision Makers and Business Leaders Transcripts
Chapter: Data science in Python
Lecture: Python and data science
0:00 You should be aware by now that Python and data science
0:03 go together so well
0:05 and it's one of the fastest growing parts of Python.
0:09 In fact, in that survey I showed you
0:10 in the web development side
0:12 the Python software Foundation survey along with JetBrains
0:15 they also ask, Are you a web developer
0:18 a data scientist, or other?
0:20 Web developer and data scientist, those were about equal
0:23 so this is a huge part of Python
0:25 and I think this is one of the areas
0:27 as I mentioned in the full spectrum section
0:30 that is really unique for Python.
0:32 People can come from other disciplines
0:34 from say science, or engineering, or economics
0:38 and they can come into Python
0:39 and they can do this type of work
0:41 that we're about to talk about
0:42 because Python is so easy to get started.
0:45 But they can do real honest to goodness work
0:48 that has incredible implications
0:50 as far out to the boundaries of science as you want to think.
0:54 How far out? Well, what if we're using telescopes?
0:57 Yes, astronomers all over the world
0:59 use Python to study all sorts of things.
1:02 To study stars, to look for exoplanets
1:05 there's a whole bunch of libraries.
1:08 One of them, the most popular one, is called Astropy.
1:11 So this is a whole project
1:12 for just doing astronomy with Python.
1:15 What was the biggest discovery in astronomy recently?
1:18 Well, as a non-astronomer, just looking in from the outside
1:22 it was probably the fact
1:23 that the very first picture of a black hole was taken.
1:27 This is not as easy as it sounds.
1:30 This doesn't just mean better cameras that can focus more.
1:34 There's all sorts of reasons that it's very hard
1:36 to actually get a clear, true picture
1:39 of what a black hole looks like.
1:40 So the people worked on this project
1:43 they actually used machine learning to try to figure out
1:46 what the real image looked like
1:48 and tried to piece it back together from a bunch of parts
1:51 with many interpretations
1:52 but they taught the machine learning model
1:53 what the most likely outcome was, and they recreated it.
1:58 They use a whole host of libraries
2:00 that are from the data science side of Python
2:02 NumPy, SciPy, pandas, Jupyter, Matplotlib
2:06 and Astropy, of course.
2:09 This is just one discipline, and one huge discovery.
2:12 It would not surprise me if a Nobel Prize
2:15 came out of this work done with Python.
2:18 But speaking of Nobel Prizes
2:19 people did win the Nobel Prize
2:21 for finding the Higgs boson at the Large Hadron Collider.
2:24 Yeah, Python was also involved in that as well.
2:27 Data science and Python, they go really well together
2:30 and it's partly because of this full spectrum language.
2:32 It's so easy to come in as a non-developer
2:34 pick up the little bits you need
2:36 and just keep going, and going, and going
2:38 until you've all of a sudden
2:39 used machine learning and artificial intelligence
2:41 to recreate the picture of a black hole. Wow.