Python for Decision Makers and Business Leaders Transcripts
Chapter: Welcome to the course
Lecture: What we are going to cover
Login or
purchase this course
to watch this video and the rest of the course contents.
0:00
Let's spend a minute and talk about the details
0:02
of what we're going to cover in this course.
0:05
We're going to start out by talking about
0:06
what you can build with Python
0:08
and we're going to look at it from two perspectives.
0:11
What are the general types of applications
0:13
that you could build with Python
0:14
say, machine learning and artificial intelligence
0:17
and what are some concrete things
0:19
people have built with Python
0:21
companies that you probably know.
0:23
I think it's very compelling to see
0:24
a company in your industry has been successful with Python.
0:28
That could help make the decision that yeah
0:30
maybe that's working out.
0:31
We're going to see that Python means three things
0:34
not just one.
0:35
It's a language, it's a set of libraries
0:37
and it's an entire ecosystem
0:39
and it's important to identify which you're talking about
0:43
when you're speaking with somebody
0:44
comparing another technology. If you say
0:47
I think the Javascript language does this
0:49
and the Python language does that,
0:51
you need to know what they're talking about.
0:53
If they say, Should we adopt Python?
0:54
I heard it's really fast.
0:56
Well, what does that mean?
0:58
Does that mean it's computational fast?
1:00
It's fast to market?
1:01
It's got a lot of libraries so we can build things quickly?
1:05
What does it mean, right?
1:06
So knowing the three things
1:07
that people talk about when they say Python
1:09
and being able to identify which those are
1:12
really helps with the conversation.
1:14
Then we're going to dig into a couple specific technologies.
1:17
We'll start with web development.
1:19
Python is great for web development.
1:20
There's a bunch of options there.
1:22
So we're going to go and check out
1:23
some Python web apps and frameworks
1:25
help you decide is that the right one for you
1:27
is that the right space at all for you
1:28
and if it is
1:29
which of the Python frameworks are good choices.
1:32
Data science is probably the place
1:34
where Python has the biggest lead over other technologies
1:38
so we're of course going to spend a little time
1:39
talking about data science and Python.
1:41
Also see that Python is really good at testing
1:44
not just testing Python code
1:46
but testing hardware testing other languages
1:49
so we're going to talk a little bit about testing with Python.
1:52
And then we're going to compare it
1:53
against other similar ecosystems
1:55
Python versus, say, .NET. How do these compare?
1:59
What are the advantages of .NET over Python
2:01
and what are the advantages of Python over .NET?
2:03
And .NET, by the way
2:05
is probably a fairly decent stand-in for Java.
2:07
They kind of play in the same space.
2:09
I know Java developers are not necessarily .NET developers
2:12
but the capabilities of those two
2:14
are pretty similar these days.
2:16
In the scientific computing world
2:18
MATLAB is really important.
2:19
Python has been making big inroads into that community.
2:23
Should you use some proprietary thing like MATLAB
2:25
or some open source thing like Python?
2:27
We'll talk about that.
2:28
And finally, Python with C++
2:30
not necessarily versus. So you might decide
2:33
should I use Python or should I use C++
2:36
or should I use them together?
2:37
They actually go together pretty well.
2:39
If you're going to switch to Python
2:41
you need to know that you can hire developers.
2:43
It's one thing to have a really cool language.
2:45
It's another to be able to say
2:47
and there's a bunch of people I can just reach out
2:49
to work with on this. Can I find consultants?
2:52
Can I find capable full-time employees, maybe co-founders?
2:55
Whatever it is that you need
2:57
knowing whether or not the job market
2:59
and the number of developers out there
3:01
is a good fit for what you're going to need
3:02
well, we're going to talk about that.
3:04
And finally, we're going to finish talking about
3:07
you've seen all this amazing stuff in Python
3:09
but when not to Python. Yeah, I'm making it a verb.
3:12
There are some areas and some situations
3:14
where it doesn't make much sense to use Python.
3:17
We'll talk about that at the end of the course.
3:18
And that's it. This is what we're going to cover.
3:21
It's going to give you a really great broad view
3:23
of the Python ecosystem.