Is Learning Python Still Worth It In 2024? | Professor's Take
Summary
TLDRDespite the hype around new technologies like GPT and AI tools, learning Python remains valuable in 2024, especially for data science and machine learning. Its ease of learning, strong community support, and extensive libraries make it a powerful tool. Fundamental coding skills are still essential, and Python provides a solid foundation to leverage new tools effectively.
Takeaways
- 🤖 Despite the rise of AI tools, learning Python is still valuable due to its versatility and powerful applications, especially in data science and machine learning.
- 🧑🏫 The speaker, having experienced several tech hype cycles, suggests that the current AI hype might not be as transformative as it is portrayed.
- 🚗 Past predictions about autonomous vehicles, medical AI, and blockchain replacing traditional systems have often been overstated in terms of their immediate impact.
- 💻 Python is a general-purpose language, not the best for every task, but it excels in machine learning and deep learning, making it a required skill for many data jobs.
- 📚 Python's syntax is designed for readability and ease of use, making it an excellent first programming language to learn.
- 🤔 The speaker emphasizes that programming languages are tools, and the choice of language should be based on the problem being solved, not a lifelong commitment.
- 🔍 Python has a strong community support, with abundant tutorials, training materials, and well-documented packages, which are crucial for learning and debugging.
- 🚀 Python's performance issues, such as its weak runtime and lack of native parallelism, are being addressed through new developments like the Mojo programming language.
- 🌐 The speaker's personal experience with multiple programming languages illustrates that programmers often learn new languages to solve different problems more efficiently.
- 🛠️ New languages and tools are created to increase efficiency and solve specific problems, but they do not replace foundational coding skills; instead, they enhance them.
- 🌟 The fundamentals of coding and problem-solving will always be in demand, and learning Python provides a solid foundation to leverage new tools effectively.
Q & A
Why are people questioning the value of learning Python today?
-People are questioning the value of learning Python due to the rise of AI tools and opinions from prominent figures like Nvidia CEO Jensen Huang, who suggests that computing technology should evolve to the point where no one has to program.
What is the speaker's perspective on the hype around AI and its impact on programming?
-The speaker believes that the hype around AI is similar to previous tech hype cycles and that the impact is often less dramatic than anticipated. They argue that coding will change but will not become obsolete, and that AI tools are not replacements for foundational coding skills.
What is the speaker's view on the future of coding and AI tools?
-The speaker thinks that coding will evolve and change, and people will use different tools in the future. However, they emphasize that foundational coding skills are essential and that AI tools are enhancements rather than replacements for coding.
Why is Python considered a general-purpose language?
-Python is a general-purpose language because it has a variety of capabilities, though it is not the best tool for most tasks. It can be used for designing games, web and mobile apps, automating processes, but there are often better-suited tools and languages for these tasks.
What makes Python particularly useful in the field of machine learning and deep learning?
-Python is particularly useful in machine learning and deep learning due to its extensive libraries and packages like pandas, numpy, scikit-learn, TensorFlow, and PyTorch, which have no rivals or equivalents in other programming languages.
Why is Python's syntax considered advantageous for beginners?
-Python's syntax is advantageous for beginners because it focuses on readability and ease of use, making it a great first language to learn. It allows for quick achievement of tasks, providing positive feedback that motivates learners to continue.
What are the main drawbacks of Python according to the speaker?
-The main drawbacks of Python mentioned by the speaker are its weak performance compared to other languages and its lack of native parallelism. Optimizing Python code for runtime or memory performance requires a lot of effort and deep knowledge.
What is Mojo, and how does it aim to address Python's limitations?
-Mojo is a new programming language under development that aims to combine the usability of high-level languages like Python with the performance of low-level languages like C and C++. It is being developed to address Python's limitations in performance and parallelism.
How does the speaker's experience with multiple programming languages inform their perspective on learning new languages?
-The speaker's experience with multiple programming languages, including Pascal, Basic, C, IDL, Fortran, and Python, shows that one doesn't choose a programming language for life. Languages are tools chosen based on the problem at hand, and learning new languages becomes easier once you are familiar with one.
What is the speaker's final verdict on the relevance of learning Python in 2024?
-The speaker concludes that learning Python is still worth it in 2024. Despite the hype around new technologies, Python remains a valuable and powerful tool, especially in data science and machine learning, due to its ease of learning, strong community support, and extensive libraries and resources.
Outlines
🤖 The Hype and Reality of Python in Tech
This paragraph discusses the skepticism around the continued relevance of learning Python amidst the rise of AI tools and the opinions of tech leaders like Nvidia's CEO, Jensen Huang. The speaker, drawing from personal experience with multiple tech trends, suggests that the impact of new technologies is often less dramatic than the hype. They argue that while headlines suggest coding may become obsolete due to AI, this is unlikely. Python's adaptability and its role in machine learning and data science, supported by a strong community and extensive libraries, ensure its continued dominance and relevance.
🛠 The Versatility and Limitations of Python
The speaker elaborates on Python's role as a general-purpose language, noting that while it has various capabilities, it is not the best tool for every task. They highlight Python's strengths in machine learning and data science, where it has become the de facto language due to its extensive libraries and packages. The paragraph also touches on Python's ease of learning and strong community support, which makes it an excellent first language for many. However, the speaker acknowledges Python's performance limitations compared to other languages and the challenges of optimizing Python code for performance. They also mention ongoing efforts to address these issues, such as the development of new programming languages like Mojo.
Mindmap
Keywords
💡Python
💡Jensen H
💡Tech Hype
💡Machine Learning
💡Readability
💡Community Support
💡Performance
💡Mojo
💡Problem Solving
💡GenI
Highlights
Debates on the value of learning Python persist due to the rise of AI tools and opinions from industry leaders like Nvidia's CEO, Jensen Huang.
The speaker, a 40-year-old experienced in multiple tech trends, doubts the current hype around AI tools replacing coding.
Historically, the impact of tech advancements has often been less dramatic than the hype suggests.
CEOs are tasked with selling visions of the future rather than delivering fully realized products.
Coding is unlikely to become obsolete; current headlines about AI writing code are just another passing hype.
Python is a general-purpose language with a variety of capabilities but not the best at most tasks.
Python excels in machine learning and deep learning, making it a required skill for many data jobs.
Python's syntax prioritizes readability and ease of use, making it an excellent first language to learn.
The speaker contrasts their difficult experience with C to the ease of learning Python.
Python's community support and extensive documentation make it an accessible language for learners.
Python's main drawbacks are its weak performance and lack of native parallelism.
New programming languages like Mojo are being developed to combine usability with high performance.
The speaker's experience with multiple programming languages illustrates the transient nature of tech tools.
Programming languages are tools chosen based on the problem at hand, not lifelong commitments.
Learning one programming language makes learning others easier due to the transferability of coding concepts.
The creation of new languages and tools is driven by the programmer's goal to increase efficiency.
AI and advanced tools are enhancements to coding, not replacements, and should be seen as aids to work smarter.
Despite technological evolution, the fundamentals of coding and problem-solving remain in high demand.
Python is still worth learning in 2024 for its value in data science and machine learning, ease of learning, and strong community support.
Transcripts
many people including my students are
asking whether learning python is still
worth it today because of the rise of ji
tools and the opinions of prominent
figures like Nvidia CEO Jensen H it is
our job to create Computing technology
such that nobody has to
program I am 40 years old I know I know
I don't look a day over 30 thank you
very much my age is relevant because I
have experienced on several Tech Hypes
and I have no reason to believe that the
geni hype is any different from the
previous ones for example since the
early 2000s I have heard that fully
autonomous self-driving cars would soon
take over the roads eliminating the need
for driver's licenses in the 2010s I
heard that Radiologists would become
obsolete because all our medical images
will be analyzed and diagnosis made by
Machine learning models we were also
told that crypto and blockchain would
replace the traditional banking system
while technological advancements and new
Innovations have certainly changed our
lives over the past few decades the
impact is often less dramatic than the
hype would have you believe keep in mind
that CEO's jobs are to sell an idea or a
vision for the future to you and to the
investors not a fully formed product
whether that idea will eventually become
reality is a whole different question
question all the eye-catching headlines
about coding becoming obsolete because
of ji will write code for us I don't
think that's going to happen it is just
another hype that will eventually quiet
down coding will certainly change don't
get me wrong and people will use
different tools a few years from now
than today but that has always been the
case tools even programming languages
come and go and I will come back to this
point after the next
mon python is a general purpose language
this means that while python has a
variety of capabilities it is not the
best tool for most of them you can
design games and web and mobile apps you
can automate processes like web scraping
and data collection however it is not
great at most of these things you can
certainly find tools and languages
better suited for each of the tasks I
have listed where python really shines
is in machine learning and deep learning
which is why python is a required skill
for most data jobs for some reason
python has become the most popular
programming language in the machine
learning community over the past decade
packages like pandas mot Le numai psyit
learn caros tensorflow pytorch and many
others have no rivals or equivalents in
other programming languages ensuring
that python will remain the dominant
language of data science for at least
the next couple of years python syntax
focuses on readability and ease of use
making it a first great language to
learn I learned C at University and
honestly it was a nightmare my brain
quite literally melted when we learned
about pointers that point to pointers no
offense to any cords out there but what
the fork is that about it took me a
semester to get to a level where I could
read in a CSV file and Implement simple
numerical methods in contrast these
tasks are much easier in Python learning
python is easy because you quickly
achieve tasks providing positive
feedback that motivates you to keep
going python also has strong Community
Support there are a ton of tutorials and
training materials available most
packages are very well documented as
well stack Overflow or geni tools are
great resources to help you debug your
code I've been coding python for more
than 5 years by now and I haven't come
across a problem that hasn't been solved
by someone else on stack overflow before
the main cons of python are that its
performance is weak compared to other
languages and it is not natively
parallel while it's easy to write code
it takes a lot of effort and deep
knowledge to optimize for runtime or
memory performance there is a lot of
active research and development going on
to address these cons one example is
Mojo Mojo is a new programming language
under development that aims to combine
the usability of highlevel language like
python with the performance of lowlevel
languages like C and
C++ as much as I hate it let's talk
about my age again I have used more than
half a dozen different programming
languages so far and I'm quite certain I
will learn a few more in my life I
started coding in elementary school back
in the 1990s using a language called
Pascal or basic I think my memory is a
bit hazy about that I didn't do much
coding in high school but as I said I
learned some C at University back when I
was an astronomer I first us a language
called IDL which is short for
interactive data language which was huge
in astrophysics back in the day lots of
packages to work with and visualize
astral data were developed in that
language then I used forrun during my
PhD because I worked on novel numerical
algorithms so I needed a lowlevel and
computationally efficient language then
I switched to python during my postdoc
years so I could take advantage of
packages like pandas numpy and Muffet
lip and later of course the mer packages
like psychic learn and others while I
use these three languages the most I
also worked a bit with r metlab SQL over
the years this shows that you don't
choose a programming language for Life
programming languages are tools and you
choose the tool that's best to solve the
problem you are working on if the
problem happens to be machine learning
python should be your choice if your
goal is something else you might need to
find a better language the great thing
about programming in general is that
once you are familiar with one language
it will be so much easier to learn a
second or third language coding Concepts
don't really change from one language to
another the main challenge will be to
learn the new
syntax programmers are some of the
smartest and laziest people and I really
mean that the goal of programmers has
always been to make their work easier
that's why new languages and tools are
being developed all the time one of the
main drivers behind the creation of new
programming languages and tools is to
increase efficiency the whole point of
software development is to solve
problems and automate tasks making life
easier for people and businesses new
languages and tools are created to
address specific needs and gaps that
existing ones cannot fill this constant
evolution is part of the beauty of the
tech industry it never stands still gen
and other Advanced tools are just the
latest in a long line of Innovations
aimed at increasing efficiency however
they are not replacements for
foundational skills like coding instead
they are enhancements that can help us
work smarter and faster knowing how to
code especially in versatile and
Powerful languages like python gives you
a solid foundation to leverage these new
tools effectively
[Music]
so is learning python still worth it in
2024 absolutely despite the hype around
new technologies python remains a
valuable and Powerful tool for many
applications especially in data science
and machine learning it's easy to learn
has a strong community and it's backed
by a wealth of libraries and resources
while the tech landscape languages and
tools will continue to evolve the
fundamentals of coding and problem
solving will will always be in demand if
you enjoy this video click the video on
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Sciences for you happy coding
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