Why I'm placing a lot more focus on learning Python....and how I'm doing it
Summary
TLDRIn this insightful video, Sam emphasizes the growing importance of enhancing data skills, particularly in Python, due to its prevalence in AI systems and automations. He shares his journey of learning Python for its versatility and value addition, recommending it for others. Sam demonstrates using Google Collab Notebook for data analysis, showcasing how to load data, perform statistical summaries, and visualize insights with Python libraries. He also discusses the ease of learning Python with AI assistance, error evaluation, and the significance of understanding Python for grasping AI automation workflows.
Takeaways
- 🚀 The importance of improving data skills, especially in Python, is highlighted as crucial for personal value and AI automations.
- 🐍 Python is emphasized as a versatile skill that is integral to many AI systems and automations currently being developed.
- 💡 The speaker has been diving deeper into Python recently, recognizing its potential for increasing personal value and enabling more capabilities.
- 🔍 Google Collab Notebook is introduced as a tool that can be used without extensive Python knowledge, focusing on operation and usage.
- 📈 The speaker demonstrates how to use Google Collab for simple data analysis, showcasing its ease of use and intuitive interface.
- 📊 The script includes a practical example of loading data into Google Collab, performing summary statistics, and visualizing data, emphasizing the efficiency of Python for data analysis.
- 📚 The use of AI systems like Chat GPT to generate random data sets for analysis is mentioned, highlighting the utility of AI in data exploration.
- 📉 The script discusses the benefits of using tools like Google Collab for data exploration before moving on to more detailed analysis in other platforms like Excel or PowerBI.
- 🔍 The speaker shares tips on using Google Collab, including error evaluation and code explanation features, to enhance learning and debugging.
- 🔄 The process of debugging and correcting code in Google Collab is demonstrated, showing that learning and mastering Python involves repetition and problem-solving.
- 🌐 The speaker concludes by expressing a commitment to mastering Python and AI automations, and plans to share more content on this journey.
Q & A
What is the main focus of the video script?
-The main focus of the video script is to emphasize the importance of improving data skills, particularly with Python, due to its versatility and relevance in AI systems and automations.
Why is Python considered a versatile skill in the context of AI?
-Python is considered versatile in AI because many AI systems and automations are developed and tested using Python code, making it crucial for understanding and interacting with these technologies.
What is the speaker's recommendation for those who haven't used Python before?
-The speaker recommends diving into Python, exploring its capabilities, and learning how to write and execute code, especially in the context of AI agents and automations.
What is Google Collab Notebook and how does it relate to Python coding?
-Google Collab Notebook is a tool that allows users to write and execute Python code in a browser. It is mentioned as a way to operate and use Python without needing extensive coding knowledge, making it accessible for data exploration and analysis.
What are some benefits of using Google Collab Notebook for data analysis?
-Google Collab Notebook enables quick data exploration and analysis, providing summary statistics and other insights with minimal code. It can be a useful step before creating more detailed reports or analyses.
How does the speaker use AI to generate a random dataset for analysis?
-The speaker uses an AI chat experience to generate a random dataset by providing an abstract of the data and the columns needed. This approach avoids data security issues while still allowing for meaningful analysis.
What is the significance of analyzing summary statistics in the given dataset?
-Analyzing summary statistics like mean, total, and average values provides a quick overview of key attributes in the data, such as passengers, distance traveled, stops, and fuel consumption, which can guide further detailed analysis.
How does the speaker plan to showcase the use of Python and AI in data analysis?
-The speaker plans to showcase the use of Python and AI in data analysis by building simple notebooks in Google Collab, demonstrating how to load data, perform calculations, and visualize results.
What is the speaker's approach to learning Python and AI for data analysis?
-The speaker's approach involves diving into new tools and methods, using AI systems to generate code and insights, and learning through repetition and practical application, such as building notebooks and analyzing data.
Why is the speaker interested in exploring the new framework called Autogen from Microsoft?
-The speaker is interested in Autogen from Microsoft because it represents a new framework for AI agent workflows and automation, which are increasingly being implemented using Python, and understanding these can enhance one's ability to work with AI systems.
Outlines
🐍 Embracing Python for Data Skills
Sam introduces the importance of enhancing data skills, particularly with Python, due to its prevalence in AI systems and automations. He emphasizes Python's versatility and its potential to increase personal value. Sam also highlights the usefulness of Google Collab Notebook for those unfamiliar with Python coding, suggesting that one can operate and utilize it effectively without extensive coding knowledge. He plans to demonstrate how to use these tools for data exploration and analysis, starting with a simple notebook in Google Collab using a random transportation dataset generated through an AI chat experience.
📊 Analyzing Transportation Data with Python
Sam demonstrates how to use Python and Google Collab to analyze a transportation dataset. He shows how to load data into a DataFrame, calculate summary statistics like total passengers, distance traveled, stops, and fuel consumption. He also explains how to calculate the average number of passengers per route and the total distance traveled per route using Python's Pandas Library. Sam further illustrates how to visualize data, such as total passengers per month and year, and total fuel consumed per month, using column charts. He emphasizes the ease of use and intuitiveness of Google Collab for building analyses and the importance of learning through trial and error.
🔍 Deepening Insights with Data Visualization
Continuing the data analysis, Sam discusses the process of visualizing total fuel consumption per month in a column chart, highlighting the need to adjust the format to display months and years side by side. He also touches on the use of error evaluation tools to understand and correct issues in the code. Sam then moves on to more complex analysis, such as investigating correlations between fuel consumption and distance traveled, using a heatmap. He stresses the importance of learning Python and understanding AI agent workflows, particularly with the new framework called AutoGen from Microsoft.
🚀 Advancing Skills with Python and AI
In the conclusion, Sam wraps up the session by emphasizing the need to master Python and AI workflows, particularly as they relate to automating analysis. He mentions his intention to produce more content on this topic, encouraging viewers to stay tuned. Sam reflects on the process of learning through repetition and the importance of understanding the automations happening behind the scenes in AI systems. He invites viewers to join him on this journey of mastering Python and AI, promising to share his learning experiences along the way.
Mindmap
Keywords
💡Data Skills
💡Python
💡AI Systems
💡Google Collab Notebook
💡Data Analysis
💡Pandas Library
💡Summary Statistics
💡Data Frame
💡Visualization
💡Correlation
💡Error Evaluation
Highlights
The importance of improving data skills, especially in Python, due to its use in AI systems and automations.
Python's versatility as a skill that can increase personal value and enable more capabilities in AI.
The recommendation for beginners to start learning Python for its role in AI and automation.
The capabilities of Python in writing, executing code automatically, and its use in loops for AI.
The significance of understanding Python for future possibilities in AI development.
Google Collab Notebook as a tool for data analysis without extensive Python knowledge.
The ease of using Google Collab for creating code, understanding it, and resolving errors.
The benefits of using notebooks for quick data exploration before creating reports or analyses.
A demonstration of loading data into Google Collab and performing summary statistics with Python.
The simplicity of obtaining summary statistics on key data attributes using one line of Python code.
The process of calculating the average number of passengers per route using Python's Pandas Library.
A tip on using the 'code explainer' feature in Google Collab for understanding specific code segments.
The creation of a column chart to visualize total passengers per month and year using Python.
Adjusting code to display data in the desired format, such as month and year on the x-axis.
Using error evaluation tools to quickly understand and resolve Python code errors.
Investigating correlations between fuel consumption and distance traveled with Python.
The use of a heat map to visualize the correlation between two data variables.
The speaker's commitment to mastering Python for understanding AI agent workflows and automating analysis.
Plans for releasing more content on Python and AI to help others learn and master these skills.
Transcripts
hey everyone Sam here what I think is
becoming more important now than than
ever has um
around how
to improve your data skills now one of
the one of the things I've been diving
into a lot more in recent months is uh
is python my python skills uh one one of
the reasons why is because a lot of the
AI systems a lot of the AI automations
that are being uh being developed and
being tested right now they're all being
run with python code right and so I I
realized this a while ago and I've been
diving into it a lot more than I ever
have in the past because I think it's
just a incredibly versatile skill that
is going to enable you to personally
increase your value and also do a lot
more that that is that is absolutely
true in my mind right so I've I've been
expanding my horizons a lot recently and
I and I really I really believe you
should as well and if you haven't done
much with python previously I really
recommend it you know I've been diving
into a lot of brand new things that you
can do with AI particularly with AI
agents it's all happening in Python
everything right the ability to write
code execute code automatically do this
on Loops it's it's incredible what is
what is possible now and what is going
to be possible in the future I don't
know exactly the direction it's going to
go but all I know is that a lot of this
has been done done within Python and
having a really solid understanding of
how python works is crucial right one
thing I'll say before we dive into this
uh Google collab notebook is that you
can um you can do a lot without knowing
how to write python code all you need to
know is more like how to operate it how
to use it right and there's a whole
range of tools now that can help you you
create the code understand the code
understand errors whole whole range of
things right and you know that's what
I'm my plan on show showcasing to you a
bit more today so okay how do we get
started now I'm just going to do some
simple analysis we're going to build a
simple notebook and Google collab here
and the reason why we're doing a
notebook here and you know and this is a
bit different to
say doing something in Excel or doing
something in powerbi but there are some
some real benefits to using these tools
even if they're not the end product that
you want to create they can with the
help of get you some you to do a lot of
like exploration around your data quite
quickly which I find very very useful
actually and so a couple of maybe like
an hour or two spent within here before
you actually do anything within a report
or or or within other analysis that you
do it actually I think can make a big
difference right okay so what I did
we've got some Transportation data here
I just created this it's totally random
data set generated it initially through
chat GPT a couple of weeks ago uh and
for me to just start off my analysis I
just put the data into our edner AI chat
experience here right okay so this is
this is our own one this is the one that
we've created through in but you can put
this in anything you mean you can put
this in chat gbt you can put this in
Gemini you can put it in co-pilot you
can put it in but put this sort of thing
anywhere I'm using our environment
because I just like it because I built
it exactly how I I want to use it
right
so I just gave it an abstract of the
data okay so I didn't actually give it
too much I just said I give it an
abstract and I said here are the columns
in my data set this is not the actual
data I want you to give me analysis the
reason why I'm doing it like this is
because I know there's a lot of data
security issues no one wants to you put
no one wants you to put your entire data
set into these AI systems well don't
just put a subset put in like one row
two rows at least give it the data
structure I don't see any data security
issues with that and you can still do a
lot or learn a lot uh on data which is
relevant to you
if you really can't even put like a row
or two go and just put the columns in
and get a random data set created by
chat gbt around those columns and then
put it into these into the system and
this it's still going to give you the
the right code the right ability
abilities to find good analysis right
okay so here's some analysis based on
your data set so route performance okay
so
let's so I'm just going to go through a
few of these to to create some analysis
I did did actually create a a bit of
analysis before you know what I've
already like actually what I've already
done is I've already loaded the data
into into Google collab so this is some
code that enables you to do that right
here right and just make this a little
bit bigger so I've loaded it in I've
taken the file from my computer and I
put it into the Google collab
environment it's built what's called a
data frame around that and then I've
already run some statistics so these
summary statistics right like these can
take some time to do elsewhere but
literally with one line of line of code
here which I I just got out of our AI
system it it gave me all the um summary
statistics on a whole range of key
attributes of the data so total
passengers distance traveled how many
stops um fuel consumption okay so if we
just look at the mean each route has 50
on average 50 passengers in the mean
27ks is the average distance traveled
average stops is 16 and the average fuel
consumption is 55 lers by the looks
I think that's how we can read it right
so yep cool okay so quick and easy to
get that information I might actually
might actually delete some of this stuff
and we'll just start from scratch right
cool
okay really easy to use Google collab
it's so intuitive you just you're
literally just building one piece of
analysis on top of the other and you can
do that by using code here you can also
add text very easily here so above shows
the
summary stats of our
transportation data so simple things
like that okay and then I can just click
this and that embeds it in and then I
can put some more code below here okay
right
the data frame is
already
available as
DF okay so I'm just going to say that
we've already got a data frame which has
a variable of DF so don't recreate
anything okay okay so sure you can
calculate the average number of
passengers per route using the Python's
Li the Panda's Library sorry here is the
code snippet okay average passengers per
route okay so let's copy this
across okay so all I have to do is copy
this in
here cool so there's a lot of routes
right so um so so this is just giving us
an idea so 100 okay so let's let's move
on to the next one so determined the
total distance traveled per route same
thing now this insight
to determine the total distance Trevor
per route in Python use Panda's Library
okay so again let's just come in here
distance total distance per
route okay and then I can just go
play cool okay here's a little tip for
you as well here's a little cool thing
that we uh you can do inside of here
what we have embedded into into here
which is quite cool is you can click one
click and go to the code explain so if
you want to get a little bit more detail
see it's not very descriptive here I can
go code explainer like this it will
paste that data in here automatically
and then I can go just run and it will
give me more detail about the spe
specific code I could also copy and
paste this by the way but we just try to
make it really really quick so
calculating total distance traveled per
rout the code provided blah blah blah it
assumes the
installed the code groups the data frame
by the column route ID this operation
creates groups Okay cool so this is how
we can learn quite quickly right this is
this is so powerful like if you just say
you think before even we had any AI
system like before we even had gbt I
mean you would just be stuck in Forum
hell trying to learn all this stuff
right like it's it's crazy it's crazy
how much quicker it is okay so y okay
now what I want to do um I want to
actually
calculate I want to calculate I want to
calculate something quite simple um now
I just want to show in
a column chart the total passengers per
month and year okay let's see what it
comes up with here okay so M poop lib is
is going to be our visualization Library
assuming the date is in date yep okay
let's give this a wh let's give this a
wh and see what it comes up
with okay hasn't just it's I mean it's
it's it's kind of interesting but I I
actually wanted it like month and year
but anyway it's still it's still giving
us some we just need to adjust the the
inside a little bit I mean this is not
going to be some sort of final product I
show anyone so I'm just going to leave
it as is for now let's just find some
other some other insights here can you
now show me the
total fuel
consumed per month in a column
chart
um please show each unique
month side by
side side
I think it's done exactly the same thing
but let's just have a
look cool okay so this is little bit
annoying that it's not not in the format
I want okay but if if we if we do have
this right if we do find this what you
can do is I want to change this code
around right so um
actually formula fixer let's let's use
this okay I'm going to type in the code
I want to show this as month and year
not and then
year with each column next to each
other and the same
size the the is it the
xaxis to be month and
year okay let's see what it comes up
with so these are just some simple tools
that we've created that you
can
use okay let's just see let's just see
if there some prer right that you can
use to here's this corrected
code um and let's see what this comes up
with
so do you imagine me to write all this
code out it's just terrible right okay
cool finally got what we wanted okay so
just a few quick things you can you can
use now if I want to learn a little bit
more about this then I can just quickly
come to the code
explainer boom like that and then I'll
get a detailed description of how this
was actually cre this this is how I am
learning python okay this is this is how
I'm learning whether it's just simple
analysis it I'm just I'm just literally
plugging myself into
into all sorts of new ways I can learn
right and like this this just gives me a
whole new dimension to how fast I can
learn
things okay now what are some things we
can round off here with um let's do
something a little bit more complex
right investigate correlations between
Fuel
consumptions and distance travel okay
let's let's I want code to complete this
analysis
please okay so just popping this in
here aha okay so we've got an we've got
an issue right so this is here's a
little little tip for you this is what I
this is what I've been doing I can go to
error evaluation like the era the I mean
you I I mean you can pop this into
anyone but this this is a new a new tool
that I created just so that I can
quickly try and understand what these
eras are the user and C because what you
find like what you find like these these
are just hopeless I mean they're so hard
to understand what is going on so I mean
just look at that it's confusing right
so trying trying as best we can to um
you know just give
simple explanations right corrected code
with comments okay so let's just try out
this new code
H
okay I didn't actually load the data in
didn't it
okay while that's running let's have a
look here
so yeah okay ensure the data frame is
loaded and contains the
columns yeah okay yeah there we go
okay so this is just a little bit of
debugging that we need to do
okay so this is this is where you know
you can get a little bit stuck but like
honestly trust me when you're using
Google collab like just working through
like what I've done like working through
errors is just part of the process it's
just part of the process so nothing ever
works first go around
right so let's just try and understand
what's going on here so the user ENC
counted a key error when trying to
create a subset of the data
frame it's probably because we haven't
actually selected
columns yeah okay the error message says
that the columns mentioned do not
exist it's because they're actually
named differently right yeah it's
because they they're named differently
that's
why okay please redo with the correct
column names from
below that's why that's why must be must
be must be let's have a
look Okay cool so it's just creating a a
heat map seeing is is there a
correlation between distance
traveled and fuel
consumption yes there is cuz it's zero
it would be minus right it would be
minus if there wasn't one okay cool
right I'm going to wrap I'm going to
wrap and I'll do more of these I'll do
more of these like I'm I'm going to
start doing a lot more of these because
there there's a reason behind this
right as I've been digging into this new
framework called autogen from Microsoft
is so much of the AI agent workflow of
like automating analysis is being done
with python right and so the more we can
just become familiar with how this
actually works the more we will be able
to understand what the automations are
doing in behind the scenes that is that
is the big reason why I'm diving into
this more and more and more right and
I'm you know I'm I'm I'm I'm really
testing myself I'm doing things that I
haven't really done a lot of before but
I'm going to be I'm G to master them I'm
going to master them by just repetition
and I'm going to show you how I'm doing
it along the way okay okay I'm GNA wrap
up and I'll be putting out a lot more
content about this and around this so so
keep watch out and um I'll talk talk to
you again soon see you later
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