Why I'm placing a lot more focus on learning Python....and how I'm doing it

Summary

In 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

00:00

🐍 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.

05:02

📊 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.

10:03

🔍 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.

15:03

🚀 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.

Keywords

💡Data Skills

Data skills refer to the ability to work with, analyze, and interpret data. In the video, the speaker emphasizes the importance of improving data skills in the current era, particularly with the rise of AI systems and automations. The speaker suggests that these skills are crucial for personal growth and value addition in the professional sphere.

💡Python

Python is a high-level programming language known for its simplicity and versatility. It is widely used in AI systems and automations, as mentioned in the script. The speaker has been focusing on enhancing their Python skills to stay relevant in the tech industry, highlighting its significance in the development and testing of AI technologies.

💡AI Systems

AI Systems, or Artificial Intelligence Systems, are computerized systems that perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making. The script discusses how many AI systems are being developed and tested, and they predominantly use Python for their operations, making Python a vital skill for those working with AI.

💡Google Collab Notebook

Google Collab Notebook, also known as Google Colaboratory, is a free cloud service for machine learning education and research. It allows users to write and execute Python code in a notebook format. The speaker mentions using Google Collab to perform data analysis, emphasizing its ease of use and the ability to operate it without extensive Python coding knowledge.

💡Data Analysis

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. In the video, the speaker demonstrates how to perform simple data analysis using Python and Google Collab, showcasing the power of these tools in quickly generating insights from data.

💡Pandas Library

The Pandas Library is a Python package for data manipulation and analysis. It provides data structures and operations for manipulating numerical tables and time series. The speaker uses Pandas in the script to load data into Google Collab, perform statistical analysis, and calculate summary statistics, demonstrating its utility in data analysis tasks.

💡Summary Statistics

Summary statistics are quantitative measures that summarize a data set, such as the mean, median, mode, and standard deviation. In the script, the speaker uses Python code to quickly generate summary statistics for their transportation data set, illustrating how these statistics can provide a quick overview of the data's key attributes.

💡Data Frame

A data frame is a two-dimensional data structure in Pandas, used to store and manipulate tabular data. The speaker mentions creating a data frame from their transportation data set in Google Collab, which serves as the foundation for further analysis and manipulation of the data.

💡Visualization

Visualization refers to the graphical representation of data and information. The speaker discusses using visualization libraries in Python, such as Matplotlib, to create visual representations of their data, such as column charts, to better understand patterns and trends.

💡Correlation

Correlation is a measure that expresses the extent to which two variables are linearly related. In the script, the speaker investigates the correlation between fuel consumption and distance traveled using a heat map, demonstrating how such analysis can reveal relationships within the data.

💡Error Evaluation

Error evaluation is the process of identifying and understanding errors in code or analysis. The speaker mentions using error evaluation tools to quickly diagnose and resolve issues in their Python code, such as key errors, which is crucial for effective data analysis and debugging.

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