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I'm a senior data analyst with 10+ years of experience and I'm breaking down exactly what I did, what tools I used, and what problems I solved across very different industries.

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⌚ TIMESTAMPS

00:00 – What nobody tells you about data analyst work

01:00 – Predicting refinery outcomes with math models

04:05 – When data analytics meets machine learning

07:00 – Finding needles in millions of log files

09:23 – How one analysis ended up driving marketing & sales

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https://datacareerjumpstart.com/daa

Avery Smith-1: I'm a senior data analyst

with 10 plus years of experience.

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What did I do in those 10 years?

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What tools did I use?

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What problems did I solve?

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That is the topic of today's episode,

and I'm gonna tell you everything

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so that way you know what to expect

as a data analyst in the future.

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I've had a really vast career where I've

worked for one of the biggest oil and

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gas companies in the world, and I've

also worked for a 10 person biotech

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startup that you've never heard of.

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Before, so let's get into it.

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By the way, if you're new here, my

name is Avery Smith and I try to

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share useful data content that will

help you start your data career.

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If that's of interest to you, you

gotta check out my newsletter.

14

30,000 other aspiring data

analysts are already subscribed.

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Go to data career jumpstart.com/newsletter

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or find the link in the

show notes down below.

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So the first company I wanna

talk about is ExxonMobil.

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And what was it like being a data analyst

and a data scientist at ExxonMobil?

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Obviously this is one of the

biggest companies in the world.

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There's like 70,000 employees and

they do a lot of different things.

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Now, I worked in the downstream.

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Part of the business, which

basically means the refiners.

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These are the people that are taking oil

and turning it into gasoline essentially.

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And what do we do there as data analysts?

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Well, we tried to make a mathematical

model of every single part of the

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refinery, and I don't think this is,

you know, groundbreaking to those who

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are in the oil and gas business or

any sort of manufacturing business.

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If you can create what's called

like a digital twin or like a math

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twin of your process, you'll be able

to experiment with the math model

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instead of experimenting in real life.

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So you can be like, well, if I twisted

this temperature, or I changed this

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pressure, or we, you know, added

this new oil, what would change?

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Would we make more money?

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Would we make less money?

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What would go well?

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What would go poorly instead of actually

experimenting In real life, you can

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experiment with these simulations with

your data model, and that way you don't

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actually have to do it in real life.

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Now to create these models, there's lots

of different ways that you can do them.

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I'm not getting into the

nitty gritty of like.

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Modeling these types of things.

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But when you think model, the simplest

version that you can think of in

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your head is linear aggression.

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And if you're not familiar

with linear aggression, you

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learned it definitely in school.

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It's the simple thing

of Y equals MX plus B.

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That's the simplest form.

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So basically you have an input.

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An X.

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If based upon your input, can you

predict what the output is going to be?

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If it, you know is a linear relationship,

you'll be able to have the slope that's

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the m and some sort of a y intercept,

and basically guess what the output

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the Y is going to be based on the X.

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Now you can do that a

lot more complicated.

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You could do multivariate, linear

regression, which is like y equals.

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M1 X one plus M two X two plus X 3M three.

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Oh, it's so confusing.

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But my whole point here is like we

were doing these mathematical models,

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and the simplest form that you

can think of is linear aggression.

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So I created a lot of these

models as a data analyst.

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And I also used data analytics to try to

understand our simulation results better.

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So we'd actually run dozens,

hundreds, thousands of simulations

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trying, you know, different things.

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Well, what if this pressure went up by a

little bit, or this temperature went down?

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To actually look at a thousand

different results is really hard to do.

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So we used data analytics

to try to understand the

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results a little bit better.

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And a lot of this was done in a

Power BI dashboard, so I used a lot

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of Power BI dashboards right there.

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And to do the modeling.

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We actually did a lot in Excel, believe

it or not, and we did a lot in Python

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and we even used a more proprietary

software that you don't hear a whole lot.

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It's from sas.

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It's called Jump, JNP, to do our modeling.

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So those are the tools that we're using

at Axon, and that's the problem that

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we're trying to solve is basically,

hey, if we wanna make changes inside of

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our huge manufacturing system, can we

actually come up with a way to test it

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before testing it in real life so we can

kind of know and expect what to happen?

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I think that's common for,

you know, manufacturing.

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I think that's common for any sort of

like time series data you might have

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is if you can create a model, it's

useful for the company to be able

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to predict the future and be able to

figure out what's going to happen.

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A lot of the times this type of

analytics is called prescriptive

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analytics, where you're actually like

trying to not predict what's going

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to happen in the future, but trying

to decide if you make these changes.

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How will the system basically be affected?

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The next data job I wanna talk about was

when I was a data analyst at this nano

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biotech startup, like think 10 people.

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When I joined the company, this

company made really cool nano sensors.

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So think of it as almost like a game

boy, uh, game, like from the olden days,

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that's like the size of this little board.

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And on this board there was a bunch

of different sensors this, you

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know, chemistry company had built.

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And the sensors would basically react to

what was in the air and we would track.

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How their electricity basically,

or their, their amperage or their

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current, through these different

sensors would change when these

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different chemicals in the air hit it.

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So, for example, if you were holding

it in the air, you know, all the

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lines would be kind of stagnant.

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But for example, let's say you

brought an orange next to it, it

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would basically smell the orange.

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And each sensor would react differently

to that orange being nearby.

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And when you have, uh, an array of

these 12 different sensors, you can

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basically create the equivalent of

like a fingerprint, but for smells.

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So think of it as like the smelling device

that would basically take smell prints.

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My job as a data analyst there was to

actually look at the time series data.

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'cause we'd run these experiments where

you'd have like basically background

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noise for a certain amount of time

and then you'd introduce something

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like an orange for maybe 30 seconds

and then take the orange away.

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And we'd look at these time series and

we're trying to use these time series data

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to actually create these smell prints.

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And that's a very difficult thing to do.

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It actually most of the

time took machine learning.

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So once again, this is maybe a

more advanced data analyst role.

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'cause most data analyst roles.

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You're not really using machine learning.

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This type of machine learning is often

called classification, where you're

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basically trying to match data to a

certain category based off of its data.

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So for example, I could bring

an apple near it, right?

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And the sensors would react.

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Maybe they'd go all down, and if

I brought an orange next to it,

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maybe all the sensors would go up.

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And so you can come up with some sort of

an algorithm that would be like, okay,

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if the sensors go up, it's an apple.

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If they go down, it's an orange.

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Now that's really oversimplifying

it because apples and oranges,

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those are only two things that

exist in the universe, right?

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There's like so many

different things that exist.

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We were playing a little

bit bigger stakes.

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You can think of it when

you go to uh, TSA line and.

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And sometimes they, you know, swab you

and they're trying to see if you have

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like any drugs or any bombs on you.

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That was kind of the stakes that we were

playing with in some of our use cases.

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So I would take this data that oftentimes,

you know, was time series based.

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We usually had like 12 to 16 to

24 different sensors on there.

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And I would try to make these

smell prints using classification

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models in machine learning.

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Now, a lot of the time I was

doing this in Python python's.

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Great for doing things

in machine learning.

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There was even some simple

algorithms that I created that were.

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Based in Excel, but

they are pretty simple.

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The more complicated stuff.

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I was doing Python at the time.

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Also, just because we were doing

a lot of these experiments, SQL

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would've been really helpful.

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We weren't actually using SQL

as much as we should have.

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We really should have been using sql.

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Uh, looking back on it a little bit more.

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The third experience I wanna tell you

about was when I was doing my own,

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uh, data science consultancy firm,

and I got hired by a cybersecurity

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company to help them with a few things.

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So obviously we live in this digital age.

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Cybersecurity is really

important, so there's a lot of

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opportunity in cybersecurity.

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And the interesting thing

about cybersecurity is a

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lot of the data is like.

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Hidden in logs, because basically anything

you do online, anything you do on the

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internet gets logged one way or another.

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Like it's, it's in there.

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They're capturing everything, but when

you capture everything, you're kind

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of capturing nothing at the same time

because it's really hard to figure out

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what's the signal amongst so much noise.

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And so this company in particular

was basically getting a bunch of

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internet logs for companies in what

you can consider their workspaces.

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So for instance, all of their Microsoft

logs, all of their Google logs, if

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they're using Slack, their Slack logs,

maybe their employee customer history.

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Just think of like anything

a company might be interested

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in from a cybersecurity stand.

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We were just getting a bunch of the logs.

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Now in these logs, there's maybe

little needles in the haystack.

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There's maybe little gems

that can be pulled out.

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It requires a lot of analysis to

try to figure out what's in there.

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Just imagine you're getting

like a ton of hay and you have

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to find this little needle.

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And so my job was to go in there and try

to see if there was any needles, anything

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that was like really worth diving into

and investigating more, and also just

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summarizing everything that was happening.

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This is how many logins

you had on Google today.

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This is how many, you know,

logouts you had on Microsoft.

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You know, this is how many users

you had from these different states.

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Just like from these giant enterprise

organizations where they have thousands of

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employees and a bunch of things going on.

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Like how do you know

everything's going okay?

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Are you sure that like everyone

is where they say they are?

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Are you sure you don't have any intruders,

you know, people accessing stuff from

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a place that you probably shouldn't?

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Those types of things.

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So we were basically taking.

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These huge dumps of logs that weren't

really important, that weren't really

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interesting, and aggregating them and

trying to find the interesting things.

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And then also making sure

that nothing nefarious was

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going on to do that analysis.

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I was actually using all of Python, but I

could really choose what tool I wanted to.

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I just chose Python personally because

I'm very comfortable in Python.

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I'm, I'm decently good at Python, uh,

and I can do things quickly with Python.

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I probably couldn't have done

this as easily, like in Excel.

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You probably could have done similar

stuff in SQL if you wanted to.

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One thing I really like about Python

is it can do anything, maybe not

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extremely well, but it can do anything.

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Um, so like I was doing all my analysis.

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Uh, in Python and I was creating

data visualizations in Python.

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They even used a lot of the insights

I found, like in terms of aggregates.

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They basically like aggregated all of

their customers data and would publish

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like a, a yearly or, or biannual

report of like cybersecurity incidents.

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And so they were kind of like with graphs

that I was creating with some of these

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KPIs or metrics that I was monitoring.

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That way they could kind of inform

the cybersecurity, you know, fields

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all of their customers about like

what the trends and what we were

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seeing on a big picture standpoint.

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And that was actually really useful 'cause

people would start to like read that and

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be like, oh, I really like this company.

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I wanna work with them.

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And that would bring in new customers.

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So even though like I was doing

that analysis for individual

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customers at an individual level.

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That analysis actually ended up being

really useful for their marketing

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team as well to get more sales and

more customers in the pipeline.

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Now, I've actually worked for way

more than just these three companies.

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I've probably done work for

about 12, including like the Utah

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Jazz, Harley Davidson, and some

other really big names like MIT.

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If you want to hear more about

those, I'll be talking about

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them more in my newsletter.

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So you can

subscribe@datacareerjumpstart.com

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slash newsletter, and

I'll be talking more.

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About these experiences in the newsletter,

but if you want me to talk about it

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on the podcast or YouTube as well,

let me know in the comments down below

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and maybe I'll do some future episodes

on that if we get enough comments.

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As always, thanks for watching

and I'll see you in the next one.