In this episode, I uncover the nine biggest LIES about landing a data job. Maybe what's stopping you from pursuing a data career is just a big lie.

No College Degree As A Data Analyst YT Playlist: https://www.youtube.com/playlist?list=PLo0oTKi2fPNjHi6iXT3Pu68kUmiT-xDWs

Don’t Learn Python as a Data Analyst (Learn This Instead):

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⌚ TIMESTAMPS

00:00 Introduction

00:05 You Need a Computer Science or Math Degree

01:20 You Have to Be Good at Math and Statistics

03:00 You Must Know Everything About Data Analytics

04:27 Certifications Matter

05:35 Skills Are Enough

07:20 AI Will Take Your Job

09:24 You'll Spend 80% of Your Time Cleaning Data

10:08 Data Titles

11:44 There Are Lots of Remote Jobs

13:17 The "Self-Taught" Data Analyst

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Here are the nine biggest lies about landing a data job

2

that are being told this year.

3

Lie number one, you need a

computer science or a math degree.

4

There's lots of people and organizations

that will tell you that in order to land

5

a data job, you need to have studied

computer science, math, or economics

6

in college, but that's not the case.

7

Take me for example.

8

I studied chemical engineering

and became a data analyst and

9

then became a data scientist.

10

But even then, chemical

engineering is pretty technical.

11

There's a lot of people who

have less technical degrees

12

than chemical engineering who

have landed into the data world.

13

For example, I've interviewed

a lot of them on this channel.

14

We had Alex Sanchez who was a high school

math teacher and he pivoted into data.

15

We had Aaron Sheena who was a music

therapist who landed a financial

16

data analyst job at Humana.

17

We had Rachel Finch who studied biology

and now has a business intelligence job.

18

And then there was Trevor Maxwell

who doesn't even have a college

19

degree and ended up landing

a technical data analyst job.

20

You don't need a computer science

degree and you don't need a math degree.

21

Whatever degree you have

now is probably good enough.

22

And if you don't have any college

degree, you can probably do it as well.

23

It's just a little bit

more of an uphill battle.

24

I have a whole YouTube playlist

where I talk to people who land

25

jobs without college degrees.

26

I'll have that in the

show notes down below.

27

Truth be told, you don't need a computer

science degree and you don't need a math

28

degree to break into data analytics.

29

Lie number two that they tell

you is that you have to be

30

good at math and statistics.

31

And honestly, you don't really

have to be good at either.

32

Now I am going to caveat here

and say if you want to be like

33

a deep research data scientist.

34

You probably want to be a little

bit good at math, but for the rest

35

of you guys who just want like a

normal data analyst job, you honestly

36

don't have to be that good at math.

37

Like honestly, most of my students,

when they actually land a data

38

job, the math that they're really

doing is mostly aggregations.

39

That's like some average max min.

40

This stuff isn't complicated.

41

You honestly probably learned

most of it in high school.

42

You may have forgotten now, but honestly,

it's kind of like riding a bike.

43

Once you review it, you'll be

able to catch up very quickly.

44

Now, I can already hear all of you

people commenting and being like,

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well, isn't statistics important?

46

There's statistics in data analytics.

47

And sure, there's definitely some

statistics in data analytics, but I

48

think most people overblow the amount

of statistics you have to know.

49

In fact, a lot of programs like data

analytics master's degrees will say

50

that you're supposed to know calculus

and linear algebra in order to even

51

like, Start the program, and that's

just a flat out lie, like the amount

52

of calculus and linear algebra that I

use as a data analyst is very minimal.

53

Can those concepts potentially help you?

54

Sure, but it's not worth the

amount of time that it takes to

55

actually learn all that stuff.

56

It's not worth it.

57

Like you're not really going to benefit

the return on investment, the ROI.

58

Is not very high.

59

Of course, there's things like AB

testing, hypothesis testing and

60

regression that are going to be

useful for a lot of data analysts.

61

But honestly, that stuff's

not super hard to learn.

62

And the majority of the time,

like you're not doing the math,

63

the computer's doing the math.

64

So as long as you know what a hypothesis.

65

test is and how to set it up and how

to interpret the results, you're good.

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And honestly, I think you can

learn that in one to two weeks.

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Lie number three is that you have to

know everything about data analytics

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in order to land a data jump.

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That you have to know Python, you

have to know Excel, you have to know

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SQL, you have to know Tableau, you

have to know Looker, you have to know

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Power BI, you have to know SAS, you

have to know R, you have to know Java.

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So on and so forth,

and it's just not true.

73

Honestly, you don't have to even know

that much to be a data analyst, and

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maybe just one of those skills is enough.

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For example, I interviewed Matt Bratton

on my podcast a while ago, and he is

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like in the C suite of the data world,

and he basically only uses Excel.

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I've interviewed different

people on my podcast.

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And sometimes they only use

Tableau or they only use SQL.

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It really just depends.

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So sometimes you only have to know one

data skill throughout your whole career.

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Now saying your whole data career,

that's a little bit dramatic.

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Like you will probably use multiple

skills throughout your career.

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But when you land that first job,

like really a lot of the time, you're

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using one to two data tools, max.

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That being said, it's like, well, how do

I know which one to two that those are?

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And you really don't.

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And it's going to change from job to job.

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But here's what I will tell you

that Python is only required 30

89

percent of the time for all data

analyst jobs from junior to senior.

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So personally, I don't really think

it's worth learning to be able to

91

apply to those extra 30 percent of the

jobs when you're just getting started.

92

I did an episode about this previously.

93

You can see it right here and I'll

have a link to it in the show notes

94

where I really don't think you should

start with Python or R to be honest.

95

The lie is that you

have to know everything.

96

And the truth is you don't,

you can get started today.

97

And honestly, you can probably land

a job pretty soon with The skills

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you have already line number three

is that certifications matter.

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I don't care if it's the IBM certificate,

the power BI certificates, the

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Google data analytics certificate.

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The truth is for the majority of

data jobs, your cert does not matter.

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I know that might hurt to hear, and

you might not want to believe me, but I

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actually run my own job board, find a job.

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com.

105

And I analyze the 2000 plus jobs that

I've posted on there the last four months.

106

And not once did any of

the jobs posted on there.

107

Ask for any sort of certificate.

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I know like the badges look cool

and like the certificate looks cool.

109

The truth is no one really cares.

110

At least employers don't really care.

111

I have a lot of people who message

me and they'll say, Hey, Avery,

112

I don't need your bootcamp.

113

I'm already data analyst certified.

114

And that is like the biggest

lie that you could ever say.

115

And I understand that someone did.

116

Certify you as a data analyst, but

there's nothing in the industry that's

117

standardized that makes you data analyst

certified It's not like a nurse or a

118

teacher where like you have a license.

119

That's the wild west out here in the data

world We don't care about that stuff.

120

So having a certificate.

121

It's not a bad thing necessarily But it's

not like all that you might think it is.

122

It's not your golden ticket into the

data world It takes a lot more than

123

that and that leads me to my next lie

lie Number four is that skills are

124

enough now you think that like If you

want to be data analyst, you have to

125

learn these X amount of things, and then

you can become a data analyst, right?

126

Wrong.

127

Skills aren't enough when you're

trying to land a data analyst

128

position for multiple reasons.

129

One, as data analyst, like you're

actually not just spending your whole

130

time using those technical skills.

131

Like you're not just in Excel all day.

132

One of the most important things you'll be

doing as a data analyst is communicating,

133

is working with stakeholders.

134

Is talking to teams and leaders and

understanding, you know, what the data

135

is, where the data is at, what, how

you should analyze it, what's important

136

for them to know, so on and so forth.

137

But two, anytime you're trying to

land a data job, it's not the most

138

skilled person who lands the job.

139

Like think about it.

140

I'm down here in my office.

141

If I spent the next 240 years of my

life just studying data analytics,

142

but I didn't have a resume, would

I land many data analytics jobs?

143

Probably not because it takes

more than just your skills.

144

There's a whole variety of things

that will actually help you get hired.

145

I create a little mnemonic

for you to remember.

146

It's called the SPN method, and

it's the easiest and fastest

147

way to become a data analyst.

148

S stands for skills, and that's

one third of the equation.

149

But it's only one third of the equation.

150

You need the P and the N.

151

The P stands for projects or portfolios.

152

And these are basically opportunities

for you to showcase your skills because

153

anyone can say that they know SQL, but

you want to back that up with tangible

154

evidence to a recruiter or hiring

manager via project on your portfolio.

155

The N stands for networking and

really like 70 percent of jobs

156

are done through networking.

157

You're really getting

recruited or referred.

158

And so there's a lot of different

ways you can network and a lot of

159

different things that you can do to

increase your chance of getting hired.

160

That is totally irrelevant and

not even related to your skills.

161

There is no correlation to how skilled you

are, how quickly you land a data job, and

162

how much you get paid as a data analyst.

163

If you want to learn more about

the SVN method, I'll have a link

164

in the show notes down below.

165

Lie number five is that AI

is going to take your job.

166

It's really interesting because

a lot of people are nervous about

167

becoming a data analyst because they

don't feel like it's very AI proof.

168

And one thing I've been

thinking to myself is Okay.

169

Well, what careers are AI proof?

170

In fact, I had one perspective student.

171

He was messaging me and saying that his

friend was kind of making fun of him

172

because like data analysts are going

to be replaced by AI and he had like a

173

blue collar, more like mechanical job.

174

And that was never going

to be replaced by AI.

175

I think that's interesting because

like throughout history, haven't

176

we seen like more of the mechanical

jobs being replaced by AI?

177

previously.

178

So like, I think those jobs aren't safe.

179

And then I thought, oh, maybe

like a doctor that I was like,

180

well, aren't like a bunch of like

robots doing surgeries nowadays.

181

And like, can't you just kind of like

use web MD or whatever chat, GBT to like

182

ask what's wrong and get a diagnosis.

183

Obviously there's going to be some

jobs like nurses, for example,

184

where I think that is basically

impossible to have a robot or AI do.

185

But honestly, I've used AI

to try to analyze data and

186

it's definitely not great.

187

Another thing you should realize

is the difference between

188

augmentation and automation using AI.

189

Augmentation is almost like you can

think of it like putting on like the

190

like glove in Iron Man or something?

191

I don't know.

192

I'm not good at Marvel, you guys.

193

Uh, like, like the Infinity

Stones in that one movie, right?

194

Like, that changes who you

can be and the powers that you

195

have, but you're still yourself.

196

And then the other one would be like,

no, I create a robot that's super

197

powerful and it replaces me completely.

198

And honestly, AI is going to augment you.

199

That's for sure.

200

It's going to change how work is done.

201

But it's still you doing

the work a lot of the time.

202

I've seen a lot of these companies

try to come out with like the auto

203

analyzing data and it's not great so far.

204

Is it going to get better in the future?

205

Yes, definitely.

206

But I definitely don't see

the human element getting

207

taken out of it anytime soon.

208

The ability to reason to actually find

like what's relevant to the business and

209

then explain all that back to someone I

think is something that's very valuable.

210

I'm a data analyst, right?

211

I teach people how to

become data analysts.

212

So my future is very heavily

tied in this and I honestly

213

am not that worried about it.

214

I think that, AI is going to

help us be better data analysts,

215

and that's about the gist of it.

216

So lie number five is that

AI is going to take your job.

217

Lie number six is that you're

going to spend 80 percent of

218

your time cleaning your data.

219

I don't know where this came from, and

I don't know who made it, and I don't

220

really know who propagates it further.

221

Personally, in the roles that I've been

in, sure, data cleaning is important,

222

and it does take a significant amount

of time, but it's nowhere close to 80%.

223

Honestly, if you're spending 80 percent of

your time cleaning data, You're probably

224

spending your time on the wrong things.

225

I honestly think that like 80 percent

of your time should be spent talking

226

to people as a data analyst before

you start a project, when you're in

227

the project and after the project,

I think communication is actually

228

way underplayed in the data world.

229

But I don't know who's saying

that 80 percent of your time is

230

cleaning data because that's.

231

A huge exaggeration.

232

Data lie number seven is all data

titles, uh, and I'm just so sorry

233

for all you job seekers out there.

234

This is the most frustrating thing

on planet earth, but once again,

235

the data world is the wild wild west

and basically job titles are all

236

kind of made up in the data world.

237

There's kind of like the big three.

238

There's the data engineer, the data

analyst, and the data scientist.

239

But there's so many more positions in

between that overlap and that are the

240

same and that are misclassified and

companies will call something, you know,

241

a data analyst one place, but that's

really a data scientist other places.

242

And it's really confusing.

243

So all the data titles you're reading

on the job board are probably lies.

244

And you should try to base it off of

what's like in the requirements section

245

of the job description to actually

know what the job is going to entail

246

and what the actual title kind of is.

247

For instance, there's something

called a data science analyst.

248

I don't know what the heck that is.

249

I've even seen data analytics scientist.

250

Technically, my role at Exxon for a long

time was optimization engineer, but I was

251

really doing the work of a data scientist.

252

And even at my first job, I was

technically a data analyst, but you could

253

have also called me a chemometrician.

254

There's so many different titles.

255

They're so confusing.

256

Honestly, I've CEO reach out to me

one time and ask me to look over.

257

Their job description for

hiring their first data analyst.

258

I looked it over and I was

like, this is a data scientist

259

job, not a data analyst job.

260

And he replied, well,

what's the difference.

261

And this is like, not

a super small company.

262

Like this is definitely a

company you've heard of before.

263

I guess it was technically like

a general manager, not the CEO.

264

It was like the president of a

local area anyways, but still

265

like that is pretty crazy.

266

Right.

267

The people who are writing these job

descriptions maybe don't necessarily know.

268

a hundred percent what

they're talking about.

269

Lie number eight is that

there is lots of remote jobs.

270

And now this one's super interesting

because anecdotally, it does feel

271

like there is a lot of remote jobs.

272

Most of my friends who work in

data have pretty flexible schedules

273

and lives for the most part.

274

And most of my students in my

program get pretty flexible jobs.

275

But when I went and actually

did the research myself and I

276

started web scraping job listings.

277

I found that remote jobs only make

about 16 percent of all the jobs on

278

the market, meaning the other jobs, the

remaining 85 percent ish are not remote.

279

And obviously most of you guys watching

probably are interested in a remote job.

280

So let's say that 95 percent of

people are interested in a remote job.

281

That means there's a demand 95

percent for a low supply of 15 percent

282

of jobs that are actually remote.

283

And this is one of the reasons why the

job market is so crazy right now and

284

really frustrating and it feels like

it's impossible to land a day job.

285

The truth is there's just not as many

remote jobs as you may think there

286

is, but there's actually equally

the same amount of hybrid jobs.

287

So there's about 15 to 16 percent of

jobs in the market that are hybrid.

288

And the cool thing about hybrid jobs

is it's on a spectrum of being in

289

the office and working from home, and

every hybrid job is somewhere on that

290

spectrum, but in different places.

291

Some of my students work from the

office four times a week and then

292

work remotely one day of the week.

293

Sometimes it's reversed.

294

Like for instance, some of my students

who work at Humana, they work from

295

home four days a week and they

work in the office one day a week.

296

I even have one student who is

hybrid, but she's only required to

297

go in the office once a quarter.

298

Now, to me that's more remote

than it is hybrid, but it

299

was still labeled as hybrid.

300

So I think the biggest play and

what you guys should be focusing

301

on right now is hybrid jobs.

302

Lie number nine is the self taught data

analyst or the self taught data scientist.

303

So many people will say I'm self taught.

304

And first off, what the

heck does that even mean?

305

Like you're learning from somewhere.

306

It's not like you just like went

out into your yard and like really

307

thought hard and you're like, Oh, yes.

308

What if I like Excel and Vlookups would

make a lot of sense in a pivot table?

309

Yes.

310

Oh, and joins and SQL.

311

That makes a lot like you're not just

like divinely absorbing this knowledge.

312

You're learning from somewhere,

whether it's a book, whether

313

it's online, so on and so forth.

314

I think most people say self taught

because they maybe don't have a

315

formal degree or something like that.

316

I would consider myself.

317

Self taught, but I eventually

got a master's degree in

318

data analytics in college.

319

I took statistics classes that

got me really interested in data.

320

I had a really good

mentor at my first job.

321

He taught me a lot.

322

So I think the concept of, I want to be a

self taught data analyst is kind of silly.

323

It's also like, you don't get sent

a trophy for being a self taught

324

data analyst, like who cares

if you're self taught or not?

325

Like you don't get to wear like a badge.

326

It's like, Oh wow.

327

Like she's self taught.

328

He's self taught like

now, like it's okay to be.

329

You know, not self taught like

that's totally acceptable.

330

And honestly, maybe you should

wear that as a badge of honor.

331

It's like, no, I didn't do this

on my own because I knew I needed

332

help or I wanted to do this faster.

333

So I sought help like there's

nothing wrong with that.

334

That's plenty cool as doing it yourself.

335

So there you have it.

336

The nine biggest lies of becoming a

data analyst and landing a data job.

337

Are there any myths that I missed?

338

Put them in the comments down below and

I'll try to respond to every comment.