Data jobs, disclaimers we're never told about! [self-promo'ish]Career Advice

It's been a long time i havent shared my two cents on this group. However, if anyone wants to understand a bit the expectations of their future job in data analysis/junior data engineering, you can read this 2-3 mins article about the ugly side of data. I wrote about it a year and half ago, but never thought of sharing it until today. Its not fully developed, but will give an image.

Many will disagree/agree, depending on the type of companies they work at, but in general almost everyone will experience this "disappointment" in their careers. (Please comment on the post, i would love to engage in discussions/see other DA/DEs opinions)

In short, expect to be the only data literate team at your company (unless very lucky).

Data driven companies are the goal, but almost everyone is far behind being one. Many companies believe they are at a higher data maturity than they actually do/are.

47
28
7mo
What you need to become a data analyst [Advice+self-promotion]Career Advice

Since the question is asked frequently here, I will share a summary of my two cents on how to become a data analyst.

Basics to cover

  1. Math and Stat
  2. Basic knowledge of a coding language, preferably the ones that can be used for data stuff (R/Python)
  3. Common sense and understanding that a company is looking to make money

Intermediate to Advanced

  1. Data manipulation and cleaning tools. (Pandas (python), Alteryx)
  2. Data visualization tools. (PowerBI, Tableau...)
  3. Understanding the purpose of each visual (graph)

To learn the intermediate to advanced stuff, I personally used DataCamp (my experience here).

If you have any question feel free to ask.

14
0
3.0y
Is DataCamp Worth it?

This review is updated based on DataCamp 2021 (for those wondering if the website has changed).

My story with DataCamp started in the 2020 lockdown. We have received from our university a confirmation of joining a Datathon and at the same time, a free 6 months subscription.

My goal was to become a Data Scientist or Analyst, however, I was not sure how to do it.

An arabic proverb says, "if it's free, benefit from it". So I did exactly that. I started my "Data Scientist Track with Python", doubting whether it might be a highly valuable certificate to obtain.

The amount of hours required to finish the full track did not motivate me at the beginning, however, I kept pushing. Day after day, hour after hour.

I stayed on track with a minimal goal of one chapter per day on my bad days and one course or more per day on my good days. It was not easy, I cannot hide that. Some days, it would take me 2 hours to finish one chapter (procrastination) and some other days, I used to rage quit because of not being able to find the solution. However, as James Clear says in his book "The Atomic Habit", 1% of progress per day is better than 0. Because, compounding growth.

Fast forward a year from those days, I am a proud Data Analyst. I did two internships at Big4 companies (due to the skillset I acquired from DataCamp). So was it worth it? Hell yeah it was!

PinnedDataCamp
74
29
3.0y

happy to see hard working men in Belgium <3

kiddo will surely be proud of you

you'll cringe when you see it and won't understand it

not sure why this doesnt get upvotes

cz its so true

most DEs do not come from SWE background, so by default they did not learn the best practices in an institution/uni/...

the majority move from data analyst positions (cleaning excels).

Or at least the couple of hundreds of people ive met. So yea

how do you go about documentation?

explaining what a function does or why it does something (business logic oriented)?

the whys or whats?

im focusing on the Why's since many book suggest that clean code should be self explanatory regarding the how/what

investing that extra day or two of work at writing clean and optimal code is better than wasting those 2 weeks of WTF does that shit do in a few month/year or so

not really. a team is either using good practices, or not.

By using good practices i mean, certain rules that all developers will agree upon without being dogmatic about them in some cases.

e.g using good names for functions and variables.

Yes sometimes naming won't be perfect, but using variable_1 variable_2 in every single piece of code you're writing, means one thing: you're very good at being bad at developing code and should receive coaching/upskilling.

two teams can have different rules and boundaries in the way they write code and still both generate good enough code for the rest of the people to understand and use or change

After almost 3 years, would you still recommend it ? :)

if so which one the most?

I totally understand the frustrations and if I work with you I wouldve probably felt the same.

However, based on what you wrote it doesnt seem like a big deal

  • checking the SQL queries of my colleagues;
    • I dont understand why you see this negatively. If you're extremely technical and a god of SQL and you and your coworkers write the most sophisticated SQL on earth, then the boss is just doing a code review/ trying to learn from you guys so they can be added value later on (approving PRs or maybe supporting in writing code)
  • they want to know if this or that has been documented for tiny operations;
    • again nothing wrong. if anything you should be thankful, if one day someone leaves the team, you won't go nuts trying to understand the shitcode left behind/functional knowledge transfer
  • ingesting new datasets before patching bugs/unit testing
    • no time to unit test = ure doing something incorrectly
    • if by data ingesting you mean raw/cleansed/curated thats one thing, and if you only mean raw, thats something else.... so cant judge on this one

overall be happy that the non technical is tryna show that they care about technical shit :) some non technicals are way worse than what you think

this !

at the end of the day ure there to learn

if your answer is incorrect you would understand why, if its correct, u would report it and move on to the next challenge

incorrect column order, rating should be first

data analysis is almost business facing, and if you can't speak the national language it will be hard to land a job remotely, UNLESS the company's main language is clearly english (multi national)

DO ITTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT!!!!!

you won't regret it

Check my pinned articles in my profile. Yes it is worth it, yes it has a learning curve, but the reward is also very worth it, money wise and career growth. However it all depends in which country you are.

will keep that in mind. As you said a mindset shift is all we need :)

i was thinking of studying for the AWS cert

ig someone just saved me some time

But thats the business team’s responsibility to make it clear on why they need something instead of just throw it right at your face. At least IMO. Especially when the analyst is a junior (confused most of the time)

Thats the summary of the post, if a company is using a very specific non general tool, its a huge red flag. With that being said, none of the current bootcamps/learnings are focused on those specific products, which is relieving.