Data36

📢📢📢 Test whether you are read for a Junior Data Scientist position!📢📢📢

Friends! Registration starts next week for arguably the most practical data science course that's out there. (Well, I argue it! 🙃 )

The Junior Data Scientist's First Month (Class of October, 2023)

A 100% practical online course. A 6-week simulation of being a junior data scientist at a true-to-life startup.

“Solving real problems, getting real experience – just like in a real DS job.”

PRE-REG:

The Junior Data Scientist's First Month (Online Data Science Course) 100% practical online data science course. A 6-week simulation of being a junior data scientist at a true-to-life startup.

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If a take-home assignment during a junior data scientist interview takes significantly more than ~6-8 hours to complete, then something is likely wrong.

Either the company:
🚩 lacks a clear understanding of what to expect from a junior data scientist
🚩 is unsure how to effectively assess a junior data scientist's skills
🚩 enjoys putting excessive pressure on its candidates (and potentially its employees)
🚩 exploits vulnerable entry-level professionals (by making them solve the company's real life data challenges for free)
..Or it could be a combination of these factors.

In any case, if you find that the interview process demands not hours but *days* of work, it's often better to walk away. Such situations usually don't lead to positive outcomes.

A well-designed junior-level take-home assignment:

🟢 is straightforward, simple, and focused
🟢 includes 1-4 data tables
🟢 can use real-world data, but even if so, it's usually prepared (at least anonymized) and rarely up-to-date (usually at least 1-2 years old)
🟢 lists 3-4 clear questions
🟢 can be solved in ~4-8 (max 12) hours
🟢 and in general gives you the impression that it's a standard assignment -- one that every candidate receives, and for which the hiring committee has a general expectation (or at least a direction) because they've already solved the task themselves.

Do you agree?
Have you ever felt that something's off with your interview process? Share your story so we can all learn from it! 👇 👇 👇

Yesterday 9 a.m. to 4 p.m. I worked on my data science project with complete focus.

No distractions.

Deep work.

Just as one wishes to work when reaching for high efficiency. And indeed, I churned out ~300 lines of Python code to crack a very challenging NLP classification problem. The result was good: the model achieved about 90% accuracy. That's pretty good, actually! However, for this specific task, I needed closer to 99%.

So I sat down again, later the same day, around 11 p.m.

And I realized that there is a waaay better solution to the issue! I completely deleted my original script and replaced it with ~50 lines of Python -- which I wrote in roughly an hour.

Now, the model reaches the desired 99% accuracy!

9am-4pm: I worked efficiently.
11pm-midnight: I worked effectively.
There is a difference.

P.S. The funny part is that it felt painful to delete my 9-to-4 code, even though I knew the second solution would be far superior. I wish I had thought of the second solution at 9 a.m., finished it by 10 a.m., and taken the rest of the day off. I guess I need to remind myself that mistakes are just stepping stones. ¯_(ツ)_/¯

Disclaimer: This is a repost of my original story from November 2022. I wanted to share it here again because it was a huge aha moment for me.

My favorite explanation of false positives, false negatives, true positives, and true negatives.

(And since we are talking: who came up with the names "Type I error" and "Type II error" in statistics? Clearly, it wasn't a marketer.)

Throwback to last year, when I was fortunate enough to be invited to consult on this factory's short-term and long-term perspectives for their data science projects. IoT, machine learning, advanced automation—it was incredibly inspiring for me to see that data can go beyond the virtual realm.

MrBeast A/B tested his videos and found that closing his mouth in the thumbnails increased watch time. Surprising... because many YouTubers have previously found that open-mouth thumbnails bring more engagement. While MrBeast hasn't disclosed the exact methodology of the split test, given his epic number of views and the significant ad revenue at stake, it's safe to assume the test was conducted professionally and the results are statistically significant.

The main lessons here are:

1) You run experiments to challenge the status quo -- and win.
People often say you should adhere to this or that "best practice," which is fine initially. However, at some point, you should question these norms and occasionally do the exact opposite of what everyone recommends.

2) What works today may not work tomorrow. Business owners frequently overlook how quickly things can change. Even if you have a process, an SOP, or automation that is currently optimal for your business, you should revisit and experiment with it at least every one to two years.

3) You might think of MrBeast as just a creative genius. Which he is. But he's also highly analytical!
I highly recommend his podcast interview with Lex Fridman. Listening to it, you'll realize the extent to which he relies on data in his day-to-day operations. Building what is arguably the best YouTube channel requires not just creativity but also rationality, analytical thinking, and a data-driven approach.

✌

Jarvis didn't take Tony Stark's job -- but helped a lot with it! ☝️ 😉

Feeling too old for a career change in your mid-30s? Well, it can be perfect for a data science pivot -- and here's why.

A small misunderstanding often arises when people ask me if it's worth starting a new career in data science in their mid-30s.

*Is it too late?*
*Are they too old?*

First of all, I really hope being in your mid-30s isn't considered "old," because I'm 34 myself. 😅

Secondly, it's certainly not too late. In fact, most of the people in my data science courses *are* in their mid-30s! Quite a few are on paternity or maternity leave, thinking about which new skills to acquire and where to re-enter the job market when they return to work. And that's how they find data science.

Well, okay. I agree that starting in data science in your early 20s has its advantages:
👉 It's easier to secure internship positions.
👉 You likely don't have family responsibilities, so you can accept an entry-level job or an internship with a lower salary, or even an unpaid position, just to learn and gain experience.
👉 You likely have more freedom to spend your days and nights learning, working on hobby projects, and generally hustling.

In your mid-30s, you probably don't have as much free time, and you might be reluctant to take a step back salary-wise. That's understandable. However, compared to those in their early 20s, you have now several advantages:
👉 You probably have roughly 10 years of work experience, possibly in a relevant domain like finance, marketing, or science. (Articulated well, that can look great on a CV.)
👉 With that decade of experience, you have a better understanding of business, something you likely didn't have fresh out of school or university. (And let's be honest, even business schools don't fully prepare you for real-world business challenges — only work does.)
👉 You probably have strong skills and experience in one or more domains (again, like finance, marketing, or science, etc.), which can be invaluable when specializing in data science.

If you leverage these advantages and acquire the necessary data science skills, you can gradually transition into a data science role. I call this "soft repositioning." Just one common example: 1) You might start with a Business Intelligence (BI) position in your field, such as creating dashboards for marketing executives. 2) Later, you could take on more analytical tasks, like analyzing data in SQL or Python before creating those dashboards. 3) Eventually, you might delve into more scientific tasks, like building basic machine learning models.

I know this sounds simplified, every journey is somewhat different. Also, the process of learning and repositioning is never easy. it can also take a lot of time. (Hence my motto: "Learn Data Science the Hard Way!")

But it's very, very rewarding in every sense!

Either way, my point is: being in your mid-30s is definitely not too late for a career in data science! Many others have made this transition at this age, and it has worked out great for them!

***
If you want to start the learning process, check out my blog where I publish a lot of free stuff: Data36 [dot] com

Is Data Science the Right Career for You? 7 Key Indicators in the post👇

Wondering if a career in data science is your calling? There's more to it than just the alluring promise of wealth and intelligence. In reality, it's a challenging field requiring continuous learning and a deep understanding of complex subjects.

QUESTION #1: Could You Learn Data Science?
I don’t believe in talent. I don’t think that anyone was born being better – either in statistics, mathematics, coding, business, communication or anything else – than others.
I believe in skills. I think that some people are better than others in some things because they are continuously practicing and developing their skills – for years, for decades, for their entire life.
Thus I strongly believe that nowadays anyone can acquire any skills if they are really dedicated to it. Simply put: if you want to learn data science, you can learn data science. So the real question is...

QUESTION #2: Would You Enjoy the Process?
I’ve found that one can only work on a specific skill without losing motivation if they enjoy the process of learning and practicing. Working on something always has its ups and downs, but if you generally enjoy the process, that will definitely help a lot to keep you in the flow.

👉 The most important question for me before you start your data science career is: would you enjoy data science at all? 👈

So I collected 7 Indicators that can help you figure out whether you're cut out for Data Science:
1) Analytical Mindset: If you naturally gravitate towards a rational analysis over relying on luck or instincts, that's a good sign.
2) Love for Math and Statistics: You'll be dealing with numbers a lot, so an affinity for mathematics is essential.
3) Business Acumen: Understanding the business implications of your analyses is crucial. Data science isn't just about number crunching; it's about driving value.
4) Coding Enthusiast: Much of your time will be spent coding. If that's something you can see yourself enjoying, you’re on the right track.
5) Team Player: Expect to collaborate with professionals from various departments. If you're comfortable in cross-functional settings, that's a plus.
6) Communication Skills: You'll need to articulate your findings clearly and effectively, often to non-data experts.
7) Lifelong Learner: The field is always evolving, and your ability to continuously learn is pivotal for long-term success.

As you can see, a great data scientist has a good combination of introverted, extroverted, rational and emotional skills. Again, with practice, you can improve all of them… The biggest question is if you want to. I hope that now you have a clearer picture.

If you want to learn more, check out my data science blog with tons of free tutorials at Data36 [dot] com.

The best investment is to learn. There is no bear market of knowledge and skills!

The months in alphabetical order:

April
August
December
February
January
July
June
March
May
November
October
September

There's a lesson here.....and I'm not the one to figure it out.

Last year, I bought a bicycle ($250 investment) and so I spent at least $500 less on gas or commuting ever since.

That’s a 200%+ ROI in ~1 year!

Better than most crypto yield farms.

Hey, after about 4 years, I'm back on Facebook. Not sure why, but we'll see how it goes. 🙃
I still recommend following me on LinkedIn or Twitter. (I only reshare my posts from those platforms to here.)

Cheers, y'all!
Tomi Mester