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ABOUT ME: I'm Mo and I work as a data analytics manager / content creator. I make videos about how you can stay competitive / ahead of the competition in the data industry.
Mo Chen
If you've been following me for a while, you know that I love being active.ย For me, it's a way of life. I don't diet, I don't stick to rigorous exercise routines - I just like keeping fit and healthy.
A yoga mat and push up bars are my perfect, compact mobile gym when traveling around.
I'd love to know how you stay active when traveling. Comment below ๐
4 days ago | [YT] | 208
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Mo Chen
"You know what really grinds my gears"?
When people come to me and ask
"Where can I find datasets?"
๐ง๐ต๐ถ๐ ๐ถ๐ ๐บ๐ ๐ป๐ผ-๐ณ๐น๐๐ณ๐ณ ๐ฎ๐ฝ๐ฝ๐ฟ๐ผ๐ฎ๐ฐ๐ต. ๐ ๐บ๐ถ๐ด๐ต๐ ๐ด๐ฒ๐ ๐๐ผ๐บ๐ฒ "๐ฑ๐ถ๐๐น๐ถ๐ธ๐ฒ๐".
But I'm here to help people transition to Data.
So, here's my take:
If you didn't create 10-20 different projects, stop asking for "real-world" datasets, because you don't have a dataset problem. You have a procrastination problem.
Laziness is present in data as it is in other things in life:
"Productivity"
"Organization"
"Focus"
Even if you land 100 interviews, "by accident", you won't get the job.
Not without work.
Not without showing you can go from problem โ thought โ solution.
So, stop "faking" it, and do it.
If you still haven't achieved your goal of landing a data job, your only focus should be to create as many projects as you can.
No matter the dataset.
No matter the industry.
I'll guarantee that 10-20 projects will change your career.
And you won't need 100 interviews to get a data job.
Agree?
1 week ago | [YT] | 120
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Mo Chen
Uncomfortable truth
2 weeks ago | [YT] | 180
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Mo Chen
Long hours learning = Fewer hours applying
There's a reason why value per second is the only metric companies use when searching for the best applicant.
It's not about how much you know.
It's about how quickly you apply it.
3 weeks ago | [YT] | 90
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Mo Chen
Changing to a data field isnโt easy, and here's why:
The first weeks in data analytics can feel overwhelming with so much to learn and so many paths you can go with.
Spreadsheets, SQL, BI, coding, AI ... you name it.
This creates a sense of uncertainty that will activate your impostor syndrome, making you feel like every lesson you watch doesn't actually make you more skilled.
The uncertainty gets even heavier when youโre not sure when things will start to make sense.
And, when you find yourself during extended periods in a situation where you feel like you're not improving, chances are you might quit because ... life's too short to waste time on something you cannot understand.
But let me tell you ...
That uncertainty fades.
One Formula at a time.
One KPI at a time.
One Query at a time.
This is the message I have for today:
If youโre just starting out or feeling stuck, keep going.
Progress comes quietly, but it comes.
3 weeks ago | [YT] | 186
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Mo Chen
How to get your first Data Analytics interview in 8 weeks:
1. Search for "[industry]" and other relevant keywords (example: "Financial Analyst", "Banking Analyst", "Risk Analyst");
2. Grab 20-30 job descriptions;
3. Use an LLM to let you know the core tools, skills, and processes;
4. Use that information to identify 1-2 projects that cover them;
5. Create those 1-2 projects, placing keywords in your documentation related to the core tools, skills, and processes;
6. Create your portfolio with these projects (only these);
7. Rewrite your resume with keywords related to the core tools, skills, and processes;
8. Update your LinkedIn to keywords related to the core tools, skills, and processes;
9. Apply to jobs every day and send a cover letter after each application;
10. After sending the cover letter, reach out to the company's hiring manager โ use A/B testing and keep iterating;
11. Do this every day for two months and aim for at least 20 applications per day (Total: 1120 applications/min) โ 4 hours every day, 10 minutes per application;
12. Don't give up on weeks 1,2,3,4,5,6,7,8;
13. Do 9-12 only after you have done 1-8;
P.S โ Use A.I to do the heavy lifting (Research about the core tools, skills and processes, research about the company, finding relevant keywords), but use your brain when it comes to writing the cover letters and speaking to people).
P.P.S โ Most people don't give up because they don't have a plan. They do because they don't follow it.
3 weeks ago | [YT] | 182
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Mo Chen
Imagine spending 3 months building your portfolio.
- Dashboards polished;
- Code cleaned.
Now, imagine someone with half your skills gets the job.
Because their work looks like it solves a business problem, while yours looks like a course assignment.
Now imagine recruiters scrolling past your work, thinking:
โAnother one who just followed the tutorial.โ
This is the โproject โ portfolioโ argument.
Itโs not about how much you build.
Itโs more ... what your work says about you ... And who itโs built for.
3 weeks ago | [YT] | 133
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Mo Chen
KPIs are meant to be stable within industries, so if your KPIs change every week, theyโre really not KPIs, the same way that if your portfolio copies the last LinkedIn trend, itโs not yours.
This type of "work ethic" is what keeps most people at the bottom.
If you have that mindset and see a great data analyst work, you'll feel bored.
They anchor.
They own their numbers.
They build work that holds up, no matter whoโs in the room.
Want to stand out?
Stand firm.
Repeat the process.
3 weeks ago | [YT] | 106
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Mo Chen
Hereโs something I found later in my data career:
Spending 15 minutes sketching the logic before coding is one of the easiest time multipliers.
Thereโs always rework you avoid if you know where it starts.
4 weeks ago | [YT] | 73
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Mo Chen
4 lessons for those switching to Data Analytics:
1. If you're too cheap with your time to explore the data, you'll pay the price of insights that don't offer value;
2. If your portfolio looks like it was built in a weekend, donโt expect weekly interviews;
3. If you're copying case studies word for word, you're not building a portfolio. You're building camouflage.
4. The more you avoid ambiguity, the further you escape from real-world problems.
4 weeks ago | [YT] | 151
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