Mo Chen

You don’t need to master calculus to land your first data job.

A lot of beginners get stuck thinking they need to learn advanced statistics, machine learning, or complex math to get hired.

The reality is: YOU DON'T.

You can stand out with just basic math — if you know how to apply it.

If you're analyzing the salaries of 10 employees and:
- 9 earn $50,000
- 1 earns $500,000

The average salary? $95,000.
The median salary? $50,000.

Which one reflects reality better?
I think you know the answer to that.

But why?!
Because one extreme value skews the average — this is called positive skew.
It's a positive skew because one very high salary is pulling the average up, even though most people earn much less.

This is why companies care about more than just averages.
It's why analysts who understand context are more valuable than those who just know formulas.

If you know when to use averages vs. medians… If you understand skewness… If you can connect numbers to real-world decisions…

You're already ahead of 80% of the competition.
You don’t need more math.
You need more clarity.

2 days ago | [YT] | 281

Mo Chen

It's not the first time that someone reaches out and asks: "I've never done a data project before—how do I actually begin?"

I remember that blank feeling. No portfolio. No idea where to start. Just the wanted to get going.

Here's what I tell every beginner who asks me this:

Step 1: Pick a dataset that feels interesting. Don't overthink it. Something simple—maybe sales data, or your city's bike-sharing records. Kaggle, your gov data, and Google datasets are all great places to look.

Step 2: Define a single, clear objective. This is where most people freeze up. It can be as basic as: "Is there a monthly trend?" or "Which product sells best?" If you have a question you're curious about, that's your objective.

Step 3: Get familiar with your data. Open it up. Glance at the columns and values. Are there missing values? Does everything make sense? Write down your first impressions.

Step 4: Clean the data just enough to analyze it. Remove obvious errors, fill in or drop missing values, and standardize your columns. All you want at this stage is clarity.

Step 5: Run simple analyses — averages, totals, min and max, basic counts. Plot a few charts (bar, line, pie). Look for anything that stands out. This is the fun part.

Step 6: Summarize your findings in clear, plain words. "Product A sold the most in June." "More people use bikes on weekends." Keep it simple, honest and data-backed.

That's it. No fancy algorithms, no big words. You've just finished your first real project.

I'm guessing it should take you no more than 30 minutes. If it does, you're making things way more complex than what they have to be.

You don't need permission to start. Just pick something and go.

Share this with anyone who's struggling with data projects.


The Ultimate Data Portfolio: If you're looking for the best data portfolio for job seekers:
mochen.info/lp-ultimate-data-portfolio

The Ultimate Resume Builder: If you're spending more than 2 minutes fixing your resume to fit specific job descriptions:
mochen.info/lp-ultimate-resume-builder

2 days ago | [YT] | 250

Mo Chen

I bet most of you doubt whether your projects will meet high standards.

And you know what?

I used to wonder the same.

The urge to keep changing things never goes away, but chasing perfection is a trap.

Good work stands on proof, not on gut.

Here’s how I avoid it:
- Set clear benchmarks before I start;
- Often revisit the project’s goal to guarantee I’m heading in the right direction;
- Check results against those benchmarks after each step — avoiding the vague sense of “good enough.”;

I also apply some principles that help me stay on track:
- Getting feedback from peers who have nothing to do with the outcome;
- Writing a short project summary as if I were my own toughest critic;
- Keeping a checklist for reproducibility, clear data lineage, code comments, and logical flow;
- Running a post-project assessment to answer: What surprised me? Where did my assumptions break down?

These practices help you improve your projects and iterate effectively.

Whether you:
- Rerun your analysis on a new data slice;
- Ask someone to follow your process and note every piece of feedback;
- Revisit an old project once a month and rate it against your standards ...

the work doesn’t truly end when you finish the project.

How do you know when it’s time to stop and present something?

When the analysis answers the business question, holds up to peer review, and is easy to repeat.

Not when it simply feels "good enough". Not when it doesn't feel "perfect".

Do you have any other techniques that help you feel more confident about a project’s quality?

3 days ago | [YT] | 85

Mo Chen

Taking courses in Excel, Tableau, Python, R, and SQL is a solid start. 

But learning doesn’t happen there.

Let me explain.

Job seekers often face rejection even after getting these skills because they simply don't put them into practice.

But if you:

1. Start a Personal Project: Analyze public datasets and publish your findings;
2. Volunteer for a Non-Profit: Offer to analyze data for local charities;
3. Freelance Small Tasks: Platforms like Upwork or Fiverr offer small tasks that can make you have great testimonials and some work;

An example from Financial Services:

1. Analyze your government open-source data to identify trends in consumer spending;
2. Share your insights on LinkedIn or a personal blog. This could be done in a day and highlights your analytical skills.

Remember, employers value critical thinking and problem-solving over memorization.

Skills are proven through action, not certificates.

4 days ago | [YT] | 201

Mo Chen

Taking courses in Excel, Tableau, Python, R, and SQL is a solid start.
But learning doesn’t happen there.
Let me explain.

Job seekers often face rejection even after getting these skills because they simply don't put them into practice.

But if you:

1. Start a Personal Project: Analyze public datasets and publish your findings;
2. Volunteer for a Non-Profit: Offer to analyze data for local charities;
3. Freelance Small Tasks: Platforms like Upwork or Fiverr offer small tasks that can make you have great testimonials and some work;

An example from Financial Services:

1. Analyze your government open-source data to identify trends in consumer spending;
2. Share your insights on LinkedIn or a personal blog. This could be done in a day and highlights your analytical skills.

Remember, employers value critical thinking and problem-solving over memorization.

Skills are proven through action, not certificates.

5 days ago | [YT] | 320

Mo Chen

80% of employers are more impressed by a portfolio than a CV (Jobvite).

Let's talk about building a one-stop shop for potential employers.

Something that shows: bio, education, projects, and contact info.

A portfolio is your personal brand's front door.
It shows your skills, achievements, and style.
Employers want to SEE what you're made of.

Here's how to do it in 24 hours:

1. Choose your platform;
2. Write a clear, concise bio: Who are you? How can you help?;
3. List your education and certifications – No fluff, just facts;
4. Showcase 2-3 of your best projects. Use screenshots and clear descriptions;
5. Make it easy to contact you – Add a contact form (or even better, a meeting link).

If you're targeting Financial Services, use it to display financial modeling projects.
In Healthcare, highlight patient data tracking systems.
In Marketing, show off your campaign results with A/B testing.

Your portfolio is more than a site.
It's your story, told your way.

6 days ago | [YT] | 147

Mo Chen

I recently visited Oslo just for a weekend. First flight out on Saturday and last flight back on Sunday. I didn’t take a single day of annual leave.

I come across lots of people in their 40s, 50s and 60s who are itching to travel because they didn’t do so whilst they were younger. They had the money, the energy and probably the time too, but somehow something always came in the way.

The older I get the more I realise that there will never be a perfect time when I have no commitments, nothing to do and I can just freely travel.

After all, life is not as simple as when I was just 20 or 25.

This is why I decided to make an effort and just go travel.

Making memories with people I love is single-handedly the most valuable thing to me.

There is nothing that is more precious than time well spent; time with family, time with close friends and time with our dog (Rocket).

I just wanted to share this with you in case you were thinking about traveling, going outside your comfort zone, trying out new things but somehow, something always came / comes in the way.

There will never be a perfect time to do anything. Especially when you get older.

The best time is always right now.

FYI - For anyone who’s interested, the YouTube video link is here: https://www.youtube.com/watch?v=vNu3r...

1 week ago | [YT] | 16

Mo Chen

Data analytics is not as complex as it seems.
Many think it requires advanced degrees, but the reality is different.

Excel, a tool most professionals already have, can be your entry point into this field.

It's not just for budgets!!

Here’s a quick way to boost your skills and showcase your capabilities without a PhD:

1. Start a Personal Project: Pick a simple data set relevant to your industry;
2. Use Excel Features: Experiment with different formulas, create pivot tables, and choose the visuals that best align with your data;
3. Derive Insights: Document all findings and think of how you can explain these insights in a simple way.

Why Excel?
1. It's accessible;
2. It's intuitive;
3. It's powerful enough for most analytical needs.

Financial services and healthcare sectors often rely on these foundational tools for decision-making.

It's easy to start and you can achieve 80% of your actual job as a data analyst just with it.

1 week ago | [YT] | 245

Mo Chen

Data Projects Feel ILLEGAL Now: 2010 vs 2025

The barrier between "thinking about" and "actually building" data projects has completely collapsed.

What used to take months now happens in conversations:

2015: Data Analytics Project Timeline

1. Spend weeks learning SQL basics;
2. Struggle for days importing data;
3. Write hundreds of lines of code with countless errors;
4. Google endlessly to fix broken visualizations;
5. Finally create basic insights after months of effort.

2025: Data Analytics Project Timeline
1. "ChatGPT, scrape these 5 job descriptions and tell me what project would impress these companies";
2. "Create a step-by-step plan for building a churn prediction dashboard";
3. "Write the SQL queries to extract this data";
4. "Debug this code that isn't working";
5. "Generate visualization code for these insights".

It feels almost criminal how easy it's become.

1 week ago | [YT] | 167

Mo Chen

If I only got 24 hours to land an interview, here's what I would do:

1. Upgrade my portfolio with the project that best fits the roles I'm applying for.
If I had no projects, I would spend 10 minutes searching for job descriptions and see a project that I could do;

2. I would put both links in my LinkedIn's "Featured section";

3. I would find hiring managers on LinkedIn, identify something specific they've written or built, and send a thoughtful 3-sentence message referencing their work – if they replied, I would deepen the conversation until I could ask them for a referral;

4. Improve my resume with quantifiable achievements that align with job descriptions;

5. I would create a 60-second video introduction of myself explaining: the specific problems you solve, how you solve them, and the results of having me on the team.

Then I would use the remaining 18 hours to apply.

1 week ago | [YT] | 135