Ever wanted to pick the brain of one of our data pros?

That recently became a reality thanks to our new subreddit, r/mavenanalytics!

On July 31st, Chris and John took the time to sit down and answer some of the community’s most pressing questions in a Reddit Ask Me Anything (AMA).

Here are some highlights that you may have missed from the session!

Essential Skills for Today’s Data Professionals

Wondering what skills you need to land your first data job? Looking to future-proof your career in the age of AI? Chris and John dug into these critical questions and more!

With the rise of AI, what skills do you recommend analysts focus on?

Chris: LOVE this question, and it's something we've been thinking about a lot!

First off, I strongly believe that you will still need a strong technical foundation. Even as LLMs become insanely good coders, that will only get you so far without a solid understanding of core principles and best practices (i.e. relational data modeling, ETL, test design, visualization principles, etc.).

That said, the ability to memorize syntax and write complex code from scratch is definitely becoming less important, so I would spend less time there are nailing the foundational stuff.

When I think about "AI-proof" skills that will become even more important for data professionals, these are the things that come to mind:

- Critical thinking & strategic problem solving

- Business acumen

- Creative thinking

- Storytelling

- Emotional intelligence

Can you break down complex problems? Can you translate stakeholder comments into clear business requirements? Can you craft a narrative designed to persuade Bill from Marketing to act on the results of your analysis?

THESE are the types of skills that will only get more important. Combine those with a strong technical foundation and the ability to use AI tools effectively, and you'll be in great shape.

What is the level of math and statistics you believe someone should learn/know in this field?

John: Math and stats knowledge is often less needed than a lot of people think. If you absolutely hate working with numbers, don't enter the field. That's what we do. But you really mostly just need basic stuff... fractions (spend per customer acquired, this year revenue divided by last year revenue, etc). 

So yea, some basic stuff helps. It's good to understand things like a normal distribution, statistical significance, etc. But you definitely don't need advanced calculus. I'm talking about analyst roles here. If you want to be in ML, then stats is indeed more important.

What level of data viz skills do you recommend to break into a career as a data analyst?

Chris: Two pieces of advice right away:

PRACTICE really does makes perfect (you need reps to see real improvement)

Focus on data viz principles and best practices first, tools second

Some people are blessed with a natural eye for a design, but most need to practice data viz just like they would practice any other skill like coding or stats. One thing that has been super helpful for me personally is finding inspiration from experts in the data community (look into people like Bas Dohmen and Gustaw Dudek) and give them a follow to see the work they are doing. You can also explore projects in Maven Showcase to find some inspo.

Last but not least, remember that if you don't have a solid foundation, no tool will help. Really take time to learn about design, Gestalt principles, color theory, etc. That will go a long way to improving your data viz game!

For Power BI, is getting the PL-300 certification very important to get a job?

Chris: Think of it this way – employers want as much PROOF of your skills as possible. Industry-recognized credentials and certifications like PL-300 are one way to do this, and building a great project portfolio to actually showcase your skills is another.

I don't think PL-300 is a strict requirement for many jobs, but it certainly couldn't hurt. If you haven't already, we have a PL-300 prep course than can help you get ready for the exam if you do plan to take it.

What is the best visualization tool to learn first: Power BI or Tableau?

Chris: There is no "best" tool – it just depends on the context. Tableau and Power BI have strengths and weaknesses, but both require the same set of foundational underlying skills.

Instead of focusing on which tool and why, start by building those foundation skills with one of them. If you end up needing to learn another for a specific job, it will be a relatively smooth transition from there. Generally speaking, this is better than trying to master both at once, especially if you have no particular reason to do so.

Are there any skills that you think data professionals don't focus enough on, but should?

Chris: Amazing question!

From what I've seen, data professionals get way too excited about tools and technical skills, rather than the impact to the business – which is ultimately the only thing that matters!

If you want to truly make an impact and earn your pay as an analyst, you need to focus on the end goal. You could do the most impressive & complex analysis in the world, but if no one makes a decision based on it, what was the point?

"Make a bigger impact" is obviously easier said than done, but these are some of the specific skills that can help most:

- Analytical thinking (can you break down and solve a complex problem?)

- Measurement planning (can you clearly articulate what you're trying to impact and why?)

- Communication & storytelling (can you convince stakeholders to take action?)

Those skills matter much more (IMO) than the ability to write great code, but most people would rather spend time arguing if Excel is dead or whether Power BI or Tableau is best for data viz.

Not to mention that these skills will only become more important as AI continues to evolve!

Taking Your Data Career to the Next Level

Ready to take that next step? If you have been curious about pursuing a career as a data scientist or data engineer, this section is for you!

What would be a good starting point to land a data science role in the future?

Chris: I actually studied econ as an undergrad as well, but took a job as a Marketing Analyst right out of school.

Management consulting and business school didn't appeal to me, and I found that my econometrics and microeconomics classes were the most interesting and practical (which are closely tied to data science & analytics). I also took an AMAZING sabermetrics course about the statistical analysis of baseball, which sealed the deal for me and inspired me to pursue a career in data.

As far as next steps, it really depends on the path you'd like to take. My personal (biased) advice would be to start by looking for an entry-level Data Analyst or Business Intelligence role, which will help you build a lot of foundational hard and soft skills and set you up for a transition to data science in the future (this tends to be a common path, but certainly not the only one).

Make sure that you are building a strong skill foundation as well as your own personal brand to help in the job hunt (see my responses to some of the other questions here).

I personally loved working as an analyst for a large (1,000+ person) company as my first gig, since it gave me a lot of structure and opportunity for growth that might not be possible at a startup or smaller company.

What job titles might serve as a good bridge between IT auditing and data analytics/engineering?

John: RE: job titles serving as a bridge - it's a good question and the answer is... there are so many. 

In almost any role, there are ways you can use data you already have access to to help the company. I'm honestly not super familiar with what an IT Auditor does, but I bet you've got some data, and if you think like the owner of your company, I bet you could think about how you might analyze it to find ways to improve the business. 

I would start there, in your current role. Start being a data analyst there. Don't wait for someone to give you permission. Then whether you pivot within the same company or jump ship, you've got a higher lever of experience to talk to.

(Find the answers to the other questions here in the full AMA!)

How and from where should I start to learn data engineering fundamentals?

John: Great questions!

I'll say a few things about this, including answering a question you didn't ask first :)

0. I think you'll have an easier time getting your first role as a Data Analyst, and then you can continue to learn DE skills on the job, while someone pays you. I just think it's harder to break directly into DE, because the skills are a bit tougher.

1. Which skills to focus on to get the DE role - to be honest, that's not really our bag. But there is someone on Substack, Madison, who I love who shares exactly this type of content...roadmaps to become a Data Engineer/Analytics Engineer, etc. She's also on LinkedIn pretty actively, Madison Schott, and she's been on our Mavens of Data podcast in the past, talking about this. She rocks! Not sure if she's on here (Reddit) or not.

2. RE: projects - think of something you're excited about. Get data. Analyze it and solve real business problems. Good projects clearly communicate the business problem solved, FIRST, then get into the key insights, then finally show off the technical skills. The common pitfall is to just dump SQL/Python code and expect someone to start reading it... no one wants to do that. That's the fastest way to lose your audience. Hook 'em with the business problem, clearly communicated, what you recommend based on the insights found, then dazzle them with the code, which they may or may not read, but they will be impressed by AFTER you've shown them you can provide value to a business.

Bonus points RE: projects if you know the industry you want to work in and can find datasets that show you can solve the specific business problems Analysts in that industry focus on.

Do I need to know Python to progress to higher career levels?

John: Great question! So Python **can be** an extremely useful tool, but you may or may not ever end up needing it in your career.

It's super valuable in some cases, because it's really flexible. You can use it for data analysis, data science, machine learning, web scraping, APIs, automation, etc. It's probably one of if not the most flexible tool for people in data.

That said, there are tons of extremely effective data pros who have made lots of money for themselves and their companies without ever writing a line of python code.

So yea, do you **need** Python? I think need is a strong word, but it could absolutely come in handy.

Personally, I wouldn't recommend learning it first, because if you do, you'll need to learn data structures / relational table thinking, AND a coding language at the same time, which can sometimes be intimidating to people.

Instead I would do this...

1. Excel (very easy gateway to data work in my opinion, and also very flexible and useful)

2. SQL (extremely widely used, not too hard if you know Excel first, and not as many people who are truly strong here)

3. Power BI or Tableau (pick one) - both widely used, not too hard, and help to visualize your insights both on the job and in your project portfolio

4. [BONUS] Python - now you're ready to tackle this one, in my opinion

Other Notable Questions

This AMA was full of incredible questions that gave Chris and John the opportunity to provide great insights for the data community. Whether you’re a seasoned data professional or new to the field, you can take something from this session.

Here are a few other questions that you don’t want to miss:

How do I justify myself after a really long career gap (6 years) in my resume/CV and in a job interview?

John: This is a question we get a lot, and it sounds like you're already being pretty thoughtful about it and have your eyes wide open about concerns potential employers have.

So let me just run through some of the things that employers (fairly or unfairly) think when they see a large gap on a resume. Then it's your job to steer them away from those things and into your ideal story, which we'll cover, too.

Things employers worry about with career gaps…

- is the person lazy?

- are they not very bright? No one else wanted to hire them?

- maybe they don't really "need to work" / have family taking care of them / etc and I couldn't rely on them to stick around long-term?

- maybe their skills are rusty from having a lot of time off

What you want to show potential employers:

- you're not lazy at all! You're passionate, self-learning, and really excited about the data skills you've been building. You've found the link between your background domain knowledge and the specific industry you're applying for roles in

- you've got some very specific projects, which relate as closely as possible to the business you are trying to break into. The more you can speak to and show off specific projects, the better. You want them to say "oh that project is exactly what we need at XYZ co. We should hire this person!"

- you can address the gap head on in conversations. It's absolutely nothing to be ashamed of. I'm so sorry you had to go through that. It sounds terrible. It also sounds like a completely legitimate reason. I would say you don't necessarily have to disclose everything, but you can say something like "I had some health problems, but I am overcoming them and am now fully ready to get back to my career"

Final side note about being open to the side door approach:

Sometimes if getting the data analyst title is tough, you might be able to get an adjacent role, like marketing, sales ops, etc. You'll often find that there is lots of data there, and you can start to build your "on-the-job" analytics experience there, and then later pivot into an official data title.

How do I make myself a competitive candidate for remote international jobs?

John: Really great question! Personally I'm a huge fan of remote work both on the employee side (I don't commute, so more time with my kids before and after work, and flexibility to work from anywhere) and on the employer side (we can hire from anywhere, so we can actually get better talent than if we were hiring within a 45 minute drive of the office).

So yea, remote is awesome, but the flip side of that is... everyone wants remote jobs, so it is SUPER competitive right now.

That said, don't give up. If you're motivated, you can do it. And I love that you are thinking about a 2-3 year plan. A person who is determined can do an ENORMOUS amount in 2-3 years.

Let's get into specifics...

1. Understand remote hiring in the US, and specifics around whether those roles can be filled by workers outside of the US -- this is a company-by-company thing, so you'll need to look at specific employers you are interested in and learn whether they offer remote roles and if they would do it outside the US. Unfortunately I don't have a list, but I bet there is one out there if you look for it. -- as a general rule of thumb, early stage startups will be more flexbile, larger companies will be more rigid

2. Understand how important it is to network. If you put yourself out there, and know people in the data community, you are much more likely to find out about opportunities early, before there are 100+ applicants. Reddit is good for that. LinkedIn also has a really great data community.

3. RE: skills and certifications... get your feet wet with Excel, then learn some SQL, then pick Power BI or Tableau (just 1 is fine to start). Those are the tools you should focus on. Skip Python at the beginning. It's super useful, but don't need it for most entry roles. You can learn it after you master the others.

4. Work on actual projects, and if you have a specific industry you want to work in, do projects around that industry... use Kaggle, Data.World, Data Playground... download data sets, and start practicing answering questions with data, and visualizing and summarizing the insights you find.

5. Put those projects into a portfolio so it's easy to share. Make sure the portfolio projects present well... start with the biz problem, then share the insights visualized, then share technical stuff like SQL code or workbooks, and make sure it includes action items. Avoid dumping SQL code first.

6. Learn from people who have recently broken into data careers. These people are often active here on Reddit, and LinkedIn. You can listen to my advice, but I've been in the game a very long time and things have changed. Look for people 2-3 years ahead of you who are thriving and learn from them. Success leaves clues.

As an Edtech entrepreneur, what have been your greatest triumphs and pitfalls on this journey?

Chris: Tough question!

In terms of triumphs, just the fact that we are hearing success stories from students almost every day about how we've changed their lives – landing jobs, earning promotions, shifting careers, etc – is unbelievably rewarding. It helps validate that we're living our mission, and inspires our team to keep pushing to build the best content and learning experience that we possibly can.

It was also really fun to make the INC 5000 for the first time, earn the silver creator award on YouTube, and cross 1,000,000 students on Udemy!

There have been plenty of pitfalls, too. Turns out building a bootstrapped EdTech company is really hard! Some days it feels like we have it all figured out, and others feel like we have no clue what we're doing :). For me, the hardest part of the job is being disciplined enough to say "no" to 99 good ideas in order to focus on 1 great one. That's something I'm still working on.

Wrapping Up…

This AMA session was full of information to help you take the next step, wherever you may be in your data journey.

You can read the full AMA here, and if you’re experiencing FOMO, be sure to join us at r/mavenanalytics; we’ll have more AMA opportunities with familiar faces very soon!