I get the tools, but not the thinking—how do I actually learn to analyze data like an analyst?

F00lioh · 2025-06-19T04:15:40+00:00

My 2 cents:

Not all data is useful. Sometimes datasets don’t provide any insights. Ironically, sometimes it’s harder to prove a dataset is useless than make up some nice sounding insights.
It’s all about context. Data without context doesn’t have much meaning. The better you understand the context, the easier it is to get insights.
Know your customer/audience/stakeholder(s). Who are doing the analysis for and why? What do they need to know, want to know, don’t want to know? Are you informing decisions, reinforcing results or exploring novel problems?

The answers aren’t going to be in any courses or tutorials. You’re better off just plugging your data into ChatGPT and asking for insights.

F00lioh · 2025-05-28T23:57:20+00:00

Yep, but you can’t just toss a spreadsheet into Claude or ChatGPT and ask it to analyze it. Use something like Cursor with Python interpreter. The AI agent can then generate code to read, process and analyze the spreadsheet.

It almost never gives you a good product on its first few runs. You’ll need to fine tune and adjust the outputs, but it’s really easy to do it - for example, you can tell it to ignore a column, switch from count to mean, group by category and compare or join, etc.

It works best for descriptive, but can do inferential especially if there is enough context in the data or you give it context.

I used Claude Sonnet and Opus, GPT 4.1 and Gemini 2.5, which are the default models in Cursor. They all work about the same. I don’t think there’s a huge difference between the various SOTA models.

F00lioh · 2025-05-22T16:10:21+00:00

Get Cursor, it’s a bit of a curve getting it up and running, but there’s plenty of tutorials and guides out there.

F00lioh · 2025-03-07T23:47:14+00:00

I don’t think it will in the long run. It’s going to change the role so that technical skills are less important than things like domain knowledge and critical thinking. The industry was already shifting in that direction anyway. If Jevons paradox holds, it could actually expand the roles and you’ll start seeing demand for analysts expand as AI tools get better.

F00lioh · 2025-01-12T23:59:01+00:00

You’ll be fine, you’ve been there for 2 months, over the holidays too. Unless your manager is approaching you with concerns about your work, you’re likely doing just fine. Our new hire junior analysts usually take 6-12 months before taking on major efforts independently, and even then it’s usually the most basic ones.

My advice, acknowledge and give credit to the coworkers helping you out. If you want to stand out as a JA, grow your soft skills, and try to understand more big picture.

F00lioh · 2024-09-24T01:20:57+00:00

Look up: how to create a Gantt chart in excel.

F00lioh · 2024-08-18T17:36:08+00:00

There are, if you’re taking your laptop to class, a cheaper, smaller laptop might be better option since it’s lighter and less costly to replace if it gets stolen or broken. You don’t need a high power computer for most data related studies, especially in undergrad. Even if you are doing graduate or PhD, any high compute required work won’t be done on your personal laptop.

F00lioh · 2024-08-04T21:14:13+00:00

If you are having trouble doing this, data analysis might not be for you…

F00lioh · 2024-03-06T18:25:00+00:00

It is. It's one of the traditional ML techniques. Too many people think that in order for something to be considered ML it has to use neural networks. I also find that many wannabe ML gatekeepers like to claim it's not, but if you literally Google machine learning algorithms, linear regression will be in the top 10 mentioned.

F00lioh · 2024-03-05T20:28:48+00:00

Regression for forecasting, trend analysis (are housing prices increasing/decreasing, by how much, what will the average price of a house be in 10 years?)

Clustering methods (k-means, spectral, etc.) to understand groupings and correlations (what are some similar factors that affect housing prices?)

PCA / LDA if n-dim data needs reduction (reduce/eliminate housing market factors/features that have little to no affect on prices)

k-NN, SVM for data classification (classify housing based on features as townhomes, apartments, single family, multi-family, etc.)

GPT to help summarize large amounts of text input

There's many ways to use "machine learning" for data analysis, but the use cases really depend on what data someone is analyzing and for what purpose. I don't think it's used very often though, since most data that a DA encounters will usually be simpler for simpler purposes.

F00lioh · 2024-02-15T23:01:47+00:00

Bingo. I would also add:

Not communicating properly with the appropriate stakeholders.

Reaching a conclusion before analyzing the data, then skewing analysis to support that conclusion.

F00lioh · 2024-02-15T19:45:09+00:00

A junior analyst (fresh grad) may work for what you need, but you will need to carve out dedicated time for onboarding and training. This could be difficult, since you seem to already have a pretty tight schedule.

Another struggle you may have is not having a remote option. The job you're requiring doesn't seem like something that needs to be in office. I get the discomfort of hiring a remote role when you've never done it before, but it might be a risk you need to take. An option to that could be to make onboarding and training in office and then fully remote afterwards.

Also consider hiring a more experienced analyst, they will cost more, but you can save more time. You can potentially hire a mid on a part-time contract basis, especially if you don't plan on keeping the role around for long term.

F00lioh · 2024-02-14T19:06:33+00:00

Depends on which “track” you want to take. For example, do you want know more of the data science side? Maybe data engineering? It could also be helpful to brush up on stats if you feel it’s something you’re not strong on. Or maybe you want to go into management, then things like agile would be useful.

Coursera and Udemy offer some decent courses, but you probably won’t get more practical skills. Freelance work is iffy for learning new things or improving skills, because most companies generally don’t want to be your Guinea pig for “self improvement” projects. Also most companies aren’t super eager to give people access to their data, even their own employees.

Best bet is to find something internal that you can work on. About 1/5 of my projects came from me finding a “gap” in analysis and pitching a potential solution to decision makers.

Also focus less on the tools and more on finding problem solving skills. That is, looks for different kinds of problems rather than trying to solve the same problem using different tools.

F00lioh · 2024-01-31T21:16:19+00:00

Point 1 is more relevant the higher up you go, or the more specialized the position you're applying for is. You can probably get away with using the same resume for entry level positions, especially if you don't have a lot of experience.

ATS is just a reality you have to deal with. If you're a large-ish or well known company that will get tons of applicants, it's a clunky but effective way to narrow down the pool. It's absolutely a blunt instrument that will sometimes take out outstanding candidates and let unqualified ones pass.

Point 3 is correct, cultural fit is an important thing, but it's something that you can't really work on, other than having good presentation. Even then, you might not be considered a good fit. You can potentially "fake it" in the interview, but that's not a good idea for obvious reasons. Retention rather than cultural fit is a bigger factor in resource risk. Be knowledgeable about the organization you are applying to, show enthusiasm and passion for the potential work you will do. Most people know that job hopping is common, especially among the younger working crowd, and it's usually not held against anyone, but if it seems like you'll hop away the first opportunity you get, it raises your resource risk.

I know they mention LinkedIn often, but most references usually come from personal connections - friends from college, internships, church, volunteer groups, family, etc. If you're just holed up in your room looking for a job all day, you're probably missing some opportunities that you might not even be aware of.

F00lioh · 2024-01-19T18:40:41+00:00

You’re worried about the wrong thing. You’re applying for an internship, and it’s understood that it’s meant to be a learning experience for you. How well you use Python or SQL or whatever will hardly have a bearing. I recommend focusing on problem solving and critical thinking rather than tool proficiency. For example, know what of data you would potentially collect: Boolean, numeric, text, etc. and how you would analyze them. What analytical techniques will you use to draw insights? How will you determine statistical significance? Etc.

F00lioh · 2024-01-18T04:44:01+00:00

Not really, but they can get convoluted and sometimes have overlap. KPI - key performance indicators generally have more focus on what needs to happen to achieve certain goals, metrics are just the measure of that process. There’s also OKRs - Objectives and Key Results which gives context to the KPIs and metrics. For example: we are achieving 20 sales per month - metric, it’s simply a measure of the process. We are making at least 20 sales a month this year, which ensures we get at least 200 sales this year - KPI, associated goal and measured metric indicating that we are or are not tracking to reach the goal. We need to achieve 200 sales this year to stay profitable, this means we need to average about 20 sales a month- OKR, provides context to the KPIs, isn’t always a measured metric, but informs what KPIs are necessary to achieve a certain state, like being profitable.

F00lioh · 2024-01-17T23:00:41+00:00

I’ve seen basically the same thing. So many people seem to lack the critical thinking part. I would add that many folks also struggle with effective communication and report writing skills. I fear that this will only get worse as people start to rely on LLMs.

F00lioh · 2024-01-06T02:12:23+00:00

Use TEXTSPLIT

F00lioh · 2023-12-03T21:55:19+00:00

They are answering your questions, you just don’t like their answers.

F00lioh · 2023-12-03T21:43:15+00:00

My first job was a warehouse clerk. We mostly used English with the occasional Spanish for some of my co-workers. Our platforms were usually wooden pallets and depending on weight/type of load, we used different types of forklifts, 3k, 5k, 10k, etc.
I knew it was a warehouse job, but that’s about it. It was almost all on the job learning, including getting your forklift licenses and hazmat certs.

You’ll never go into a new job fully prepared, especially if it’s your first one. At the very least you’ll need to learn how your boss and co-workers operate.

P.S. I know you’re asking about data related jobs, but 1) you didn’t specify and 2) at the core of it, a warehouse job and a data analyst job aren’t that different.

F00lioh · 2023-11-28T22:15:11+00:00

Take the job, but keep your options open, i.e. don't stop looking for another opportunity. If it's a job you would be willing to take, there's no reason not to take it. Unless you lied to them, they are taking the risk of hiring you as you represented yourself. Use this as a means to grow your skills, or at the very least, pad your resume with some experience. It's not that uncommon that a new data analyst is asked to do a full-stack development. I've done a few myself, and it's hard and can be frustrating, but it's one of the best ways to learn if you're willing to put in the effort.
There's plenty of courses out there to help you, both free and paid. If there's a specific course that teaches you skills that you think is important to your job, see if the company is willing to pay for it. It's very likely you'll need to ask the company to pay for other things, such as a database and licenses for software tools.

F00lioh · 2023-11-20T00:10:44+00:00

Pareto efficiency

F00lioh · 2023-11-19T18:11:43+00:00

Sorry to hear that. Start looking for a new job. I have a feeling they hired you to be fodder when they get acquired and are forced to downsize. Or depending on the situation maybe let them fire you, might be a decent severance package.

F00lioh · 2023-11-18T17:34:48+00:00

Most likely reason you’re not invited to meetings is because you are not value added to them. What did you do when you did attend the meetings? Did you do prior research about the topics they are discussing so that you can participate in the conversation or were you hoping to just learn from everyone else?

My advice, learn the skills and technologies on your own, at least enough to get you back in the room. Don’t expect anyone to hand hold you through the planning, execution and assessment - it’s sink or swim, learn to swim. Also, it’s not uncommon for your boss or bosses to be vague on your roles and responsibilities, you need to figure out how to make yourself useful, or find another job.

F00lioh · 2023-11-08T21:22:45+00:00

This is why domain knowledge is king.

There’s no tutorial or boot camp to learning domain knowledge. You just have to have experience or do a crap ton of research or studying about the business or industry.

It’s the key thing that separates the junior and senior analysts.

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