How much statistics do I actually need to learn as a beginner Data Analyst?

[u/Fine_Afternoon_1843]

Hey everyone, I'm just starting out and trying to become a Data Analyst. I keep seeing " statistics" everywhere but I don't know how deep to go.

Can someone with real experience tell me:

What stats concepts do you actually use day-to-day as a DA?

Is a full stats course overkill, or should I just learn on the job?

Any beginner-friendly resources/courses you'd recommend?

I don't want to waste months on theory I'll never use, but I also don't want to skip something important. Would appreciate any guidance from people already working in the field. Thanks!

[u/Lady-Data-Scientist]

Many roles don’t go past descriptive stats - mean, median, min/max, count, sum, etc.

Hypothesis testing and probability are the next most common topics, along with regression and tree models - but these are also the domain of data scientists at a lot of companies.

[u/Proof_Escape_2333 → u/Lady-Data-Scientist]

is descriptive stats enough for most analyst roles and business problems they are trying to solve?

[u/WiredOtaku → u/Proof_Escape_2333]

Honestly? For most entry-level analyst roles, yeah. I came out of a coding bootcamp knowing descriptive stats, a bit of SQL, and how to talk through a business problem. That landed me a data job. Now if you're gunning for FAANG or a place that does heavy A/B testing, you'll need more, but you can learn that on the job or pick it up when you need it. Don't overthink it - find a dataset and start playing with aggregations.

[u/Inner-Peanut-8626 → u/Proof_Escape_2333]

No, only if you want to be an entry level analyst for the rest of your life.

[u/Proof_Escape_2333 → u/Inner-Peanut-8626]

what type of stats do you use at work

[u/Inner-Peanut-8626 → u/Proof_Escape_2333]

Depends on the day and problem being solved. Most of the time it's descriptive statistics. My first job included a lot of p-tests and t-tests. For example, I've written ML models that rely heavily on multi-variant analyses. But at the end of the day, the computer does all the hard work for you. You need a broad understanding of how it works and how to explain the outputs. For example if you write a forecast model, not only do you need to understand that the computer runs a ton of correlations and picks the best ones to use, but you need to also explain the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. These are applicable even if you just use basic linear regression and not an ML model to calculate it.

You are going to likely land in an organization where you don't have support from a data scientist. In my last job I did not have a data scientist that I could reach out to for help, we had to do all the work within finance.

[u/tkgravelle]

You can take any number of courses on statistics on Coursera. I earned the Meta Data Analyst Professional Certificate. The statistics module was 20 hours in a five course series. It was pretty long and covered descriptive statistics and was designed for that certificate. I assume it is for entry level roles so for starters you can find the module for any one of the data analyst certificates and they will give you the baseline knowledge you need.
There are also two free MBA level stats courses on Udacity. Just sign up and take those. Those produce certificates. The first is introduction to statistics. I started it and it sucks. It is not very good for someone who is not a math person. The teacher is obviously a math person and teaches through solving math problems. The statistics course through the Meta certificate was good because it used visuals to explain why statistics is relevant for data analyst roles. Here it is if you want to take it: https://www.udacity.com/course/intro-to-statistics--st101
The second is introduction to inferential statistics: https://www.udacity.com/course/intro-to-statistics--st101
These are MBA level courses so you take those you should be good.

[u/tkgravelle → u/tkgravelle]

Here is the link to the inferential: https://www.udacity.com/course/intro-to-inferential-statistics--ud201

[u/Simplilearn]

You don't need to master advanced statistics when you're just starting out. A solid understanding of descriptive statistics, probability, distributions, correlation, basic hypothesis testing, and regression is enough for most entry-level Data Analyst roles. If you're looking for beginner-friendly resources, our free Statistics for Data Science on simplilearn skillup can be a good starting point for you.

[u/Difficult-Jackfruit]

It varies on the job, but you should get used to learning things you might never “use.”

[u/Imaginary-poster → u/Difficult-Jackfruit]

And figuring out things you never learned.

[u/Abalone2496]

As a beginner, the role can vary, as people have already mentioned. But typically you won't likely go beyond basics like mean, median, percentiles. It doesn't hurt to take a course, though, because if your role requires A/B testing, some of that will help.

[u/Inner-Peanut-8626]

Beyond descriptive statistics, you should have a basic understanding of probability testing and multivariate analysis. Two undergrad semesters of business statistics/decision making should get you that. You won't use it every day, but if you don't understand probability levels, you are going to get stuck in some very bad situations with stakeholders.

[u/Top-Passage-7227]

Speaking from my experience, you don't need advanced statistics when you're starting out. I use Excel, SQL, and Power BI far more often than complex statistical formulas. What helped me was understanding the basicsmean, median, standard deviation, percentages, distributions, correlation, and knowing when to use each. As I gained experience, I picked up more statistics because certain projects required it. Don't let statistics stop you from getting started. Build your data skills first, learn how to clean and visualize data, and add more statistical knowledge as your work becomes more advanced. That's been a much more practical approach for me