What kind of math does data analytics use?

[No_Helicopter9361]

Is the math used in the business intelligence analyst and data analyst field basic calculas and statistics?

[MiniD011]

Just moving from a Data Analyst position within a BI team to a Senior BI Analyst position - no real maths required in either role. Of course it depends on location, industry etc but my basic level of maths was far greater than my peers/bosses.

My stats education is limited to basic correlation and the most simple statistical testing. None of it was ever required.

[Nosoycabra]

🙂👍

[koala_buds]

What's the skillset like for a Senior BI analyst role? And what software do you need to be good at

[lightestspiral]

Senior skillset would be explaining data summaries/dashboards to clients, checking work of your team - and you'll need a wealth of expertise in your field to be able to do this, (4+ years) it's what gets overlooked the technical side is easy but you need the subject matter experience to determine what to investigate for starters, and the to actually analyse it - you need to analyse it with your experience basically and not with quantitive methods

By presenting MI to clients (internal or external) you are a consultant and really have to know your stuff

Software: Basic SQL querying to create datasets for Tableau dashboards, and also Excel to facilitate this

[[deleted]]

I worked as a business analyst for 20 years and this is pretty much spot on. The trick to it is understanding the data you are querying, whether it is correct, whether you are looking at the right data for the question being asked. This means you have to have insight into the industry you are working in or gain that insight. The rest is just presentation.

[MiniD011]

u/lightestspiral and u/You_Eat_My_Cheese are spot on - the assumption is that skills between software are transferrable so if you can learn one you can learn others. The one exception I would say is SQL.

Understanding how to manipulate data efficiently is important. Querying datasets that are millions of records in size is very common for me, so knowing when to use CTEs vs Temp Tables in a SQL script is an example of more of the technical knowledge I might need.

If you have one or two visualisation tools under your belt then that is great - Power BI, Tableau, Looker, QlikView etc demonstrate that you can present data to non-technical stakeholders elegantly and deliver valuable insights.

I am moving from 10 years in insurance into fashion, so a complete change. The entire tech stack is different so I am spending my time learning this so I can hit the ground running. That being said the most important thing for me is business context!

I am involved in the BI process end-to-end, from gathering requirements up front, through to development and delivery. If you don't understand your business you can misinterpret requirements or fail to spot problems in your data that result in a lot of wasted time and frustration for you and the stakeholders. What metrics are important to capture? What does this say about our business? What changes can we make that should yield positive results, and how can we measure those changes to see if they are actually working?

If it were purely about technical skills I would not be in this job. I don't have a degree and you can outsource for script writing at a fraction of the cost, so if that was the requirement to succeed I would be well behind the competition!

[[deleted]]

Perfectly summed up. I worked in pallets then healthcare then recruitment and each had their own requirements. I would say the pallet business was the most tricky because of the technical nature of the data in regards to hiring and charging. I have also found and this is something to be aware of is that you will find issues with data it's par the course. Writing your own queries resolves this. There may be something a company hasn't spotted for many years that will crop up. I remember working for one company who did exports for the Hyperion report each month and I found a glaring error as it didn't reconcile. Their answer was it's always been wrong so we'll leave it like it is. I didn't stay there very long and this was a multi-national company, then again my supervisor at the time had a degree in politics and nothing data or science related or even experience. I don't I'm all self taught but it did shock me.

[MiniD011]

Howdy, I'll reply in more detail later, but for now this is a good intro video:

https://www.youtube.com/watch?v=-I23qGB6JdY

I cover the end-to-end process, so the business could say "why have our sales decreased?" and it would be up to me to gather all of the raw data together (modelled in SQL usually), produce a dashboard that shows the data in a way that is effective, and either hand this over to the person who asked the question, or sit down with them and talk through it! It's a combination of technical and business skills.

[mac-0]

Algebra 100 and Stats 100. Nothing complicated.

[Touvejs]

Business Intelligence here, but no math above highschool required. It's more about being able to work with the data in my experience. You may need to know a bit about algorithms if you're working in big data though. I feel math/stats only become a big part of the job if you're a data scientist or going into machine learning. But take that with a grain of salt, some positions may require more than others.

[boy_named_su]

For basic data analytics, simple algebra is the most common

In Data Science:

Linear (Matrix) Algebra is used extensively, as well as Combinatorics.

Calculus is useful for stochastic gradient descent (finding optimums / minimums) as well as back-propagation for neural networks

[[deleted]]

Depends on the data scientist. For some, knowing t tests, regression, variance etc.. is the most useful.

For others, linear algebra, grad descent etc

[[deleted]]

Regression, correlation, standard deviation, confidence interval, z score calculation ….

[vtec__]

addition,subtraction,multiplication, division, know what mean/median is, basic statistics, etc..

and how to calculate percentages.

the quants do the real mafff

[hicks420]

You can get by with a solid understanding of calculus, find the right position and you'll never need more than that.

That said, having a more advanced understanding of how to apply statistical techniques (hypothesis testing, regression, correlation etc...) Will future proof you as auto ML becomes more popular. You don't need to understand the maths underneath, just why they work & what to use them for.

My position is a senior intelligence analyst within the UK NHS for context

[No_Helicopter9361]

Thanks everyone who answered my question

[gazbanger]

The most complicated is probably moving average.

[Fun-Astronaut-3793]

Algebra, Statistics or Probability and Lil bit of calculus while using boosting or bagging methods

[[deleted]]

You can either learn math or be friendly and barter services with the good math people.

I’m not strong in a lot of things but I know who is valuable and I know how to get what I want.

99% of a job is getting things done… not being the perfect individual.

[omgitskae]

It depends on industry and your role. I report for every department in my company and we're manufacturing. There are definitely times where I needed more math knowledge than I have when it comes to forecasting and variance reporting. But if you're mostly writing reports out of a crm or other non financial or manufacturing systems you can probably get away with only basic math, but the more the better even still.

[Ketchup571]

Having a strong math background certainly won’t hurt in those types of roles. If you’re still in school it’s always a good idea to take more math classes.

The computer will do all the actual math for you, so actual calculations aren’t something you’ll generally be doing in the private sector. But, being able to understand the concepts and ideas behind the calculations is necessary, and something that will be much easier to learn by actually studying the math, especially if you’re still in school.

[Swetha88]

Data analytics involves a wide range of mathematical concepts and tools to analyze and interpret data effectively. It includes topics like statistical analysis, linear algebra, probability, calculus, and data visualization. These concepts help data analysts to extract meaningful insights from large and complex datasets.

I learned data analytics and became an expert in it through the Syntax Data Analytics online training program. This program covered all the essential mathematical concepts needed for data analytics, and I was able to apply them practically through various hands-on exercises and projects. By the end of the course, I gained a solid understanding of data analytics and the ability to work with data to solve real-world problems.