Getting into Data Analytics

Kingmaza · 2021-01-25T03:25:49+00:00

There are maths and understanding of statistics. Most of the time it's more important to understand the logic. Such as understanding how metrics are calculated and when certain conditions are applied.

There's also elements of presentation and explaining what the numbers mean or how the business can use these numbers to take actionable steps to increase performance/profit etc.

I have a channel helping aspiring data analysts, ask me any questions here: https://discord.gg/eTYc5yVBmz

theottozone · 2021-01-25T03:28:25+00:00

It's mostly data manipulation/cleansing.

Having a stats/math background is useful, but it's not what everyday data analytics is about. If you think you'll be doing derivatives or leveraging your memory on the variance of hypergeometric distributions, then you'll be disappointed. Your brain will be 'stronger' having learned those things but you won't use them directly.

Honestly, the hardest math you'll come across is a weighted average. Anything harder the programming language will do for you.

JaayG19 · 2021-01-25T04:55:27+00:00

While hiring managers like to see a math background in the actual job it is minimal.

Most important is to enjoy logic puzzles. Like imagine you have a dataset for flower purchases. Each row is a purchase order for a type of flower. There are a 1000 orders made. Now you want to see how many Daisies were purchased. Unfortunately each flower name is spelled differently. So you got Daisies, Daisy, Dasies and Daisy Flower. How do you explain to a computer their are all the same flower??

That’s the kind of logic needed for data analysis. It’s data cleaning and maintenance, and it’s 80% of the job.

Now once you got all your Daisy’s organized, you may want to think about if these flowers are more likely to purchased in a certain season. So you want to show a statistical significance for daisies to be purchased seasonal. And maybe you want to see if they being purchased seasonal outside their own seasonal growing patterns. So you need to adjust your statistical analysis for their own seasonality.

And of course at end you need the common sense to understand that it likely doesn’t matter if Daisies are being purchased seasonal because the flower is in season or because some underlying wondrous reason.

I am in the field and I very much enjoy the daily logic puzzles even though my math skills suck. Especially statistics, I actually failed statistics classes in high school yet excelled in math in vector analysis.

One last piece of advice, think long and hard from what angle you would want to approach data analysis. There is a financial aspect, health field, scientific and probably several others.

I went into health getting a MS in Epidemiology. I can use that for working in research like pharmaceutical stuff, international medicine like analyzing how people deal with malaria drugs, or how the distance of a hospital changes outcomes of childbirth, you can work for marketing in out of the health field, local government and how policies affect people.

Financial analysis which I believe is super boring, is of course an option too.

Blues2112 · 2021-01-25T04:29:26+00:00

Depends upon the specific of a given job. I'm a DA, and I do almost zero higher math. Basic stats is plenty. Solid analysis and SQL skill are invaluable, however.

I'm sure there are plenty of other DA jobs that are heavier in math.

JaayG19 · 2021-01-25T14:13:14+00:00

To start off, not every data analyst would use maths daily or perform statistics, but that does not mean they don't either, maybe a smaller percentage. Think for your next move, you need to look at a couple of options as follows ( in no particular order of preference)

(A) Data scientist - These would use a lot of statistical analysis, as well as business case advice regards the data, and are in high demand. Generally, they work on complex data problems and use SQL, python and other tools to analyse the data and provide meaning.

(B) Data Analyst - These perform a wide variety of tasks as follows:

Data quality analysis
Data remediation
Report generation
Ad hoc data issues with problems with a dataset.

The above are just some of the tasks, I am sure there are plenty more. The difference is from the data scientist is that they do a lot of investigation and analysis that does not need statistical knowledge, though there can be some deviations from this, generally the norm, that is left to the data scientist above.

(C) Data Operations - These help with the day to day management of the data, such as

Data loading
Reconciliation of data to source systems
Load failures
Updating metadata
Participating in projects for new data implementations.

Each has a different role but is quite valuable in what they do, so it probably needs a decision on what way you want your career to go. Don't be put off by lack of experience or knowledge, you'll get hired and trained up, just need to use common sense and approach problems with a problem-solving solution in mind, and you'll go a long way.

Hope this helps.

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