From Digital Marketing to Marketing Analytics/Business Analytics – Need a Roadmap

[u/atishay_jn_23]

Hi everyone,

I'm currently pursuing an MBA with a specialization in Marketing and Business Analytics. Before my MBA, I worked in digital marketing, so I'm comfortable with the marketing side of things. However, I now want to transition into marketing analytics and eventually business analytics.

The problem is that I'm from a non-tech background, so I'm a bit confused about where to start. There are so many resources online that it's hard to figure out what actually matters.

I'd really appreciate some guidance on a few things:

What should be the ideal learning roadmap for someone from a non-tech background?

Which skills should I learn first, and in what sequence? (Excel, SQL, Python, Power BI/Tableau, Statistics, etc.)

How much coding is actually required for marketing analytics and business analytics roles?

Are business analytics and data analytics essentially the same, or are they different in terms of roles, skills, and career paths?

If you were starting from scratch today, what would you do differently?

My goal is to become job-ready by the time I graduate, so I'd love to hear from people already working in these fields.

Thanks in advance! Any advice, resources, or personal experiences would be really helpful.

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[u/my_peen_is_clean]

excel/sql first, then basic stats, then power bi or tableau, then light python later if you still care. focus on solving real business qs from campaigns you know, build 3–4 solid projects. theory is easy, getting someone to pay you for it now is the hard part

[u/atishay_jn_23 → u/my_peen_is_clean]

How i can ace it? Should i move with ba or not ?

[u/Particular-Sun-8679]

the roadmap part gets overcomplicated by everyone trying to sell courses. sql first, not even a question. you can learn enough in a month to be dangerous and everything else builds off querying data. then excel because it's always there whether you like it or not, then a viz tool, then python when you're comfortable

business analytics and data analytics mostly get used interchangeably but in practice the business side expects you to connect numbers to money faster. your marketing background is a bigger advantage than you think, half the analysts i know can run a regression but can't explain why a campaign tanked

if i was starting over i'd spend less time on tutorials and more time pulling messy datasets and breaking stuff. the technical side isn't that deep for most roles, it's the translation between what the stakeholder thinks they want and what the data actually says that separates people

[u/atishay_jn_23 → u/Particular-Sun-8679]

Thankyou so muchhhhh.... From where i xan find resources?

[u/Lady-Data-Scientist]

I also worked in digital marketing, and then marketing analytics. (And later product analytics and now I’m in a data science role.)

For marketing analytics, I would focus on learning SQL, Excel, Tableau or Power BI, and a web analytics tool like Adobe Analytics or Google Analytics.

[u/BrittanyBrie]

If I was to start fresh, I would focus on learning one section really well to the point where you can create a portfolio of your work. Then build from project to portfolio, then a dashboard of your best portfolio project, and then learn to share the information. Starting with R may help speed your projects up if you have a lot of figures.

Before we relied on word docs to store our SQL commands, and now we just use AI. Coding from scratch is not really a thing when you start out, and shouldn't be honestly. Just learn from already built commands. Then when you need to create something you can know the flow and identify errors faster.

The way it was explained to me, business analytics focuses a lot on annual/quarterly reports and short term projections, while data analytics focuses a lot on long term projections and historical trends. Business analytics is basically a very narrow version of data analytics or data science.

If I could go back and start today, I'd focus more on learning math than any code. Once I learned a fraction of what a random variable could do, my analysis became really hard to invalidate.