In Retail Sales Analytics project in Power BI, I started investigating different categories, quantities, and discounts to understand why.
But I found myself wondering: Am I getting closer to answering the business question, or am I just analyzing more data?
There are always more charts to create, more numbers to compare and more questions to ask. But not every finding helps us understand the actual problem.
I am curious to know how experienced data analyst approach this.
How do you decide which direction to investigate and how do you avoid spending time on analysis that does not add much value?
I love to hear your thoughts.

Guys i am thinking of conducting an experiment. I have a masters degree in a language (sanskrit) and have no background in statistics.

My experiment will include around 100 participants, each rating 30 objects in 1-7 likert scale (6 scales). I asked claude and it suggested linear mixed model in R.

But as i said i have no experience. Can you guys recommend some courses or YouTube videos to get started. I feel so overwhelmed looking at different type of tests or models.

Any resources for getting started in forcasting?

I have a work project in mind but feel like I'm both over complicating and simplifying this process. I seem to keep landing on extremes when I try to search it up with either machine learning/ai overkill or generic overviews.

For context I'm just aiming to forcast whether locations will hit monthly budget goals based on historical data. Ive considered either:

1. pulling average daily services and dragging that out to see what the likely total is.

Or

1. Calculating cummulative % of services completed by day for the previous month and use that to assume estimate the end point for the current month.

Again I'm pretty sure Im over complication and simplifying this. So any help would be greatly appreciated.

Over the years, I've collected practical notes, examples, and references while working on real-world data analytics projects.

I recently organized them into a free learning pack covering:

Power BI & DAX – Data modeling, calculations, and reporting.

SQL – Queries, data manipulation, and analysis.

Tableau – Data visualization and dashboards.

Python – A compact guide to data analysis with practical examples.

I also included DataBridge AI, a Python-based tool I've been developing for data cleaning, validation, mapping, and analytics workflows.

These materials started as personal working references, so the focus is on practical application rather than theory alone.

I'm sharing everything for free in case it's useful to other analysts or people learning data analytics.

Free resources: https://aabdelghaffar650-cloud.github.io/portfolio/#free-pack

I'd also be interested to hear which topics or practical examples others find most useful when learning data analytics