It could be resources for both qualitative and quantitative data analysis. I feel there are so many resources on Google and course platforms. I wonder what would be the most useful resources to get better at data-driven decision-making.
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Honestly, this sub is too focused on skills and training. I’m not saying training is bad but hear me out. Everyone focuses on trainings and classes. Largely I suspect because they want a framework. Moreover, trainings are nice objective tasks which a nicely organized to complete.
Data analysis is an input to make decisions on your product. So learn as you’re developing the product. Make a dashboard with data from a csv file. If you can’t query then ask you’re engineer to give you a data export. Focus on the hard questions like what to build and learn the how parts as you go. There’s no shortage of drones to pull data and build a dashboard from specs. The hard part is knowing how to use the data and making intelligent decisions.
Just so I’m not a pure waste of time. Here are a few things…
- write queries with SQL for large data projects and learn how about RDBMS to crate tables and store data. I’m the event you might not wanna dump the somewhere.
- use sheets slides to work on smaller data set and build nice charts and slides.
- You can look at software like tableau for dashboards
- Talk to your customers and find key metrics to monitor to start. Simple metrics. Don’t get too fancy. Revenue, usage, etc.
Hope I helped.
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What are you trying to accomplish? There are many layers to data analysis that go from simple data visualization (e.g. in Tableau or Excel), basic data interpretation like pivot tables, descriptive stats gets you a step deeper into interpretation of your data and inferential stats allows you to model and test hypothesis.
Then there’s also data management stuff that deals with engineering data pipelines and consolidating data to make it actionable.
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I suggest starting with a more fundamental question - does your product have any user analytics? Many, in fact, do not. If your does, then does it have the analytics needed to understand user behavior? If it has useful user analytics, then put together a request for specific metrics, visualizations and options / filters that you believe you need in order to make product decisions - ask your BI team to build that for you. Keep in mind that your team should be observing changes in behavior / revenue / other metric, so the data should be displayed in a way where that is easy to understand the goal. If you do not have an AB testing system (where test groups are labeled), then you need to make sure the data is displayed in a way that matches easily with an ‘external’ feature tracking system (as in a manual match between feature deployment date and the dates in the dashboard).
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I use Excel to handle simple data analysis and cleaning. Python’s pandas library is my go-to tool for in depth analysis in the event that the dataset gets too large.
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Be data-informed, not data-driven. I’d also recommend the book Product Research Rules.