I'm learning data analysis and so far I've mostly worked with clean datasets. I'm curious about what real-world data is actually like.
What do you find most difficult when working with real data missing values, messy data, choosing the right metrics, understanding business requirements, or something else?
I'd be interested to hear what experienced analysts struggle with the most.
I'm learning more about data analysis and I'm curious about what experienced analysts think.
What do you think beginners spend too much time on or focus on the wrong way?
For example, is it trying to learn too many tools, focusing too much on theory, not practicing with real data, or something else?
I'd like to hear what mistakes you made when you were starting out and what you would do differently now.
I'm learning Data Science and trying to understand what beginners should focus on first.
For people who have experience in the field, what is one skill, tool, or concept you wish you had learned earlier?
I'm learning Data Science and I'm curious about other people's experiences.
For those who work in Data Science, what skill took you the longest to become comfortable with?
Was it statistics, Python, SQL, machine learning, data cleaning, or something else?
When I started learning data analysis, I thought I needed to learn every tool before I could actually work on projects.
Over time, I realized that focusing on the basics was much more useful:
I'm curious to hear from people who have been doing data analysis for a while.
What is one thing you wish you knew when you first started learning data analysis?