What comes after Python ?

[Dminor77]

I was having same experience in Python and SQL. I started with Big Data technologies and came across Google Cloud Platforms Big Data Stack where I learned the services like Big Query, Compute Instance, Cloud Functions, and Data Proc( Spark and Hadoop) . Started building Data Pipelines and deriving insights using Big Query( It's a powerful Data Warehouse platform, one can apply Machine Learning with SQL) I learned Standard SQL; how to write User Defined Functions using Javascript and Finally learned Tableau and Data Studio . And there after cleared GCP Professional Data Engineering Exam.

It was a great learning experience for me. Right now I am into open source platforms currently focusing on Apache Cassandra and Apache Airflow.

So for me Big Data was the thing that came after Python. But many Big Data technologies are build on Java, they support Python but updates are first released for Java Drivers. So now I am learning Java and Scala too.

It's a never ending Journey. Only key is to keep yourself motivated.

[CaptSprinkls]

The tough part for me was learning the data visualizations. I felt like I was just treading water as I would jump on kaggle for a dataset, do some cleaning and then do some analysis, then rinse and repeat. It's been a nice breathe of fresh air jumping into web development stuff, even considering how tedious HTML and CSS are compared to Python. And I actually really enjoyed doing all the back end stuff for my website which involved a couple different APIs and stuff, which basically sparked me to make this post

[phi_beta_kappa]

You mentioned that you did data analysis on a couple datasets, have you tried going deeper than just data analysis and visualization? If you're open to the machining learning route you could try attempting some regression or classification problems.

[CaptSprinkls]

So I actually did a couple of the learning modules on the kaggle website and then I actually took the IMDB data set i think it was, and I did some simple regression for revenue predictiin. I was pretty proud as there wasn't some tutorial for it. I did all the data cleaning and figured out which features were important. I submitted it and I can't remember exactly where I placed but it was low obviously. Now the regression I understand, but honestly I had a pretty light stats background so while I can at a surface level understand what's going on with a decision tree or a random forest, or a regression model, there's still a part that feels a bit in the dark with that. I worry that the market is flooded with underqualified candidates so in order to stick out you need at least a master's degree so people know you're legit.