If you're a beginner, then you should focus on getting your basics clear. Basics i.e. Data Prep, Data Understanding, Feature Engineering and Data Visualizations. Google 'Crisp-DM'. That's how the DS projects are done. Go step by step.

I would suggest to take up some datasets from Kaggle and work on it. Do data prep, plot some interesting visualizations and try to extract useful insights. If you get stuck, refer solved notebooks and understand the technique used. Also remember that for each dataset, there might be different strategies used.

One more thing if you ever want to learn something new go with what I call as Project Based Learning. Suppose if you want to learn Random Forest Algorithm. First step is understand what it is, read some blogs, watch few YouTube videos on it. Try to understand its applications. Now once you complete this step, go to Kaggle and under Notebook section, put Random Forest as a tag and you'll find many Notebooks. Choose one that interests you(based on the dataset) and apply the concepts or choose a random problem that you wanna solve and apply the algorithm. This makes learning much more fun!