What have you tried? There is documentation on complete pipelines available online. Did you search show you any useful link?

Since you tagged this scRNAseq, FastQC is not going to be useful for much here.

You will need to follow a proper analysis workflow like one described in https://bioconductor.org/books/release/OSCA/

Here's a list of steps you might want to try assuming library prep was done with 10x Genomics:

1. Run cellranger to get to counts files (matrix, barcodes, genes).
2. Load the data using scanpy.
3. Compute QC metrics and filter the cells and genes.
4. Run normalization, log transformation (log1p), scaling, PCA, neighborhood graph, and UMAP (in this order). I assumed no batch effect correction is needed.
5. Run a clustering algorithm such as leiden.
6. Based on your expected cell composition, you can plot marker genes to identify cell types. You can also color your UMAP by gene list scores to identify cell types.
7. Perform additional analyses such as cell composition with scCODA, pseudotime ordering, cell-cell communication, perturbation modelling, etc.

I hope this gives you an idea of where you need to look.