IMO the edgeR package is amazing, has great documentation and is highly validated. A really good tutorial for differential expression is this one:
For complete tutorial theres: https://github.com/griffithlab/rnaseq_tutorial
There's a massive amount of info online, google is your friend
Simply, (1) Check the quality of raw sequences (FastQC), (2) Remove adapters and low quality reads (this is OPTIONAL and can use a tool like Cutadapt), (3) map to a reference genome of interest (STAR), (4) prepare count matrices (Subread featureCounts), (5) differential gene expression analysis using R package DESeq2 or edgeR and (6) pathway analysis using a program like clusterProfiler.
I have written a complete pipeline that I use for my RNA-seq data analyses: https://github.com/jkkbuddika/RNA-Seq-Data-Analyzer (This pipeline takes care of steps 1-4)
The repository also contain a detailed step-by-step user guide that would walk you through getting things started. The Python pipeline eventually generates count tables using featureCounts and additionally bedgraphs for IGV visualization using DeepTools. These final count tables can be used as an input for DESeq2 or edgeR to do the differential gene expression analysis. Let me know if you need any help.