## this creates the DESeq object from martix
dds <- DESeqDataSetFromMatrix (countData = countdata,
                               colData = metadata,
                               design = ~ Condition + Genotype)

## this runs the DESeq2 analysis as per your specified condition
dds <- DESeq(dds)
## you don't need to estimateSizeFactors manually because DESeq() does this as part of the pipeline

## returns the result from the DESeq analysis, you can specify the contrast specifically here - see the DESeq2 manual
dds.res <- results(dds)

## transforms the dds object using a variance stabilising transformation which converts the counts to a ~log2 type format
ddt <- vst(dds)

## returns the matrix of transformed ~log2 counts for downstream analysis & visualisation like PCA/TSNE/heatmaps etc
vst.mat <- assay(ddt)

As others have said, you should familiarise yourself with the DESeq2 manual, but this is a basic set of commands to doing a DESeq2 analysis.

Provided you've used the right contrast, dds.res contains information like LFC, standard error, p-value and adjuvsted p-value for the differentially expressed genes. You can convert it to a data-frame and immediately use it in visualisations like volcano-plots etc.

Just remember that DESeq2 will always require raw, untransformed counts for running differential analysis, and the rlog2/vst transformed data for downstream applications.

If you see:

dds <- estimateSizeFactors(dds)
vst_data <- vst(dds, blind = TRUE)

This is skipping the differential expression part and just generates ~log2 transformed data for downstream without doing any statistical testing.