As a Data Scientist with a focus on Bioinformatics, My expertise lies in the analysis and visualization of large-scale biological datasets, employing advanced statistical techniques to transform raw data into visually compelling insights. I am adept at applying machine learning algorithms to predict biological outcomes, classify biomarkers, and optimize experimental design. This encompasses both supervised and unsupervised learning approaches. Proficient in Linux, Python, R, and associated bioinformatics tools such as Bioconductor and Biopython, I am skilled in developing custom scripts and pipelines for data analysis and automation. A key aspect of my expertise lies in Genomic Data Processing, where I demonstrate proficiency in handling and processing diverse genomic datasets, including DNA and RNA sequencing data. I utilize a range of tools such as FastQC, Trimmomatic, Bowtie, BWA, STAR, HISAT2, Kallisto, GATK, Samtools, MultiQC, and IGV to extract valuable insights from omics datasets. Additionally, my statistical modeling skills contribute to a strong foundation in hypothesis testing and experimental design, offering valuable insights to guide research setups and validate findings.
