Data Analysis Methods

Master the essential principles of statistical inference, bioinformatics pipelines, and reproducible research. Converting raw data into biological insights.

Statistical Inference

Fundamentals of hypothesis testing including p-values, confidence intervals, and power analysis for experimental design.

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Bioinformatics Pipelines

Advanced workflows for genomic sequencing analysis, variant calling, and transcriptomics quantification using command-line tools.

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Analysis Workflow Checklist

Data Cleaned Raw data imported, null values handled, and outliers identified. Tidy data format established.

Assumptions Checked Normality tests and homogeneity of variance verified before selecting parametric vs non-parametric tests.

Code Documented Scripts commented thoroughly. Version control initialized (Git) and dependencies listed for reproducibility.

Certification Modules

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Statistical Basics

ID: STAT-101

Hypothesis testing, regression, and ANOVA.

Python for Biology

ID: CODE-200

Data manipulation with Pandas and NumPy.

Data Visualization

ID: VIZ-305

Creating publication-quality figures.

Microscopy FAQ

What is a p-value?

The probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is correct.

R or Python for analysis?

R is often preferred for specialized statistical packages, while Python excels in general scripting, machine learning, and integration.

What is statistical power?

The probability that a study will detect an effect when there is an effect to be detected. High power reduces Type II errors.