🔍 What is this about?

In today’s biotechnology and life sciences world, experiments generate massive amounts of data — from DNA sequences to enzyme activity curves to entire transcriptomes.

Data analysis & computational biology teach students how to handle, analyze, visualize, and make sense of this biological data using tools like R and Python.
It’s where biology meets statistics & coding.

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🐍 Why learn R or Python?

• R is widely used in bioinformatics for statistics & beautiful plots (via ggplot2), plus specialized packages like Bioconductor.
• Python is flexible, has powerful libraries (Pandas, NumPy, Seaborn, Scikit-learn) for data cleaning, visualization, and even machine learning.

Knowing these makes students capable of analyzing experiments themselves — rather than waiting for a “data person.”

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📊 What types of analyses are common?

Type of analysis	Why do it?
Statistical tests	Check if differences (e.g. between treated vs. control) are significant. E.g. t-tests, ANOVA.
Plotting growth curves	Visualize how bacteria or enzyme reactions change over time.
-Omics data interpretation	From RNA-seq counts, identify up/down-regulated genes, or from proteomics, spot key proteins.
Heatmaps & clustering	Group similar genes/samples based on expression patterns.
Machine learning basics	Predict outcomes, classify samples (e.g. cancer vs. normal).

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🔄 Mini workflow diagram: typical biological data analysis

Raw data (lab or sequencing)
    ↓
Data cleaning (remove errors, format tables)
    ↓
Statistical analysis (find significant differences)
    ↓
Visualization (plots, heatmaps, networks)
    ↓
Biological interpretation (pathways, phenotypes)

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📝 Example case study: analyzing enzyme kinetics

🧪 Scenario

A lab runs an experiment testing how an enzyme reacts to different substrate concentrations.

🖥️ Using R or Python, they:

• Enter data in a CSV: substrate concentration vs. reaction rate.
• Plot the Michaelis-Menten curve to estimate Vmax and Km.
• Use scipy.optimize.curve_fit in Python or nls() in R to fit the curve.

🎯 What does it tell them?

• The enzyme’s efficiency under lab conditions.
• Helps compare wild-type vs. mutant enzyme performance.

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✅ Short summary table

Tool / Concept	Used for	Example
R / ggplot2	Beautiful statistical plots, e.g. boxplots	Visualize gene expression across conditions
Python / Pandas	Clean & manipulate large datasets	Filter out low-quality reads from RNA-seq data
Statistical tests	Check significance (p-value)	See if treated group differs from control
Heatmaps / clustering	Visualize large -omics datasets	Group genes by similar expression patterns
Pathway enrichment tools	Link gene lists to biology	Find affected pathways in cancer samples