Copilot & AI Agents for Data Science Bootcamp [2026] By Prof. Ryan Ahmed
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Copilot & AI Agents for Data Science Bootcamp [2026] · Program
About this program
Copilot & AI Agents for Data Science Bootcamp [2026]
Online Course
Learning format
AI Agents
Subcategory
English
Course language
$19.99 (list $9.99)
Price
Price may change · updated within 1–2 weeks
Official program page
What you'll learn in Copilot & AI Agents for Data Science Bootcamp [2026]
- Build Data Wrangling AI agents in CoPilot to automate cleaning and preparation tasks on complex datasets.
- Design effective prompts and apply prompting strategies (zero-shot, few-shot, chain-of-thought) to optimize outputs from generative AI systems.
- Use the Pandas library and Microsoft CoPilot to load, manipulate, and analyze real-world datasets programmatically.
- Perform feature engineering tasks such as one-hot encoding, normalization, and standardization to prepare data for machine learning models.
- Apply practical techniques for cleaning messy datasets: handling missing values, removing duplicates, merging data sources, and ensuring consistent formatting.
- Master Data visualization Libraries such as Matplotlib, Seaborn, and Plotly Express to plot static and interactive insight-rich visuals.
- Gain hands-on experience with Microsoft Copilot’s Analyst Agent to automate visualization workflows, generate perspectives quickly, and interpret outputs
- Understand common data visualization types including scatterplots, bubble charts, bar charts, line charts, histograms, box plots, pie charts, and area charts
- Build and interpret regression line plots to study correlations between features and quantify the strength of relationships in data.
- Develop and evaluate classification models (e.g., Logistic Regression, Decision Trees, SVMs, Random Forests, Gradient Boosting, kNN, Naive Bayes)
- Construct and analyze confusion matrices, & calculate key metrics (accuracy, precision, recall, specificity, F1 score, ROC-AUC) to assess model performance
- Identify which performance metrics matter most in specific contexts (e.g., fraud detection vs. marketing campaigns) and justify model selection
- Use CoPilot to build, evaluate, & interpret machine learning pipelines; from exploratory data analysis to model training & evaluation
- Explain the concept of anomaly detection, describe its importance in uncovering unusual patterns, and illustrate real-world applications such as fraud detection
- Apply the Z-score method by calculating and interpreting z-scores, detecting outliers in sales datasets, and visualizing deviations from average performance
- Build an AI Agent in Microsoft Copilot that automates Z-score analysis for sales data, detects anomalies beyond set thresholds, & provides clear visualization
- Implement the Isolation Forest algorithm in Copilot to design an AI Agent (“Isolation Forest Detector”) that isolates and highlights anomalous sales behaviors
- Evaluate the business impact of anomalies uncovered through both techniques, explaining how these insights inform decisions on risks (e.g., revenue drops)
What's Included in Copilot & AI Agents for Data Science Bootcamp [2026]
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