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Avis du cours Copilot & AI Agents for Data Science Bootcamp [2026] par Prof. Ryan Ahmed

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Master Data Science with CoPilot & AI Agents: Data Wrangling, Analysis, Visualization, Model Building & Validation

Format d'apprentissage Online Course

Sous-catégorie AI Agents

Prix du cours $19.99 (list $9.99)

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À propos de Prof. Ryan Ahmed

Master Data Science with CoPilot & AI Agents: Data Wrangling, Analysis, Visualization, Model Building & Validation

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12,696 students

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English

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Que apprend-on dans le cours de Prof. Ryan Ahmed ?

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)

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