Copilot & AI Agents for Data Science Bootcamp [2026]
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Unclaimed ProfileThe Copilot & AI Agents for Data Science Bootcamp [2026] is a forward-thinking curriculum designed to bridge the gap between traditional data science workflows and the rapidly evolving world of Generative AI. As the industry shifts toward agentic workflows, this course empowers learners to stop performing repetitive coding tasks manually and instead start orchestrating AI agents to handle data wrangling, feature engineering, and complex model validation. You will dive deep into the integration of Microsoft Copilot and Python, moving beyond simple prompt engineering to build specialized agents that perform tasks like automated Z-score anomaly detection, data cleaning, and systematic visualization. The bootcamp offers a unique advantage by focusing on both the technical implementation of machine learning algorithms—such as Random Forests and Support Vector Machines—and the strategic deployment of AI assistants that interpret metrics like F1 scores and ROC-AUC. Whether you are aiming to streamline your EDA (Exploratory Data Analysis) process or looking to automate large-scale classification pipelines, this course provides a pragmatic, hands-on framework. It helps data professionals transition from 'code-first' to 'agent-first' development, ensuring you stay competitive in an era where AI-augmented analysis is becoming the standard for business intelligence and predictive modeling.
About the creator
Prof. Ryan Ahmed
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Prof. Ryan Ahmed is a distinguished educator and technology leader dedicated to making artificial intelligence and machine learning accessible to a global audience. With a profound understanding of cutting-edge tech, Ryan specializes in demystifying complex concepts such as Agentic AI engineering, LLM system architecture, and advanced retrieval-augmented generation (RAG). His teaching methodology centers on a practical, hands-on approach that bridges the gap between theoretical data science and enterprise-ready application development. Whether guiding his students through building custom Python-based AI agents from scratch or configuring enterprise workflows using Microsoft Copilot Studio, Ryan ensures every lesson is grounded in real-world utility. He…Show more
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Program Overview
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What You'll Learn
- 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)
Best For
- Data analysts looking to automate repetitive data cleaning and visualization tasks.
- Machine learning practitioners who want to integrate AI agents into their existing pipelines.
- Professionals familiar with Python who want to master Microsoft Copilot for enterprise workflows.
- Aspiring data scientists eager to combine traditional algorithm training with generative AI automation.
Not For
- Complete beginners who have never written a single line of Python code.
- Learners seeking a deep-dive theoretical course on advanced neural network architecture.
- Individuals who prefer manual coding workflows and are skeptical of generative AI assistance.
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