Copilot & Ai Agents For Data Science Bootcamp [2025]

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

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)

Requirements

No Programming Skills is required.

Description

In this hands-on bootcamp, you will master Microsoft CoPilot, GPT-5, and intelligent AI agents for data science. You’ll master the full data science workflow, including data wrangling and feature engineering, data cleaning and merging with CoPilot. We will then cover data visualization and storytelling, turning raw data into dashboards and narratives that drive business decisions. You’ll also cover model development and validation, building and evaluating classifiers while tracking performance using metrics such as accuracy, precision, recall and ROC curves. Finally, you’ll cover anomaly detection, applying methods such as Z-Score and Isolation Forest to spot unusual patterns before they cost money.. What You’ll Learn:Clean and prepare real-world datasets using CoPilot’s advanced prompt engineering.Build predictive models for forecasting, classification, and anomaly detection.Automate feature engineering and data wrangling tasks with custom AI agents.Visualize trends and correlations using Matplotlib, Seaborn, and Plotly inside CoPilot.Detect anomalies using Z-Score and Isolation Forest techniques.Create executive-level insights and recommendations from raw data.Compare and evaluate multiple machine learning models with proper validation.Design custom GPTs for advanced analysis, reporting, and business strategy.Bootcamp Modules:CoPilot Overview & AI Agents Demo – From messy data cleanup to CEO-level storytelling.Data Wrangling & Feature Engineering in CoPilot – Practical workflows for handling missing values, merging datasets, and creating features.Data Visualization in CoPilot – Scatter plots, heatmaps, pairplots, and executive-ready dashboards.Model Development & Validation – Build, evaluate, and deploy machine learning pipelines.Anomaly Detection – Spot unusual trends with Z-Scores and Isolation Forest agents.By the end of this bootcamp, you’ll know how to analyze data and have the skills to build AI-augmented workflows that drive faster, smarter, and more impactful decisions.

Overview

Section 1: Introduction

Lecture 1 Instructor Introduction and CoPilot for Data Science Practical Demo!

Lecture 2 Bootcamp Outline & Key Success Tips

Lecture 3 CoPilot & AI Agents 101

Lecture 4 Download the Bootcamp Materials

Section 2: Data Wrangling and Analysis with CoPilot & GPT-5

Lecture 5 Module Agenda - Data Wrangling and Analysis

Lecture 6 Data Wrangling, Analysis, & Feature Engineering 101

Lecture 7 Prompt Engineering & Top 5 Prompt Engineering Tips

Lecture 8 Prompt Engineering Techniques: Zero, Few, and Chain-of-thought Prompting

Lecture 9 Pandas Library and CoPilot Integration

Lecture 10 Project 1 – Task 1: Importing Excel Files into Pandas DataFrames with CoPilot

Lecture 11 Project 1 – Task 2: Locating and Handling Missing Datasets

Lecture 12 Project 1 – Task 3: Data Merging and Concatenation with CoPilot

Lecture 13 Project 1 – Task 4: Data Analysis, Filtering and Sorting

Lecture 14 Project 1 – Task 5: Data Visualization

Lecture 15 Feature Engineering Techniques

Lecture 16 Practical Project 2 – Task 1: Data Loading, Imputation, & Exploration

Lecture 17 Practical Project 2 – Task 2: One Hot Encoding & Features Scaling

Lecture 18 Practical Project 2 – Task 3: Pandas DataFrame Filtering & Data Visualization

Lecture 19 Practical Project 3 – Task 1: Project Overview & GPT-5 Powerful Features

Lecture 20 Practical Project 3 – Task 2: Build a Data Wrangling AI Agent in CoPilot

Lecture 21 Practice Opportunity Question: Data Wrangling & Feature Engineering

Lecture 22 Practice Opportunity Solution Part 1: Data Wrangling & Feature Engineering

Lecture 23 Practice Opportunity Solution Part 2: Data Wrangling & Feature Engineering

Lecture 24 Concluding Remarks and Thank You!

Section 3: Data Visualization & Storytelling Using Microsoft CoPilot & Analyst AI Agents

Lecture 25 Module Agenda & Data Visualization Libraries in Python

Lecture 26 Data Visualization Types

Lecture 27 Project 1 Overview - World Happiness Report Visualization & Storytelling

Lecture 28 Project 1 (Part A) - Scatterplot, Best-Fit Regression Line, & Bar Chart

Lecture 29 Practice Opportunity Question: Scatter, Bar, & Regression Line Plots

Lecture 30 Practice Opportunity Solution: Scatter, Bar, & Regression Line Plots

Lecture 31 Project 1 (Part B) - Correlation Heatmaps, Pairplots, & 10 GPT-5 Visualizations

Lecture 32 Project 1 (Part C) - Analyst AI Agent for Data Visualization

Lecture 33 Project 2 Overview - Walmart Sales Data Visualization & Storytelling

Lecture 34 Project 2 (Part A) - Walmart Sales Data Visualization & Storytelling

Lecture 35 Project 2 (Part B) - Walmart Sales Data Visualization & Storytelling

Lecture 36 Practice Opportunity Question: AI Analyst Agent

Lecture 37 Practice Opportunity Solution: AI Analyst Agent

Lecture 38 Final Project Overview - Cancer Data Visualization & Storytelling

Lecture 39 Final Project Solution (Part A) - Cancer Data Visualization & Storytelling

Lecture 40 Final Project Solution (Part B) - Cancer Data Visualization & Storytelling

Lecture 41 Final Project Solution (Part C) - Cancer Data Visualization & Storytelling

Lecture 42 Concluding Remarks & Thank You!

Section 4: Model Development and Validation Using CoPilot & AI Agents

Lecture 43 Model Development and Validation Module Overview

Lecture 44 Practical Project Overview - Build a Marketing Predictor AI Agent in CoPilot

Lecture 45 ML Classifier Models Comparison - Logistic Regression, Random Forest, SVM,..etc

Lecture 46 Classification Models KPIs & Confusion Matrix

Lecture 47 Classification Models Practice Opportunity

Lecture 48 Classification Models Practice Opportunity Solution

Lecture 49 Practical Project: Build AI Agents in CoPilot - Part 1

Lecture 50 Practical Project: Build AI Agents in CoPilot - Part 2

Lecture 51 Practical Project: Build AI Agents in CoPilot - Part 3

Lecture 52 Practical Project: Build AI Agents in CoPilot - Part 4

Lecture 53 Practice Opportunity Question: Train ML Classifier Models in CoPilot

Lecture 54 Practice Opportunity Solution Part A: Train ML Classifier Models in CoPilot

Lecture 55 Practice Opportunity Solution Part B: Using CoPilot Analyst AI Agent

Lecture 56 Conclusion, Summary, & Thank You Message!

Section 5: Anomaly Detection Using CoPilot & GPT-5

Lecture 57 Anomaly Detection Module Agenda

Lecture 58 Introduction to Anomaly Detection and Techniques Overview

Lecture 59 Z-Score Anomaly Detection Method

Lecture 60 Practical Project Part A - Build Anomaly Detector AI Agent in CoPilot

Lecture 61 Practical Project Part B - Build Anomaly Detector AI Agent in CoPilot

Lecture 62 Isolation Forest Algorithm

Lecture 63 Practice Opportunity Question: AI Agent for Isolation Forest Anomaly Detection

Lecture 64 Practice Opportunity Solution: AI Agent for Isolation Forest Anomaly Detection

Lecture 65 Concluding Remarks & Thank You!

Section 6: Appendix A: Machine Learning & Data Science Fundamentals

Lecture 66 Appendix A.1 - Simple Linear Regression Math 101

Lecture 67 Appendix A.2 - Least Sum of Squares

Lecture 68 Appendix A.3 - Scikit Learn

Lecture 69 Appendix A.4 - XGBoost overview

Lecture 70 Appendix A.5 - Intro to XG-Boost

Lecture 71 Appendix A.6 - What is Boosting

Lecture 72 Appendix A.7 - Ensemble Decision Trees

Lecture 73 Appendix A.8 - Bias Variance Tradeoff

Lecture 74 Appendix A.9 - L2 regularization Ridge

Lecture 75 Appendix A.10 - L1 regularization Lasso

Section 7: Appendix B: Data Quality and Requirements in Data Science

Lecture 76 Appendix B.1 - Data Strategy and Key Components

Lecture 77 Appendix B.2 - Data Strategy Components - Practical Example

Lecture 78 Appendix B.3 - Defining Data Requirements Part 1

Lecture 79 Appendix B.4 - Defining Data Requirements Part 2

Lecture 80 Appendix B.5 - Defining Data Requirements Part 3

Lecture 81 Appendix B.6 - Data Quality Assessment

Lecture 82 Appendix B.7 - Data Labeling

Lecture 83 Appendix B.8 - Data Lake Vs. Data Warehouse Vs. Database

Lecture 84 Appendix B.9 - Data Governance and Security

Section 8: Appendix C: Microsoft CoPilot (Additional Optional Materials)

Lecture 85 Appendix C.1 - Microsoft CoPilot Vs. CoPilot Pro Vs. Microsoft 365 CoPilot

Lecture 86 Appendix C.2 - CoPilot General Use Cases - Part 1

Lecture 87 Appendix C.3 - CoPilot General Use Cases - Part 2

Lecture 88 Appendix C.4 - Performing Data Wrangling Using Python in Excel

Section 9: Congratulations & Thank You Message!

Lecture 89 Congratulations on Completing the bootcamp!

Data scientists and analysts looking to supercharge productivity with CoPilot.,Business professionals who want to turn data into strategy without heavy coding.,Students and learners eager to bridge the gap between AI automation and real-world data science workflows.