Copilot & AI Agents for Data Science Bootcamp [2026]

Name: Copilot & AI Agents for Data Science Bootcamp [2026]
Rating: 4.6 (268 reviews)

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

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Created byProf. Ryan Ahmed, PhD, MBA | 1M+ Students | #1 Best Selling AI Courses

Last updated 1/2026

English

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)

Course content

9 sections • 89 lectures • 9h 46m total length

Instructor Introduction and CoPilot for Data Science Practical Demo!14:06
Explore Microsoft Copilot and AI agents for data science through hands-on demos that clean messy data, analyze sentiment, generate visualizations, and build forecasting models.
Bootcamp Outline & Key Success Tips7:03
CoPilot & AI Agents 1015:40
Discover Microsoft Copilot and AI agents for data science, including integration with PowerPoint, Teams, Word, Outlook, and Excel, and how agents plan, use tools, with grounding and guardrails.
Download the Bootcamp Materials0:01

Module Agenda - Data Wrangling and Analysis2:57
Data Wrangling, Analysis, & Feature Engineering 1014:59
Explore data wrangling and feature engineering with real-world datasets, using Copilot to clean, format, and organize data, handle missing values, and create powerful features for machine learning models.
Prompt Engineering & Top 5 Prompt Engineering Tips7:34
Prompt Engineering Techniques: Zero, Few, and Chain-of-thought Prompting6:22
Explore zero-shot prompting, few-shot prompting, and chain-of-thought prompting to guide Copilot and AI models, using examples, step-by-step reasoning, and sentiment and math tasks.
Pandas Library and CoPilot Integration6:38
Project 1 – Task 1: Importing Excel Files into Pandas DataFrames with CoPilot11:34
Project 1 – Task 2: Locating and Handling Missing Datasets10:36
Locate and handle missing data in Pandas dataframes by using isnull, count with sum, and fill with mean or mode, demonstrated in Jupyter and Copilot prompts.
Project 1 – Task 3: Data Merging and Concatenation with CoPilot4:10
Merge two pandas data frames on client ID to create a unified clients combined data set, handling missing values and using Copilot for seamless analysis.
Project 1 – Task 4: Data Analysis, Filtering and Sorting6:43
Project 1 – Task 5: Data Visualization8:45
Feature Engineering Techniques13:23
Practical Project 2 – Task 1: Data Loading, Imputation, & Exploration7:14
Practical Project 2 – Task 2: One Hot Encoding & Features Scaling4:30
Practical Project 2 – Task 3: Pandas DataFrame Filtering & Data Visualization4:21
Practical Project 3 – Task 1: Project Overview & GPT-5 Powerful Features4:19
Practical Project 3 – Task 2: Build a Data Wrangling AI Agent in CoPilot11:40
Build a data wrangling ai agent in copilot that loads csvs, performs eda, cleans duplicates and missing values, and saves a verified clean dataset after user confirmation.
Practice Opportunity Question: Data Wrangling & Feature Engineering1:43
Practice Opportunity Solution Part 1: Data Wrangling & Feature Engineering5:54
Practice Opportunity Solution Part 2: Data Wrangling & Feature Engineering4:09
Concluding Remarks and Thank You!1:41
Build a data wrangling AI agent in Microsoft Copilot to automate data cleaning and preparation with pandas, applying one-hot encoding, normalization, and standardization along with zero-shot, chain-of-thought, and few-shot prompting.

Module Agenda & Data Visualization Libraries in Python6:43
Data Visualization Types6:23
Project 1 Overview - World Happiness Report Visualization & Storytelling3:52
Project 1 (Part A) - Scatterplot, Best-Fit Regression Line, & Bar Chart8:24
Practice Opportunity Question: Scatter, Bar, & Regression Line Plots1:01
Practice Opportunity Solution: Scatter, Bar, & Regression Line Plots5:54
Project 1 (Part B) - Correlation Heatmaps, Pairplots, & 10 GPT-5 Visualizations10:19
Create correlation heatmaps and Seaborn pairplots from the happiness report data, using pandas and seaborn, and generate ten GPT-5 visualizations with matplotlib and Plotly.
Project 1 (Part C) - Analyst AI Agent for Data Visualization3:07
Project 2 Overview - Walmart Sales Data Visualization & Storytelling3:03
Project 2 (Part A) - Walmart Sales Data Visualization & Storytelling7:39
Project 2 (Part B) - Walmart Sales Data Visualization & Storytelling7:44
Practice Opportunity Question: AI Analyst Agent0:36
Practice Opportunity Solution: AI Analyst Agent6:04
Final Project Overview - Cancer Data Visualization & Storytelling3:15
analyze and visualize cancer.csv using seaborn, matplotlib, and Plotly to compare benign and malignant samples, generating multiple visuals and exploring correlations to tell a story from the data.
Final Project Solution (Part A) - Cancer Data Visualization & Storytelling5:54
Unlock cancer data storytelling through Copilot guided analysis, building pandas data frames and creating seaborn visuals—histograms, KDEs, box plots, and pair plots by target class.
Final Project Solution (Part B) - Cancer Data Visualization & Storytelling5:51
Plot cancer data including bar charts of feature correlations to the target, a benign-vs-malignant pie chart, count plots, scatter and heat map plots using pandas, matplotlib, seaborn, and plotly express.
Final Project Solution (Part C) - Cancer Data Visualization & Storytelling2:49
Concluding Remarks & Thank You!1:13

Model Development and Validation Module Overview3:35
Learn to develop and validate models using Microsoft Copilot, compare classical classifiers and neural networks, evaluate with confusion matrix and metrics, and visualize performance with roc curve and auc.
Practical Project Overview - Build a Marketing Predictor AI Agent in CoPilot4:40
ML Classifier Models Comparison - Logistic Regression, Random Forest, SVM,..etc6:45
Classification Models KPIs & Confusion Matrix11:05
Explore how a confusion matrix visualizes classifier performance, showing true positives, true negatives, false positives, and false negatives, and learn how accuracy, precision, and recall are calculated.
Classification Models Practice Opportunity1:24
Classification Models Practice Opportunity Solution3:05
Practical Project: Build AI Agents in CoPilot - Part 18:32
Build an AI agent in Copilot to manage the full data science lifecycle, from data ingestion and EDA with visuals to one-hot encoding and predicting campaign responses.
Practical Project: Build AI Agents in CoPilot - Part 24:43
Practical Project: Build AI Agents in CoPilot - Part 36:01
This practical project guides you through building a marketing data scientist agent in CoPilot, running prompts to summarize data, generate visuals, and explore features for predicting campaign conversion.
Practical Project: Build AI Agents in CoPilot - Part 47:22
Practice Opportunity Question: Train ML Classifier Models in CoPilot2:08
Practice Opportunity Solution Part A: Train ML Classifier Models in CoPilot9:48
Practice Opportunity Solution Part B: Using CoPilot Analyst AI Agent4:32
Explore how a pre-built analyst ai agent trains ten classifier models, preprocesses data, and uses roc curves and auc to compare performance, revealing a leaderboard and tuning options.
Conclusion, Summary, & Thank You Message!1:46

Anomaly Detection Module Agenda1:31
Introduction to Anomaly Detection and Techniques Overview5:57
Z-Score Anomaly Detection Method8:15
Practical Project Part A - Build Anomaly Detector AI Agent in CoPilot7:18
Practical Project Part B - Build Anomaly Detector AI Agent in CoPilot7:54
Isolation Forest Algorithm14:19
Practice Opportunity Question: AI Agent for Isolation Forest Anomaly Detection1:10
Practice Opportunity Solution: AI Agent for Isolation Forest Anomaly Detection7:03
Build an ai agent in Microsoft Copilot to detect anomalies with the isolation forest algorithm, visualize results with plots, and compare against the z-score method.
Concluding Remarks & Thank You!1:46
Explain anomaly detection concepts and real-world applications, including z score outlier detection in sales data, and compare Copilot AI agents with isolation forest for performance and business impact.

Appendix A.1 - Simple Linear Regression Math 1016:59
learn simple linear regression to predict y from x, using the straight-line model y = m x + b, with slope m and intercept b, to forecast revenue from temperature.
Appendix A.2 - Least Sum of Squares11:14
Appendix A.3 - Scikit Learn7:35
Appendix A.4 - XGBoost overview20:57
Appendix A.5 - Intro to XG-Boost7:18
Appendix A.6 - What is Boosting9:06
Appendix A.7 - Ensemble Decision Trees9:13
Explore ensemble learning, including bagging and boosting, and see how multiple decision trees combine to improve accuracy and generalization in XGBoost, with leaves, decision nodes, and CART concepts.
Appendix A.8 - Bias Variance Tradeoff13:36
Appendix A.9 - L2 regularization Ridge8:02
Explore L2 regularization, ridge regression, to balance bias and variance, reduce overfitting, and improve generalization across training and testing data, with alpha as the tuning parameter in models like XGBoost.
Appendix A.10 - L1 regularization Lasso5:39

Appendix B.1 - Data Strategy and Key Components9:31
Appendix B.2 - Data Strategy Components - Practical Example5:16
Appendix B.3 - Defining Data Requirements Part 17:31
Define data requirements to train, validate, and deploy AI models, covering data type, structure, sources, volume, velocity, and labeling, with structured vs unstructured and labeled vs unlabeled data.
Appendix B.4 - Defining Data Requirements Part 24:45
Appendix B.5 - Defining Data Requirements Part 311:20
Appendix B.6 - Data Quality Assessment7:03
Appendix B.7 - Data Labeling7:21
Discover how data labeling tags images, text, audio, and video for supervised learning using guidelines and a mix of automated and human labeling workflows on Amazon SageMaker Ground Truth.
Appendix B.8 - Data Lake Vs. Data Warehouse Vs. Database3:27
Appendix B.9 - Data Governance and Security10:02

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.

Who this course is for:

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.

Copilot & AI Agents for Data Science Bootcamp [2026]

What you'll learn

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Course content

Introduction4 lectures • 27min

Data Wrangling and Analysis with CoPilot & GPT-520 lectures • 2hr 9min

Data Visualization & Storytelling Using Microsoft CoPilot & Analyst AI Agents18 lectures • 1hr 30min

Model Development and Validation Using CoPilot & AI Agents14 lectures • 1hr 15min

Anomaly Detection Using CoPilot & GPT-59 lectures • 55min

Appendix A: Machine Learning & Data Science Fundamentals10 lectures • 1hr 40min

Appendix B: Data Quality and Requirements in Data Science9 lectures • 1hr 6min

Appendix C: Microsoft CoPilot (Additional Optional Materials)4 lectures • 43min

Congratulations & Thank You Message!1 lecture • 1min

Requirements

Description

Who this course is for: