UDEMY Copilot AI Agents for Data Science Bootcamp 2026 BOOKWARE-MiMiR
Mimir, Keeper of the Well of Wisdom
Copilot & AI Agents for Data Science Bootcamp [2026]
https://www.udemy.com/course/copilot-ai-agents-for-data-science-
bootcamp/
Year : 2026
Language : English
Level : All Levels
Category : Development
Subcategory : Data Science
Duration : 9h 46m
Lectures : 89
Rating : 4.6/5 (227 reviews)
Students : 1,474
INSTRUCTOR(S)
HEADLINE
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.
WHO IS THIS COURSE 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.
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.
COURSE CONTENT
Chapter 1: Introduction
1. Instructor Introduction and CoPilot for Data Science
Practical Demo!
2. Bootcamp Outline & Key Success Tips
3. CoPilot & AI Agents 101
4. Download the Bootcamp Materials
Chapter 2: Data Wrangling and Analysis with CoPilot & GPT-5
5. Module Agenda - Data Wrangling and Analysis
6. Data Wrangling, Analysis, & Feature Engineering 101
7. Prompt Engineering & Top 5 Prompt Engineering Tips
8. Prompt Engineering Techniques: Zero, Few, and Chain-of-
thought Prompting
9. Pandas Library and CoPilot Integration
10. Project 1 ? Task 1: Importing Excel Files into Pandas
DataFrames with CoPilot
11. Project 1 ? Task 2: Locating and Handling Missing
Datasets
12. Project 1 ? Task 3: Data Merging and Concatenation with
CoPilot
13. Project 1 ? Task 4: Data Analysis, Filtering and Sorting
14. Project 1 ? Task 5: Data Visualization
15. Feature Engineering Techniques
16. Practical Project 2 ? Task 1: Data Loading, Imputation,
& Exploration
17. Practical Project 2 ? Task 2: One Hot Encoding &
Features Scaling
18. Practical Project 2 ? Task 3: Pandas DataFrame Filtering
& Data Visualization
19. Practical Project 3 ? Task 1: Project Overview & GPT-5
Powerful Features
20. Practical Project 3 ? Task 2: Build a Data Wrangling AI
Agent in CoPilot
21. Practice Opportunity Question: Data Wrangling & Feature
Engineering
22. Practice Opportunity Solution Part 1: Data Wrangling &
Feature Engineering
23. Practice Opportunity Solution Part 2: Data Wrangling &
Feature Engineering
24. Concluding Remarks and Thank You!
Chapter 3: Data Visualization & Storytelling Using Microsoft
CoPilot & Analyst AI Agents
25. Module Agenda & Data Visualization Libraries in Python
26. Data Visualization Types
27. Project 1 Overview - World Happiness Report
Visualization & Storytelling
28. Project 1 (Part A) - Scatterplot, Best-Fit Regression
Line, & Bar Chart
29. Practice Opportunity Question: Scatter, Bar, &
Regression Line Plots
30. Practice Opportunity Solution: Scatter, Bar, &
Regression Line Plots
31. Project 1 (Part B) - Correlation Heatmaps, Pairplots, &
10 GPT-5 Visualizations
32. Project 1 (Part C) - Analyst AI Agent for Data
Visualization
33. Project 2 Overview - Walmart Sales Data Visualization &
Storytelling
34. Project 2 (Part A) - Walmart Sales Data Visualization &
Storytelling
35. Project 2 (Part B) - Walmart Sales Data Visualization &
Storytelling
36. Practice Opportunity Question: AI Analyst Agent
37. Practice Opportunity Solution: AI Analyst Agent
38. Final Project Overview - Cancer Data Visualization &
Storytelling
39. Final Project Solution (Part A) - Cancer Data
Visualization & Storytelling
40. Final Project Solution (Part B) - Cancer Data
Visualization & Storytelling
41. Final Project Solution (Part C) - Cancer Data
Visualization & Storytelling
42. Concluding Remarks & Thank You!
Chapter 4: Model Development and Validation Using CoPilot & AI
Agents
43. Model Development and Validation Module Overview
44. Practical Project Overview - Build a Marketing Predictor
AI Agent in CoPilot
45. ML Classifier Models Comparison - Logistic Regression,
Random Forest, SVM,..etc
46. Classification Models KPIs & Confusion Matrix
47. Classification Models Practice Opportunity
48. Classification Models Practice Opportunity Solution
49. Practical Project: Build AI Agents in CoPilot - Part 1
50. Practical Project: Build AI Agents in CoPilot - Part 2
51. Practical Project: Build AI Agents in CoPilot - Part 3
52. Practical Project: Build AI Agents in CoPilot - Part 4
53. Practice Opportunity Question: Train ML Classifier
Models in CoPilot
54. Practice Opportunity Solution Part A: Train ML
Classifier Models in CoPilot
55. Practice Opportunity Solution Part B: Using CoPilot
Analyst AI Agent
56. Conclusion, Summary, & Thank You Message!
Chapter 5: Anomaly Detection Using CoPilot & GPT-5
57. Anomaly Detection Module Agenda
58. Introduction to Anomaly Detection and Techniques
Overview
59. Z-Score Anomaly Detection Method
60. Practical Project Part A - Build Anomaly Detector AI
Agent in CoPilot
61. Practical Project Part B - Build Anomaly Detector AI
Agent in CoPilot
62. Isolation Forest Algorithm
63. Practice Opportunity Question: AI Agent for Isolation
Forest Anomaly Detection
64. Practice Opportunity Solution: AI Agent for Isolation
Forest Anomaly Detection
65. Concluding Remarks & Thank You!
Chapter 6: Appendix A: Machine Learning & Data Science
Fundamentals
66. Appendix A.1 - Simple Linear Regression Math 101
67. Appendix A.2 - Least Sum of Squares
68. Appendix A.3 - Scikit Learn
69. Appendix A.4 - XGBoost overview
70. Appendix A.5 - Intro to XG-Boost
71. Appendix A.6 - What is Boosting
72. Appendix A.7 - Ensemble Decision Trees
73. Appendix A.8 - Bias Variance Tradeoff
74. Appendix A.9 - L2 regularization Ridge
75. Appendix A.10 - L1 regularization Lasso
Chapter 7: Appendix B: Data Quality and Requirements in Data
Science
76. Appendix B.1 - Data Strategy and Key Components
77. Appendix B.2 - Data Strategy Components - Practical
Example
78. Appendix B.3 - Defining Data Requirements Part 1
79. Appendix B.4 - Defining Data Requirements Part 2
80. Appendix B.5 - Defining Data Requirements Part 3
81. Appendix B.6 - Data Quality Assessment
82. Appendix B.7 - Data Labeling
83. Appendix B.8 - Data Lake Vs. Data Warehouse Vs. Database
84. Appendix B.9 - Data Governance and Security
Chapter 8: Appendix C: Microsoft CoPilot (Additional Optional
Materials)
85. Appendix C.1 - Microsoft CoPilot Vs. Pro Vs. Microsoft
365 CoPilot
86. Appendix C.2 - CoPilot General Use Cases - Part 1
87. Appendix C.3 - CoPilot General Use Cases - Part 2
88. Appendix C.4 - Performing Data Wrangling Using Python in
Excel
Chapter 9: Congratulations & Thank You Message!
89. Congratulations on Completing the bootcamp!
DATES
Published : 2025-09-18
Last Updated : 2026-01-09
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