AI for Data Analytics
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Book live on Noble DesktopSummary
Learn how to use AI-powered tools to streamline the data analysis process, from cleaning raw data to generating predictions and communicating findings. In this hands-on course, students work with real datasets, use current AI tools, and create practical deliverables they can apply to reporting, business analysis, and decision-making tasks.
Critical evaluation is built into the course from the start, not saved for the end, so students learn how to use AI responsibly as they go. By the end of the workshop, students will know how to clean, explore, analyze, visualize, and report on data with AI while using validation and traceability practices to support trustworthy results.
Who this course is for
This course is designed for analysts, business professionals, and decision-makers who work with data and want practical experience using AI without needing a programming background.
Curriculum
What you'll learn
- Use AI tools to clean, explore, analyze, visualize, and report on datasets.
- Write stronger prompts for data work using a simple analytical prompt framework.
- Validate AI-generated results with a structured 7-step checklist.
- Build and evaluate predictive models through natural language, without coding.
- Document your workflow with an AI Traceability Document for accountability and reproducibility.
- Present and defend AI-assisted findings in professional settings.
- Read a forecast for its uncertainty rather than its headline number, and know the four things that quietly break one
- Get themes and sentiment out of thousands of free-text comments, and run the check that decides whether to believe them
- Catch the harder errors that survive a first check: fabricated figures, trends that reverse when you split the data, and models that memorized instead of learned
Course syllabus
Welcome and Orientation
- Who is in the room, where everyone stands on AI, and your first question put to a real dataset inside the first hour
What You Need to Know About AI
- How AI actually works and the three consequences that make up the rest of the course
- Why having no internal test for truth means AI can fail confidently
- Why some data must never be entered into a system you do not control
- Why verification is not optional
What You Need to Know About Data
- Structured and unstructured data and where data comes from
- The four levels of analytics, from what happened to what we should do about it
- The habit of asking who is missing from a dataset before trusting what it says
Where AI Earns Its Place, and Where It Does Not
- Five kinds of work worth handing over to AI
- Three conditions under which you should not reach for AI at all
How to Talk to It
- What a strong prompt contains and which of six prompt patterns fits the moment
- How to iterate without starting over
- Why a long conversation eventually stops seeing the file you uploaded
Data You Do Not Know
- How to orient yourself before working with data you cannot check by eye
- Profile a messy, unfamiliar dataset and distinguish errors from real anomalies
- Clean data transparently, explore it, and audit what the cleaning process removed
- Identify rows or information removed without your approval
Communicating What You Found
- Choose which chart answers which question and make AI justify the chart it selected
- Apply five rules for reading a chart critically
- Use three sentences to turn a finding into a decision
- Complete a mini-analysis that runs the full workflow from beginning to end
Unlocking What Was Out of Reach
- Build a working prediction from a paragraph of plain English and interrogate the result
- Read a forecast for its interval rather than only its line
- Pull meaning from thousands of customer reviews without reading each one individually
- Apply checks that determine whether predictions, forecasts, and text analysis can be believed
The Errors That Survive a First Check
- Identify fabricated figures and other errors that initially appear reasonable
- Recognize trends that reverse when data is split and hidden third factors that affect results
- Identify models that memorized rather than learned
- Use deliberate checks to catch errors that pass a casual review
- Hunt for problems within analyses that initially appear correct
Putting It Together
- Use seven prompts in one conversation to complete the full workflow
- Examine what changes when nobody is reading the intermediate steps
- Understand agents in practical terms, including how most people will encounter them by inheriting rather than building one
- Solve and verify a real question from someone in the room
Capstone and Close
- Apply the workflow to one real task from your own job
- Document the actual prompts used and the verification attached to each
- Identify the first action you will take when you return to your desk
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
Choose dates and book your instructor-led class on nobledesktop.com.
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
-
–
In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
Confirm current dates and availability on Noble Desktop before booking.
Self-paced course
Learn through recorded lessons on your own schedule.
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $695
- Course length
- 12 hours
- Schedule
- On your schedule
Transform your data analysis workflow with artificial intelligence in this self-paced course. You’ll explore AI tools that automate data collection, preparation, analysis, and visualization, making it possible to uncover valuable insights with minimal coding. By the end of the course, you’ll be able to apply AI-driven analytics across industries such as finance, marketing, and healthcare, and clearly present your findings through professional-quality visualizations and reports.
The course is organized into structured, self-paced modules that guide you through the full AI-enabled analytics process. You’ll start with an overview of AI tools for data analysis, then move through data collection, cleaning, and preprocessing techniques at your own pace. From there, you’ll explore exploratory data analysis, predictive modeling, and advanced topics such as natural language processing and time series forecasting. The course concludes with a capstone-style project that allows you to apply everything you’ve learned to a real-world data analysis scenario.
Self-paced curriculum
What you'll learn self-paced
- Get an introduction to widely used AI platforms such as IBM Watson, Google AI, Tableau, and Microsoft Azure AI
- Learn how to automate data cleaning, including handling missing values, outliers, and inconsistent data using AI tools
- Use AI to generate summary statistics, create visualizations, and uncover patterns within datasets
- Build and evaluate predictive models by applying regression, classification, and clustering techniques with AI support
- Explore practical uses of natural language processing for text analysis and AI-powered time series forecasting
Self-paced syllabus
Introduction to AI in Data Analytics
Overview of AI & Data Analytics
- Understanding AI and its applications in data analytics
- Benefits of using AI for data analysis
Introduction to AI Tools
- Overview of popular AI tools and platforms (e.g. IBM Watson, Google AI, Tableau, Microsoft Azure AI)
Data Collection & Preparation
Data Sources & Collection Methods
- Identifying various data sources
- Using AI tools to collect data from different platforms
Data Cleaning & Preprocessing
- Automated data cleaning techniques
- Handling missing data and outliers using AI tools
Exploratory Data Analysis (EDA)
Understanding Your Data
- Using AI tools to generate summary statistics
- Visualizing data distributions and relationships
Advanced EDA Techniques
- Automated pattern and trend detection
- AI-driven feature selection and engineering
Data Visualization
Creating Visualizations
- Using AI tools to create charts, graphs, and dashboards
- Best practices for data visualization
Interactive Dashboards
- Building interactive dashboards with AI tools
- Customizing dashboards to meet specific needs
Predictive Analytics & Modeling
Introduction to Predictive Modeling
- Understanding regression, classification, and clustering
- Using AI tools to build predictive models
Model Evaluation & Validation
- Automated model evaluation techniques
- Understanding metrics and performance evaluation
Application of AI in Various Domains
Financial Data Analysis
- Case studies and applications in financial forecasting
Marketing Data Analysis
- Analyzing customer behavior and market trends
Healthcare Data Analysis
- Applications in patient data analysis and medical research
Advanced AI Techniques
Natural Language Processing (NLP)
- Using AI for text analysis and sentiment analysis
Time Series Analysis
- Automated time series forecasting with AI tools
Capstone Project
Project Planning & Execution
- Defining a project scope and objectives
Applying Learned Skills
- Using AI tools to complete a comprehensive data analysis project
Presentation & Reporting
- Presenting findings using AI-generated reports and visualizations
What's included with self-paced
- Self-paced video lessons
Self-paced lessons
Watch free previews and explore the lessons included with enrollment at Noble Desktop.
AI for Data Analytics (Self-Paced)
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Course Introduction
Lesson 1: Introduction to AI in Data Analytics
1:16
Understand how AI enhances traditional analytics and supports descriptive, predictive, and prescriptive decision making.
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Reframing Analytics Through AI Augmentation
Lesson 1: Introduction to AI in Data Analytics
5:15
Define the four types of analytics and position AI as a tool that augments rather than replaces analysts.
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Understanding the AI Pipeline and Managing Bias
Lesson 1: Introduction to AI in Data Analytics
5:56
Explain the AI pipeline from data collection through deployment & examine how bias in data, algorithms, or deployment can lead to unfair outcomes.
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Unlocking the Strategic Benefits of AI in Analytics
Lesson 1: Introduction to AI in Data Analytics
7:22
Identify the benefits of AI including speed, accuracy, scalability, and discovery.
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Navigating Enterprise AI Platforms and Smart Use Cases
Lesson 1: Introduction to AI in Data Analytics
5:18
Compare enterprise AI platforms and BI tools that integrate AI capabilities.
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Accelerating Descriptive Statistics with AI
Lesson 1: Introduction to AI in Data Analytics
6:12
Review core descriptive statistics including mean, median, variance, distribution shape, and outliers.
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Discovering Relationships Through Correlation
Lesson 1: Introduction to AI in Data Analytics
5:18
Explain the correlation coefficient and reinforce that correlation does not imply causation.
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Predicting Outcomes with Regression and Forecasting
Lesson 1: Introduction to AI in Data Analytics
7:41
Use AI to generate and interpret predictive models in accessible language.
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Applying Probability and Distributions in Analysis
Lesson 1: Introduction to AI in Data Analytics
5:33
Define theoretical, experimental, conditional, and compound probability.
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Modeling Risk with Monte Carlo Simulation
Lesson 1: Introduction to AI in Data Analytics
6:06
Explain Monte Carlo simulation and expected value under uncertainty.
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Integrating End to End AI Analysis
Lesson 1: Introduction to AI in Data Analytics
5:27
Combine descriptive, predictive, and probabilistic techniques into a unified AI prompt.
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Sourcing and Classifying Data for AI
Lesson 2: Data Collection & Preparation
6:24
Identify internal and external data sources and understand how AI accelerates data collection through APIs, scraping, and file merging.
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Cleaning Dirty Data with AI Assistance
Lesson 2: Data Collection & Preparation
4:41
Learn to recognize common data quality issues such as missing values, duplicates, inconsistent formats, and outliers.
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Automating Exploratory Data Review and Cleanup
Lesson 2: Data Collection & Preparation
6:51
Evaluate a real dataset to identify structural issues, missing values, formatting errors, and outliers.
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Applying the DIG Framework for AI Assisted EDA
Lesson 3: Exploratory Data Analysis (EDA)
7:24
Understand EDA as the process of reviewing, visualizing, and filtering data to uncover structure, trends, and anomalies.
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Designing Insightful Pivot Tables with AI Guidance
Lesson 4: Data Visualization
6:18
Build meaningful summaries using pivot tables to compare categories, calculate metrics, and surface unusual patterns.
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Creating Interactive Dashboards and Data Stories
Lesson 4: Data Visualization
7:46
Select effective chart types such as bar, line, and scatter plots to communicate trends and relationships.
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Building and Evaluating Predictive Models with AI
Lesson 5: Predictive Analytics & Modeling
4:19
Differentiate between regression, classification, and clustering models and connect each to real business use cases.
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Demonstrating AI Powered Classification and Model Validation
Lesson 5: Predictive Analytics & Modeling
5:09
Build a predictive model that classifies satisfaction scores using an uploaded dataset and structured prompts.
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Applying Predictive Models
Lesson 6: Application of AI in Various Domains
5:17
Connect regression, classification, and clustering models to real use cases such as churn prediction, demand forecasting, workforce planning, and employee retention.
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Leveraging AI Modeling in Finance and Healthcare
Lesson 6: Application of AI in Various Domains
2:03
Apply predictive modeling to fraud detection, credit scoring, financial forecasting, and real time risk monitoring.
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Building Scenario Models and Executive Recommendations with AI
Lesson 6: Application of AI in Various Domains
4:05
Generate revenue, cost, and profit scenarios using AI driven modeling and compare financial outcomes across strategic options.
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Analyzing Unstructured Text with Natural Language Processing
Lesson 7: Advanced AI Techniques
5:55
Apply NLP techniques such as sentiment analysis, keyword extraction, topic clustering, summarization, and text classification to transform unstructured reviews and comments into structured insights.
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Forecasting Trends with Time Series Analysis
Lesson 7: Advanced AI Techniques
4:08
Identify trends, seasonality, cycles, and anomalies in time based data such as sales or website traffic.
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Conclusion
Lesson 7: Advanced AI Techniques
1:27
Reflect on the core AI frameworks, hands on projects, and real world applications explored throughout the course.
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