FreeCourse Logo
FreeCourse.io
Verified CouponsFree CoursesJobsBlog
Categories
Home/Courses/AI-Powered Data Analysis & Business Intelligence 2026
AI-Powered Data Analysis & Business Intelligence 2026
Development100% OFF

AI-Powered Data Analysis & Business Intelligence 2026

Udemy Instructor
0(455 students)
Self-paced
All Levels

About this course

“This course contains the use of artificial intelligence”The world of data analytics is changing rapidly, and Artificial Intelligence is transforming how analysts collect, clean, analyze, visualize, and communicate data. In this comprehensive course, you'll learn how to combine the power of ChatGPT, prompt engineering, Microsoft Excel, SQL, Power BI, and AI Agents to complete analytical tasks faster, improve accuracy, and generate meaningful business insights. Whether you're starting your analytics journey or looking to modernize your existing workflow, this course provides a practical, hands-on roadmap for becoming an AI-powered data professional.

You'll begin by building a strong foundation in AI for Data Analytics, understanding what modern AI can and cannot do, and learning how to use it responsibly in professional environments. You'll then master Prompt Engineering, discovering how to write effective prompts, structure context, refine outputs, use advanced prompting techniques, and create reusable prompt templates that consistently produce high-quality analytical results. Next, you'll explore how AI can dramatically improve productivity in Microsoft Excel.

You'll learn to generate and debug formulas, clean and prepare datasets, apply conditional formatting, build Pivot Tables, create professional charts, develop interactive KPI dashboards, perform trend analysis, and generate forecasts with AI assistance. From there, you'll move into SQL, where you'll master everything from SELECT statements and filtering to joins, aggregations, subqueries, Common Table Expressions (CTEs), Window Functions, and query optimization using AI as your coding assistant. Clean, reliable data is the foundation of successful analytics, so you'll dedicate an entire section to Data Cleaning and Feature Engineering.

You'll learn techniques for handling missing values, removing duplicates, validating and profiling datasets, transforming data, creating derived columns, categorizing information, working with dates, and enriching data for deeper analysis. The course then shifts to Business Analytics, where you'll learn how organizations measure performance using KPIs, customer metrics, operational metrics, and financial metrics. You'll develop analytical thinking skills by performing descriptive, diagnostic, predictive, and prescriptive analytics, while also learning how to tell compelling business stories with data that influence decision-makers.

You'll build professional dashboards using Power BI, learning data modeling, relationships, visualizations, report design, and AI-assisted DAX generation. You'll discover how AI can recommend visualizations, optimize dashboard performance, and help publish executive-ready business intelligence reports. Moving beyond traditional reporting, you'll learn how AI can automatically generate executive summaries, identify root causes, produce actionable recommendations, detect risks and opportunities, and transform raw analytical findings into clear business narratives.

You'll also master data storytelling, presentation design, stakeholder communication, and executive reporting techniques that make complex data understandable and actionable. Automation is becoming an essential skill for modern analysts, so you'll explore how to automate repetitive reporting tasks using AI-powered workflows, scheduled reports, KPI monitoring, alerts, report distribution, and no-code automation platforms. You'll discover how AI integrations can streamline business processes and dramatically improve productivity across analytics teams.

One of the most exciting sections focuses on AI Agents for Analytics. You'll learn how specialized AI agents can perform research, write SQL, generate reports, build dashboards, collaborate with one another, and automate complete analytical workflows. You'll also build your own Custom GPTs, create knowledge bases, connect external tools, and design practical AI assistants tailored to real business scenarios.

To reinforce everything you've learned, you'll complete multiple real-world projects, including building sales dashboards, performing customer and marketing analytics, creating financial reports, analyzing HR data, and executing complete end-to-end analytics projects—from raw data preparation to executive presentation. These projects will help you build a portfolio that demonstrates practical skills employers value. Finally, you'll explore the future of AI-powered analytics, including Generative AI, Autonomous Analytics, AI Copilots, Agentic AI, and emerging industry trends.

You'll also receive guidance on building a professional portfolio, preparing for analytics interviews, earning valuable certifications, and creating a long-term learning plan that keeps your skills current in an evolving industry. By the end of this course, you'll be able to confidently use ChatGPT, Excel, SQL, Power BI, Prompt Engineering, AI Automation, and AI Agents together to analyze data, build dashboards, generate business insights, automate repetitive workflows, and solve real-world business problems. You'll gain practical, job-ready skills that can immediately improve your productivity and help you stand out in today's rapidly evolving analytics and business intelligence landscape.

Skills you'll gain

Data ScienceEnglish

Available Coupons

Loading...

Course Information

Level: All Levels

Suitable for learners at this level

Duration: Self-paced

Total course content

Instructor: Udemy Instructor

Expert course creator

This course includes:

  • 📹Video lectures
  • 📄Downloadable resources
  • 📱Mobile & desktop access
  • 🎓Certificate of completion
  • ♾️Lifetime access
$0$87.99

Save $87.99 today!

Enroll Now - Free

Redirects to Udemy • Limited free enrollments

Share this course

https://freecourse.io/courses/ai-powered-data-analysis-business-intelligence-2026

You May Also Like

Explore more courses similar to this one

Machine Learning & Predictive Analytics Python Exams
Development
0% OFF

Machine Learning & Predictive Analytics Python Exams

Udemy Instructor

Data tells a story of the past, but Machine Learning predicts the future. Welcome to the Machine Learning & Predictive Analytics practice assessments! In today's tech landscape, understanding how to train an algorithm is one of the most lucrative skills you can possess. However, technical interviews rigorously test your ability to prevent data leakage, tune hyperparameters, and select the correct evaluation metrics. This comprehensive practice test course provides you with 200 expertly crafted, highly unique practice questions designed to simulate the rigorous challenges faced by professional Data Scientists.Across these four complete practice exams, you will be thrown into realistic predictive modeling scenarios. You will test your ability to build house price prediction regression models, develop customer churn prediction classifiers, and train deep neural networks for structured business data. The questions push you to evaluate complex data science trade-offs: When should you use a Random Search over a Grid Search? How do you stop a TensorFlow/Keras model from overfitting? Why is accuracy a terrible metric for a highly imbalanced dataset?Every single question in this course is unique and includes a detailed explanation of the "why" behind the correct algorithmic choice. By reviewing these explanations, you will learn industry-standard methodologies for cross-validation and pipeline creation in Scikit-Learn. If you are preparing for a technical data science interview, a university exam, or looking to certify your Kaggle skills, this is your ultimate testing ground. Enroll today and start predicting!Course locale: English (US) Course instructional level: Expert Level Course category: Development Course subcategory: Data Science

0.0•620•Self-paced
FREE$86.99
Enroll
Python Data Science and Machine Learning Made Easy
Development
0% OFF

Python Data Science and Machine Learning Made Easy

Udemy Instructor

Unlock the power of Python Data Science and Machine Learning and transform your data into actionable insights. This course is designed for beginners, aspiring data scientists, and developers who want to gain practical, hands-on experience in one of the fastest-growing fields in technology.You will start by learning the fundamentals of Python programming and how it applies to data science. Key concepts such as data types, loops, functions, and libraries will be introduced, giving you a solid foundation for more advanced topics in Machine Learning.You will dive into data manipulation and analysis using Python’s powerful libraries such as Pandas and NumPy. You’ll learn how to clean, organize, and explore data efficiently, preparing it for machine learning workflows and real-world applications.The course then covers Machine Learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and model evaluation. You’ll understand how to build predictive models and make data-driven decisions using Python.Throughout the course, you will gain practical skills including:Working with Python for data analysis and visualizationCleaning and preparing datasets for machine learningBuilding and evaluating machine learning modelsApplying algorithms such as regression, classification, and clusteringVisualizing insights using Python libraries like Matplotlib and SeabornBy the end of this course, you will be able to confidently apply Python Data Science and Machine Learning techniques to real-world problems. You’ll know how to turn raw data into meaningful insights, create predictive models, and present your findings effectively.This course is perfect for:Beginners looking to enter the field of data science and machine learningDevelopers and analysts wanting to enhance their Python skillsStudents preparing for data science roles or competitionsAnyone interested in leveraging Python for data-driven decision-makingEnroll now to Master Python Data Science and Machine Learning, real-world examples, and hands-on exercises that will prepare you for a career in data science.

4.5•2.6K•Self-paced
FREE$84.99
Enroll
DP-900 Azure Data Fundamentals Practice Exam Questions 2025
Development
0% OFF

DP-900 Azure Data Fundamentals Practice Exam Questions 2025

Udemy Instructor

Prepare for the DP-900 exam with confidence! This set includes 230 unique practice questions created from scratch and fully compliant with the official 2025 exam syllabus.The DP-900: Microsoft Azure Data Fundamentals exam syllabus is structured around four main domains, covering core data concepts and how they are implemented using Microsoft Azure data services.Domain                                                                                                               Approximate Weighting1. Describe core data concepts                                                                                    25−30%2. Identify considerations for relational data on Azure                                            20−25%3. Describe considerations for working with non-relational data on Azure        15−20%4. Describe an analytics workload on Azure                                                               25−30%1. Describe Core Data Concepts (25−30%)Describe ways to represent data:Features of structured, semi-structured, and unstructured data.Identify options for data storage:Common formats for data files.Types of databases (e.g., relational, non-relational).Describe common data workloads:Features of transactional (OLTP) workloads.Features of analytical (OLAP) workloads.Identify roles and responsibilities for data workloads:Responsibilities for Database Administrators, Data Engineers, and Data Analysts.2. Identify Considerations for Relational Data on Azure (20−25%)Describe relational concepts:Features of relational data (tables, columns, rows).Normalization and why it is used.Common SQL statements (DDL and DML).Common database objects (tables, views, stored procedures).Describe relational Azure data services:The Azure SQL family of products (Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines).Azure database services for open-source database systems (e.g., Azure Database for PostgreSQL, Azure Database for MySQL).3. Describe Considerations for Working with Non-Relational Data on Azure (15−20%)Describe capabilities of Azure storage:Azure Blob storage.Azure File storage.Azure Table storage.Describe capabilities and features of Azure Cosmos DB:Identify use cases for Azure Cosmos DB (globally distributed, multi-model).Describe Azure Cosmos DB APIs (e.g., SQL, MongoDB, Cassandra).4. Describe an Analytics Workload on Azure (25−30%)Describe common elements of large-scale analytics:Considerations for data ingestion and processing (ETL/ELT).Options for analytical data stores (e.g., Data Lakes, Data Warehouses).Microsoft cloud services for large-scale analytics, including Azure Synapse Analytics and Azure Databricks.Describe consideration for real-time data analytics:Difference between batch and streaming data.Technologies for real-time analytics (e.g., Azure Stream Analytics).Describe data visualization in Microsoft Power BI:Identify capabilities of Power BI (interactive reports, dashboards).Describe features of data models in Power BI.

0.0•2.5K•Self-paced
FREE$101.99
Enroll
FreeCourse LogoFreeCourse

Freecourse.io brings you high-quality online courses with free certificates to help you upskill, boost your career, and achieve your goals anytime, anywhere.

Resources

  • Courses
  • Jobs
  • Categories
  • Features

Company

  • About
  • Blog
  • Contact

Legal

  • Privacy
  • Terms
  • Cookies
  • Licenses

© 2026 FreeCourse. All rights reserved.