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Welcome to the #1 best-selling Power BI Desktop course – completely rebuilt for 2023/2024!

If you’re a data professional or aspiring analyst looking to learn the top business intelligence platform on the market, you’ve come to the right place. With more than 100,000 perfect 5-star reviews from students around the world, this is the course you’ve been looking for.

Throughout the course, you’ll learn from top instructors on the Maven Analytics team and put your skills to the test with hands-on projects and unique, real-world assignments.

THE COURSE PROJECT:

You’ll play the role of Business Intelligence Analyst for AdventureWorks Cycles, a fictional manufacturing company. Your role is to transform raw data into professional-quality reports and dashboards to track KPIs, compare regional performance, analyze product-level trends, and identify high-value customers.

But don’t worry, we’ll be here to guide you along every step of the way, with intuitive, crystal clear explanations and helpful pro tips to take you from zero to expert – guaranteed.

This course is designed to follow the key stages of the business intelligence workflow (data prep, data modeling, exploratory data analysis, data visualization & dashboard design) and simulate real-world tasks that data professionals encounter every day on the job:

STAGE 1: Connecting & Shaping Data

In this stage we’ll focus on building automated workflows to extract, clean, transform, and load our project data using Power Query, and explore common data connectors, storage modes, profiling tools, table transformations, and more:

Data connectors

Storage & import modes

Query editing tools

Table transformations

Connecting to a database

Extracting data from the web

QA & Profiling tools

Text, numerical, date & time tools

Rolling calendars

Index & conditional columns

Grouping & aggregating

Pivoting & unpivoting

Merging & appending queries

Data source parameters

Importing Excel models

STAGE 2: Creating a Relational Data Model

In stage 2 we’ll review data modeling best practices, introduce topics like cardinality, normalization, filter flow and star schemas, and begin to build our AdventureWorks data model from the ground up:

Database normalization

Fact & dimension tables

Primary & foreign keys

Star & snowflake schemas

Active & inactive relationships

Relationship cardinality

Filter context & flow

Bi-directional filters

Model layouts

Data formats & categories

Hierarchies

STAGE 3: Adding Calculated Fields with DAX

In stage 3 we’ll introduce data analysis expressions (DAX). We’ll create calculated columns and measures, explore topics like row and filter context, and practice applying powerful tools like filter functions, iterators, and time intelligence patterns:

DAX vs. M

Calculated columns & measures

Implicit, explicit & quick measures

Measure calculation steps

DAX syntax & operators

Math & stats functions

Conditional & logical functions

The SWITCH function

Text functions

Date & time functions

The RELATED function

CALCULATE, FILTER & ALL

Iterator (X) functions

Time intelligence patterns

STAGE 4: Visualizing Data with Reports

Stage 4 is about bringing our data to LIFE with reports and dashboards. We’ll review data viz best practices, building and format basic charts, and add interactivity with bookmarks, slicer panels, parameters, tooltips, report navigation, and more:

Data viz best practices

Dashboard design framework

Cards & KPIs

Line charts, trend lines & forecasts

On-object formatting

Table & matrix visuals

Conditional formatting

Top N filtering

Map visuals

Drill up, drill down & drillthrough

Report slicers & interactions

Bookmarks & page navigation

Numeric & fields parameters

Custom tooltips

Importing custom visuals

Managing & viewing roles (RLS)

Mobile layouts

Publishing to Power BI Service

We’ll also introduce brand new features as they are released, powerful artificial Intelligence tools like decomposition trees, key influencers, smart narratives and natural language Q&A, and performance optimization techniques to keep your reports running smoothly at scale.

Ready to get started? Join today and get immediate, lifetime access to:

15+ hours of high-quality video

200+ page Power BI ebook

25 homework assignments & solutions

Downloadable course project files

Expert Q&A support forum

30-day money-back guarantee

If you’re looking for the ONE course to help you build job-ready Power BI skills, you’ve come to the right place.

Happy learning!

-Chris & Aaron (Maven Analytics)

See why this is one of the TOP-RATED Power BI courses in the world:

“I believe this is the best Power BI course out there. I spent £1400 to attend a 3-day Power BI crash course, and have to confess it’s nothing compared to the knowledge, skills, expertise and understanding derived from this course. I am forever grateful to Chris and the Maven Analytics team for doing such amazing work and to Udemy for making this available.”

-Isaac Mensah

"Resources are awesome. Presenter is brilliant. I found this course more useful than the official Power BI course from Microsoft. Things are easy to follow, and presentations are high quality."

-Jacobus M.

"Chris is a skilled communicator and does a great job of explaining a complex tool like Microsoft Power BI. His 'pro-tips' are great for new user productivity and gaining a sense of the big picture, and I value his best practices on building and managing Power BI queries and reports. I'm feeling much more confident to dig in and use Power BI on my own projects!"

-Bill Jerrow

“Simply put, this course is AMAZING! The instructor literally takes you step-by-step from knowing nothing about Power BI into nearly an expert! I have had experience working with Power BI even in a corporate setting in the past, and I was still blown away by the level of granularity Chris was able to casually explain in a way that made sense. The hands-on exercises are THE perfect way to reinforce the concepts you learn throughout the course and connect theory to application. Can't speak enough to how great this course is, I will definitely be coming back to it as a reference guide in my work and would recommend it to anyone looking to learn Power BI!”

-Ikenna Egbosimba

“I've been in university classrooms for much of my life and Chris is a university level instructor.”

-Allan Searl

Looking for the full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau, Alteryx & Python!

Automate Files, Excel Reports, Web Scraping & Emails with Python Through 4 Real-World Projects

New

Description

Python Automation: Save Hours with 4 Real Projects

Are you tired of performing the same computer tasks over and over again?

What if you could use Python to automate repetitive work, save hours every week, and improve your productivity with just a few lines of code?

In this beginner-friendly course, you'll learn practical Python automation by building 4 real-world projects from scratch. Instead of focusing on complicated theory, this course takes a hands-on approach and shows you how Python can automate tasks that many people perform manually every day.

Whether you're a student, office worker, freelancer, business owner, or aspiring developer, automation is one of the most valuable skills you can learn. By the end of this course, you'll understand how to use Python to handle repetitive tasks more efficiently and create useful automation tools for yourself or your workplace.

What You'll Learn

- Python fundamentals required for automation

- Variables, data types, lists, dictionaries, loops, and functions

- Working with files and folders using Python

- Reading and updating Excel spreadsheets

- Generating automated Excel reports

- Collecting data from websites using web scraping

- Understanding HTML and extracting webpage information

- Sending emails automatically with Python

- Combining multiple automation techniques into complete workflows

Projects You'll Build

Project 1: Automatic File Organizer

Build a tool that automatically sorts files into organized folders.

Project 2: Excel Report Generator

Read spreadsheet data, perform calculations, and generate reports automatically.

Project 3: Website Data Scraper

Collect and save information from websites using Python.

Project 4: Bulk Email Sender

Create a system that sends emails automatically to multiple recipients.

Final Automation Challenge

Combine everything you've learned into a complete automation workflow.

Who This Course Is For?

- Complete Python beginners

- Students interested in automation

- Office workers who want to save time

- Freelancers looking to increase productivity

- Business professionals who work with files, spreadsheets, or reports

- Anyone who wants to automate repetitive tasks using Python

Why Take This Course?

Many Python courses teach programming concepts without showing how they are used in real life. This course focuses on practical automation projects that demonstrate how Python can solve everyday problems and eliminate repetitive work.

By the end of this course, you'll have built multiple automation projects, gained valuable hands-on experience, and developed the confidence to create your own Python automation solutions.

Enroll today and start saving hours of manual work with Python automation.

Who this course is for:

Complete beginners with little or no Python experience.

Students who want to learn automation skills that can be applied immediately.

Office workers who perform repetitive tasks involving files, spreadsheets, reports, or emails.

Freelancers looking to improve productivity and automate routine work.

Business professionals who want to save time using Python.

Anyone interested in learning web scraping, Excel automation, and email automation.

Aspiring developers who prefer learning through hands-on projects rather than theory alone.

Analyze Data, Automate Reports, Generate Insights, and Build Business Intelligence with Claude AI

“This course contains the use of artificial intelligence”

Welcome to Claude AI for Data Analysis & Business Intelligence, a comprehensive, practical course designed to help professionals, analysts, managers, entrepreneurs, and business leaders transform the way they work with data using the power of Claude AI.

In today's data-driven world, organizations generate massive amounts of information every day. The challenge is no longer collecting data—it is turning that data into meaningful insights, actionable recommendations, and strategic decisions. Traditional analytics workflows often require multiple tools, complex formulas, lengthy reporting processes, and significant manual effort. This course shows you how to leverage Claude AI as your intelligent analytics partner to dramatically accelerate data analysis, reporting, forecasting, research, and business intelligence workflows.

Throughout this course, you will learn how to build a complete AI-powered analytics workflow using Claude. You will start by understanding the foundations of modern business intelligence, learning how AI is transforming analytics, reporting, and decision-making across organizations. You will then create an optimized analytics workspace, develop reusable analytical frameworks, and learn how to communicate with Claude using professional data analyst prompts that generate consistent, high-quality outputs.

You will work extensively with Excel spreadsheets, CSV files, and common business datasets, learning how to explore data, identify trends, uncover anomalies, perform segmentation, compare performance, and generate meaningful insights. You will also learn how to analyze KPIs, automate recurring reports, create executive summaries, and develop decision-ready recommendations for leadership teams.

As the course progresses, you will dive into specialized areas of business analytics including financial analysis, marketing analytics, sales intelligence, and operations performance analysis. You will learn how to evaluate business performance, identify growth opportunities, assess risks, analyze customer behavior, improve operational efficiency, and support strategic planning initiatives using AI-assisted workflows.

You will also master the art of executive reporting and data storytelling, transforming complex findings into clear narratives that business leaders can understand and act upon. In addition, you will learn how to design effective dashboards, recommend visualizations, plan reporting structures, and create presentation-ready business intelligence outputs.

The course further explores forecasting, predictive analysis, market research, and competitive intelligence, showing you how Claude can assist with trend analysis, scenario planning, opportunity discovery, competitor benchmarking, and strategic recommendations. These skills will help you move beyond basic reporting and into higher-value analytical work that drives business outcomes.

One of the most valuable aspects of this course is its strong emphasis on hands-on learning. Throughout the program, you will complete practical exercises, real-world business scenarios, guided projects, and hands-on labs that demonstrate how Claude can be applied to everyday analytics challenges. Rather than focusing on theory alone, you will build repeatable systems and workflows that can be immediately applied in your professional environment.

By the end of this course, you will be able to use Claude AI to analyze business data, automate reporting workflows, generate executive-level insights, support strategic decision-making, perform forecasting and research, design business intelligence systems, and even create specialized AI Analyst Teams that help scale analytical capabilities across an organization.

Whether you are a business analyst, data analyst, manager, consultant, entrepreneur, finance professional, marketing professional, operations leader, or simply someone who wants to become more effective with data, this course will provide a practical framework for using AI to work faster, uncover deeper insights, and make better business decisions.

Join us and learn how to transform Claude AI from a simple chatbot into a powerful Business Intelligence Partner, Analytics Assistant, and AI-Powered Decision Support System.

Manage AWS IAM with Claude Code, Terraform and GitLab

Unlock the future of cloud security and automation. As cloud environments grow, managing AWS Identity and Access Management (IAM) manually becomes a bottleneck and a security risk.This hands-on course bridges the gap between traditional DevOps and modern AI assistance, teaching you how to build, secure, and deploy AWS IAM infrastructure at scale.

You will start with the fundamentals of AWS IAM before quickly diving into Claude Code, leveraging next-generation AI to accelerate your development workflow. From there, you will transition into practical Infrastructure as Code (IaC), using Claude Code to co-pilot the creation of robust Terraform configurations for IAM users, groups, and precise, least-privilege policies.

Finally, you will bring it all into a production-ready GitLab CI/CD pipeline. You won't just automate deployments—you will learn how to embed rigorous automated security gates directly into your pipeline to catch misconfigurations before they hit production.

What You Will Learn:

AI-Assisted IaC: Use Claude Code to write, refactor, and optimize Terraform.

AWS IAM Mastery: Design secure, scalable access controls.

DevSecOps Pipelines: Build GitLab CI/CD workflows with integrated security gates.

Ready to revolutionize your workflow? Bridge the gap between AI innovation and cloud security, and build pipelines that are fast, automated, and secure by design.

Build & deploy 8 microservices to production on AWS — Karpenter, GitOps, CI/CD, Observability + Resume Prep

What if you could deploy production-grade AWS infrastructure without writing a single line of code yourself?

That’s exactly what this course is about. You’ll take a real Spring Boot microservices application—eight services, real databases, real traffic—and push it all the way to production on AWS. Every Terraform module, every Kubernetes manifest, every CI/CD pipeline, and every runbook is generated by Claude Code. Your role is to think like an architect: write precise prompts, review the outputs, and make sure everything is production-ready.

This isn’t a step-by-step tutorial. It’s a project.

You step into the role of a DevOps engineer handed a Jira board and expected to deliver. You’ll work through real epics—networking, compute, container registry, databases, secrets, GitOps, observability—in the same sequence a real production team would follow.

What you’ll build:

A VPC with public subnets across multiple availability zones

An Amazon EKS cluster running cost-optimized Graviton ARM nodes

Amazon RDS MySQL for persistent storage

Amazon ECR with lifecycle policies and vulnerability scanning

A GitOps pipeline using ArgoCD (auto-sync for dev, manual approvals for production)

GitHub Actions CI pipelines that build, push, and trigger deployments

Secrets Manager integrated with External Secrets Operator for Kubernetes

A full observability stack with Prometheus, Grafana, Fluent Bit, and Zipkin

Why Claude Code?

AI doesn’t replace engineers—it amplifies them. But only if you know how to guide it, evaluate its output, and catch what it misses. This course focuses on building that skill in the context of a real-world project, so you walk away with both working infrastructure and a repeatable workflow.

By the end, you’ll have:

A production-ready AWS platform in your GitHub portfolio

Hands-on experience with Terraform, EKS, ArgoCD, and GitHub Actions

A repeatable, AI-assisted workflow you can apply to future projects

If you’ve been meaning to get serious about cloud infrastructure, this is where it starts.

Master NumPy, Pandas, Matplotlib, Scikit-Learn and PyTorch with real African datasets — before your first ML mod

The course you’re viewing, *Python for Data Science & Machine Learning Foundations* on Udemy, is designed to give learners a solid grounding in both Python programming and the core concepts of data science and machine learning. Here’s a quick breakdown of what such a course typically covers:

### 📘 Key Learning Areas

- **Python Basics**: Variables, data types, loops, functions, and libraries.

- **Data Handling**: Using libraries like NumPy and Pandas for data manipulation and analysis.

- **Visualization**: Creating plots and charts with Matplotlib and Seaborn to understand data patterns.

- **Machine Learning Foundations**: Introduction to supervised and unsupervised learning, regression, classification, clustering.

- **Model Evaluation**: Understanding accuracy, precision, recall, and other metrics.

- **Practical Applications**: Hands-on projects to apply concepts to real-world datasets.

### 🎯 Who It’s For

- Beginners in Python who want to transition into data science.

- Professionals looking to strengthen their machine learning fundamentals.

- Students preparing for advanced AI or data science coursework.

### 🚀 Why It’s Useful

- Builds a strong foundation before diving into advanced ML frameworks like TensorFlow or PyTorch.

- Helps you understand the “why” behind algorithms, not just the “how.”

- Provides practical coding exercises that mirror industry workflows.

Would you like me to create a **structured study roadmap** for this course—breaking down what to focus on week by week—so you can pace your learning effectively?