AI, Analytics, and Cloud Computing

via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

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Artificial intelligence, business analytics, and cloud computing are transforming how organizations operate, compete, and make decisions. This specialization teaches you how businesses use analytics, AI, machine learning, data-driven decision-making, and cloud computing technologies to solve problems, improve performance, and drive smarter outcomes. You'll start by learning the business analytics process, exploring descriptive, predictive, and prescriptive analytics along with tools such as online analytical processing (OLAP), dashboards, and key performance indicators (KPIs) used to transform data into business insights. You'll then examine artificial intelligence and machine learning, learning how neural networks, generative AI, and tools like ChatGPT power applications ranging from fraud detection to recommendation engines. Finally, you'll explore how organizations plan, justify, and acquire IT applications, walk through the systems development life cycle, and learn how cloud computing, including IaaS, PaaS, SaaS, and leading cloud platforms, supports today's digital businesses. By the end of this specialization, you'll be able to evaluate analytics, AI, and cloud computing opportunities, communicate effectively with business and technology stakeholders, and apply data-informed thinking to real business decisions. No coding or technical background required.

Syllabus

Course 1: Business Analytics and Data-Driven Decision Making
Course 2: Artificial Intelligence for Business
Course 3: IT Acquisition, Systems Development, and Cloud Computing

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Artificial intelligence, machine learning, deep learning, and neural networks are transforming how businesses operate, compete, and make decisions. This course explores how AI and machine learning work and how organizations across industries use tools like ChatGPT, generative AI, computer vision, and natural language processing to solve real business problems. You'll learn to differentiate supervised, semi-supervised, unsupervised, reinforcement, and deep learning, and understand how neural networks process data to power AI applications such as image recognition, speech recognition, robotics, and intelligent agents. Through real-world examples from Amazon, JPMorgan Chase, Wayfair, and other leading companies, you'll explore how AI is applied in accounting, finance, marketing, operations, human resources, and information systems. By the end of this course, you'll be able to evaluate AI and machine learning opportunities, communicate confidently with technical teams, and apply AI-informed thinking to real business decisions, regardless of your major or industry. No coding or technical background required.
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Business analytics, business intelligence, and data-driven decision-making are essential skills for today's professionals. In this course, you'll learn how organizations use data to solve business problems, improve performance, and make informed decisions. Explore the business analytics process, from defining business challenges and managing data to generating insights through descriptive, predictive, and prescriptive analytics. You'll also examine how organizations use data visualization and presentation tools to communicate findings and drive action. Through real-world examples, you'll discover how analytics helps organizations identify trends, forecast outcomes, and support decisions. You'll explore decision-support frameworks, data mining, online analytical processing (OLAP), dashboards, and key performance indicators (KPIs) used to transform data into business insights. By the end of the course, you'll understand how organizations use analytics to evaluate opportunities and make decisions across marketing, operations, finance, and technology. You'll also gain familiarity with Microsoft Excel, OLAP, dashboards, and data mining methods used for data-informed decision-making.
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IT acquisition, systems development, and cloud computing are core competencies for anyone involved in building or managing business technology. In this course, you'll learn how organizations plan, justify, and acquire information systems, and how they choose between building custom applications and deploying cloud-based solutions. You'll explore cost-benefit approaches like net present value, ROI, and break-even analysis, then examine the strategic decisions behind buying, leasing, or building applications. You'll walk through the traditional systems development life cycle — from feasibility study to implementation and maintenance — and modern alternatives such as agile development, DevOps, and low-code/no-code platforms. You'll then dive into cloud computing: the IaaS, PaaS, and SaaS service models, the public, private, hybrid, and vertical cloud types, and the benefits and risks organizations face when moving to the cloud. You'll compare the "Big Three" cloud vendors — AWS, Microsoft Azure, and Google Cloud — and finish by learning how Web services and service-oriented architecture let systems share data and work together seamlessly.