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AI For Data Analysts · LearnSpace
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AI For Data Analysts

Курс от AI CERTs
Средний≈ 9.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Learn how to apply AI in data analysis to uncover insights faster, improve reporting accuracy, and support smarter business outcomes. For example, AI can help analysts identify customer behavior patterns, automate repetitive reporting tasks, detect anomalies in financial records, and improve forecasting speed across large datasets. This AI for Data Analysts course explains how artificial intelligence is used across data preparation, visualization, forecasting, reporting, and predictive analytics functions. Designed for beginners, it helps you build practical AI knowledge without requiring advanced coding or technical expertise. You’ll explore how AI supports trend analysis, dashboard reporting, business intelligence, and forecasting activities across modern enterprises. The course focuses on practical applications that help reduce manual effort, accelerate reporting cycles, and improve analytical accuracy in real business environments. Through guided examples, you’ll learn how to identify opportunities for automation and apply AI-driven insights more effectively. Practical scenarios such as customer trend analysis, sales forecasting, anomaly detection, reporting automation, and operational performance tracking make learning easier to apply across industries. You’ll also understand how AI-powered analytics skills are becoming increasingly valuable across sectors such as finance, healthcare, retail, telecom, manufacturing, and enterprise operations. Organizations are actively seeking professionals who can interpret data efficiently and support faster strategic decisions using AI-enabled tools. Whether you are exploring AI-powered analytics tools or looking to understand how AI integrates into modern data environments, this course offers a clear and structured starting point. By the end of the course, you’ll be able to use AI tools to analyze data more efficiently, improve reporting quality, strengthen forecasting accuracy, and contribute more confidently to data-driven business initiatives. Enroll in this AI data analytics training course to build practical, career-relevant AI skills and confidently apply AI in modern analytics environments.

Навыки, которые вы освоите

Data PreprocessingData CollectionForecastingFinancial ForecastingOperational Performance ManagementOperational EfficiencyInformation PrivacyAnomaly DetectionBusiness ReportingPredictive AnalyticsBusiness IntelligenceBusiness Analytics

Программа курса

7 модулей · 56 учебных материалов

01Module 1: Introduction to AI Agents10 материалов
Navigation VideoВидеоCourse IntroductionВидеоEbook: Module 1: Introduction to AI AgentsЧтение1.1 Understanding AI Agents in Data SystemsВидео

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AI CERTs Team

Преподаватель курса

AI For Data Analysts
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 9.3 ч

7 модулей

Язык: Английский

Субтитры: Венгерский, Дари, Пушту

Часть программы вашего университета
1.2 Components of AI AgentsВидео
1.3 Types of AI AgentsВидео
1.4 Architectures and Real-World Applications of AI AgentsВидео
1.5 Ethical and Design Considerations in AI Agents & Future ScopeВидео
ACTIVITY: HotspotPLUGIN
Quiz 1Задание
02Module 2: Data Agents and Their Role in AI Systems8 материалов
Ebook: Module 2: Data Agents and their Role in AI SystemsЧтение2.1 AI Data AgentsВидео2.2 AI vs. AI Data AgentВидео2.3 Components of AI Data AgentsВидео2.4 Types of AI Data AgentsВидео2.5 AI Data Agents – Types, Applications, and Real-World Case StudiesВидеоACTIVITY: TabPLUGINQuiz 2Задание
03Module 3: Data Collection and Acquisition for AI Data Agents9 материалов
Ebook: Module 3: Data Collection and Acquisition for AI Data AgentsЧтение3.1 AI Data Agents – Data Acquisition, Planning, and Case Studies in Smart AgricultureВидео3.2 AI Data Collection – Design, Preparation, and Quality AssuranceВидео3.3 Storing the Data and Process DocumentationВидео3.4 Annotation of the DataВидео3.5 Methods of Data Collection and Real-Time Data StreamingВидео3.6 Batch and API-Based Data CollectionВидеоACTIVITY: Problem StatementPLUGINQuiz 3Задание
04Module 4: Data Pre-Processing and Feature Engineering9 материалов
Ebook: Module 4: Data Pre-Processing and Feature EngineeringЧтение4.1 Data Pre-processing and Feature EngineeringВидео4.2 Feature Engineering for AI ModelsВидео4.3 Feature Selection, Feature Extraction, and Real-World ApplicationsВидео4.4 Dimensionality Reduction & EncodingВидео4.5 Binning, Bucketing, Feature Transformation and Feature EngineeringВидео4.6 No-Code AI Data Agent for Preprocessing & Feature EngineeringВидеоACTIVITY: Drag and DropPLUGINQuiz 4Задание
05Module 5: AI and Machine Learning Models for Data Agents8 материалов
Ebook: Module 5: AI and Machine Learning Models for Data AgentsЧтение5.1 Overview of Machine Learning for AI Data AgentsВидео5.2 Machine Learning Techniques in AI Data AgentsВидео5.3 Advanced Machine Learning for AI Data AgentsВидео5.4 Selecting and Training Machine Learning Models for AI Data AgentsВидео5.5 Advanced Machine Learning Models and Optimization for AI Data AgentsВидеоActivity: AccordionPLUGINQuiz 5Задание
06Module 6: Ethics, Security, and Privacy in AI Data Agents8 материалов
Ebook: Module 6: Ethics, Security, and Privacy in AI Data AgentsЧтение6.1 Key Ethical Issues and Broader Ethical Principles in AI Data AgentsВидео6.2 Fairness and Accountability in AI Data AgentsВидео6.3 Risk of Bias and Ethical Frameworks in AI Data AgentsВидео6.4 Security and Privacy Concerns in AI Data AgentsВидео6.5 Legal, Regulatory, and Ethical Data Practices in AI Data AgentsВидеоACTIVITY: Case StudyPLUGINQuiz 6Задание
07Module 7: Capstone Project: Building and Deploying an AI Data Agent4 материалов
Ebook: Module 7: Capstone Project: Building and Deploying an AI Data AgentЧтение7.1 Introductory Video for Capstone ProjectВидеоCourse SummaryВидеоQuiz 7Задание