How is AI used in data analytics?

AI helps analysts automate reporting, identify trends, detect anomalies, improve forecasting accuracy, and process large datasets more efficiently.

Is this course suitable for non-technical learners?

Yes. It is designed for both technical and non-technical professionals.

Will I learn how AI tools support analytical workflows?

Yes. The course explains how AI tools support reporting automation, forecasting, trend analysis, anomaly detection, and operational insights.

Can AI improve reporting and forecasting accuracy?

Yes. AI helps reduce manual effort, accelerate reporting cycles, and improve forecasting quality through predictive insights and pattern recognition.

What industries use AI-powered data analytics?

AI-powered analytics is widely used across finance, healthcare, retail, telecom, manufacturing, and enterprise operations.

Is coding required for this course?

No coding is required. The course focuses on practical understanding and business applications.

How will this course help my career?

This course helps you build practical AI analytics skills that are increasingly valuable across industries using AI-powered reporting, forecasting, and business intelligence tools.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI For Data Analysts

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AI For Data Analysts

Dozent: AI CERTs Team

1.517 bereits angemeldet

Bei Mehr erfahren enthalten

7 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

7 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Apply AI Across Data Analytics Functions
Use AI tools to support reporting, forecasting, trend analysis, and business intelligence activities
Analyze Data Faster and More Accurately
Identify patterns, detect anomalies, and interpret large datasets more efficiently using AI
Strengthen Forecasting and Reporting Quality
Apply AI-driven insights to improve forecasting accuracy and accelerate reporting processes
Support Smarter Business Insights
Use AI-powered analytics to generate clearer insights and contribute to faster strategic decisions

Kompetenzen, die Sie erwerben

Kategorie: Business Reporting
Kategorie: Anomaly Detection
Kategorie: Information Privacy
Kategorie: Business Analytics
Kategorie: Operational Efficiency
Kategorie: Forecasting
Kategorie: Financial Forecasting
Kategorie: Predictive Analytics
Kategorie: Business Intelligence
Kategorie: Operational Analysis
Kategorie: Data Preprocessing
Kategorie: Data Collection

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Bewertungen

7 Aufgaben

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Weitere Informationen zu Coursera für Unternehmen

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In diesem Kurs gibt es 7 Module

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.

Introduces the fundamentals of AI agents, their architecture, capabilities, and role in modern AI systems. This module covers how AI agents perceive information, make decisions, perform tasks autonomously, and interact with users, data sources, and other systems to achieve specific objectives.

Das ist alles enthalten

6 Videos1 Aufgabe1 Plug-in

6 VideosInsgesamt 31 Minuten

Course Introduction4 Minuten
1.1 Understanding AI Agents in Data Systems6 Minuten
1.2 Components of AI Agents5 Minuten
1.3 Types of AI Agents6 Minuten
1.4 Architectures and Real-World Applications of AI Agents4 Minuten
1.5 Ethical and Design Considerations in AI Agents & Future Scope5 Minuten

1 AufgabeInsgesamt 30 Minuten

Quiz 130 Minuten

1 Plug-inInsgesamt 5 Minuten

Activity: Hotspot5 Minuten

Explores the concept of data agents and their importance in managing, processing, and utilizing data within AI ecosystems. Learners will understand how data agents support data-driven decision-making, automate data workflows, improve data accessibility, and enhance the performance of AI applications.

Das ist alles enthalten

5 Videos1 Aufgabe1 Plug-in

Covers the principles and techniques involved in gathering high-quality data for AI data agents. This module focuses on data sources, data acquisition methods, data integration, data labeling, and strategies for ensuring data accuracy, relevance, and reliability.

Das ist alles enthalten

6 Videos1 Aufgabe1 Plug-in

6 VideosInsgesamt 35 Minuten

3.1 AI Data Agents – Data Acquisition, Planning, and Case Studies in Smart Agriculture7 Minuten
3.2 AI Data Collection – Design, Preparation, and Quality Assurance8 Minuten
3.3 Storing the Data and Process Documentation4 Minuten
3.4 Annotation of the Data7 Minuten
3.5 Methods of Data Collection and Real-Time Data Streaming4 Minuten
3.6 Batch and API-Based Data Collection5 Minuten

1 AufgabeInsgesamt 30 Minuten

Quiz 330 Minuten

1 Plug-inInsgesamt 5 Minuten

Activity: Problem Statement5 Minuten

Provides an understanding of preparing raw data for AI systems through cleaning, transformation, and optimization techniques. Learners will explore data preprocessing methods, handling missing data, normalization, feature selection, and feature engineering approaches to improve AI model performance.

Das ist alles enthalten

6 Videos1 Aufgabe1 Plug-in

6 VideosInsgesamt 36 Minuten

4.1 Data Pre-processing and Feature Engineering7 Minuten
4.2 Feature Engineering for AI Models8 Minuten
4.3 Feature Selection, Feature Extraction, and Real-World Applications5 Minuten
4.4 Dimensionality Reduction & Encoding7 Minuten
4.5 Binning, Bucketing, Feature Transformation and Feature Engineering6 Minuten
4.6 No-Code AI Data Agent for Preprocessing & Feature Engineering3 Minuten

1 AufgabeInsgesamt 30 Minuten

Quiz 430 Minuten

1 Plug-inInsgesamt 5 Minuten

Activity: Drag-and-Drop5 Minuten

Introduces machine learning algorithms and AI models that power data agents. This module explores model selection, training, evaluation, optimization, and the application of supervised, unsupervised, and advanced AI techniques to enable intelligent data processing and decision-making.

Das ist alles enthalten

5 Videos1 Aufgabe1 Plug-in

5 VideosInsgesamt 22 Minuten

5.1 Overview of Machine Learning for AI Data Agents5 Minuten
5.2 Machine Learning Techniques in AI Data Agents6 Minuten
5.3 Advanced Machine Learning for AI Data Agents3 Minuten
5.4 Selecting and Training Machine Learning Models for AI Data Agents4 Minuten
5.5 Advanced Machine Learning Models and Optimization for AI Data Agents4 Minuten

1 AufgabeInsgesamt 30 Minuten

Quiz 530 Minuten

1 Plug-inInsgesamt 5 Minuten

Activity: Accordion5 Minuten

Examines the ethical, security, and privacy considerations involved in developing and deploying AI data agents. Topics include responsible AI practices, data protection, access control, bias mitigation, transparency, compliance, and strategies for building trustworthy AI systems.

Das ist alles enthalten

5 Videos1 Aufgabe1 Plug-in

5 VideosInsgesamt 23 Minuten

6.1 Key Ethical Issues and Broader Ethical Principles in AI Data Agents6 Minuten
6.2 Fairness and Accountability in AI Data Agents5 Minuten
6.3 Risk of Bias and Ethical Frameworks in AI Data Agents4 Minuten
6.4 Security and Privacy Concerns in AI Data Agents4 Minuten
6.5 Legal, Regulatory, and Ethical Data Practices in AI Data Agents4 Minuten

1 AufgabeInsgesamt 30 Minuten

Quiz 630 Minuten

1 Plug-inInsgesamt 5 Minuten

Activity: Case Study5 Minuten

Provides hands-on experience in designing and implementing an AI data agent solution. Learners will apply concepts from previous modules to develop a project involving data collection, processing, AI model integration, evaluation, and presentation of an AI-driven solution to address a real-world problem.

Das ist alles enthalten

2 Videos1 Aufgabe

Dozent

AI CERTs Team

AI CERTs

62 Kurse63.586 Lernende

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AI CERTs

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Häufig gestellte Fragen

It is designed for data analysts, business analysts, reporting professionals, and learners interested in AI-powered analytics.

No. The course is beginner-friendly and focuses on practical AI applications in analytics.

You’ll learn how AI supports forecasting, reporting, visualization, predictive analytics, and business intelligence processes.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.