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

Instructeur : AI CERTs Team

1 522 déjà inscrits

Inclus avec En savoir plus

7 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

7 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

7 devoirs

Enseigné en Anglais

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Il y a 7 modules dans ce cours

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.

Inclus

6 vidéos1 devoir1 plugin

6 vidéosTotal 31 minutes

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

1 devoirTotal 30 minutes

Quiz 130 minutes

1 pluginTotal 5 minutes

Activity: Hotspot5 minutes

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.

Inclus

5 vidéos1 devoir1 plugin

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.

Inclus

6 vidéos1 devoir1 plugin

6 vidéosTotal 35 minutes

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

1 devoirTotal 30 minutes

Quiz 330 minutes

1 pluginTotal 5 minutes

Activity: Problem Statement5 minutes

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.

Inclus

6 vidéos1 devoir1 plugin

6 vidéosTotal 36 minutes

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

1 devoirTotal 30 minutes

Quiz 430 minutes

1 pluginTotal 5 minutes

Activity: Drag-and-Drop5 minutes

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.

Inclus

5 vidéos1 devoir1 plugin

5 vidéosTotal 22 minutes

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

1 devoirTotal 30 minutes

Quiz 530 minutes

1 pluginTotal 5 minutes

Activity: Accordion5 minutes

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.

Inclus

5 vidéos1 devoir1 plugin

5 vidéosTotal 23 minutes

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

1 devoirTotal 30 minutes

Quiz 630 minutes

1 pluginTotal 5 minutes

Activity: Case Study5 minutes

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.

Inclus

2 vidéos1 devoir

Instructeur

AI CERTs Team

AI CERTs

62 Cours63 810 apprenants

Offert par

AI CERTs

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Foire Aux Questions

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.