Courses AI certifications (AI CERTs®)

AI+ Data Practitioner™

  • Price 279 EUR + VAT
  • E-learning Start any time
  • Duration 40 hours of content, self-paced
  • Language English
  • Topic AI Data & Robotics

How you can take it

  • E-learning Start any time · 279 EUR + VAT Order
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Overview

What the course covers

The course, the materials and the exam are in English. The description below is the original AI CERTs® text.

  • Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
  • Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics
  • Capstone Application: Solve real-world problems like employee attrition with AI
  • Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship

Prerequisites

Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.

Audience

Who is it for

Data Analysts & Scientists: Enhance data analysis capabilities using AI for predictive modeling and decision-making.

Business Intelligence Professionals: Leverage AI to uncover insights, trends, and opportunities in complex data sets.

IT Specialists & System Integrators: Implement AI-powered solutions to optimize data management and infrastructure.

Data Engineers: Design and develop AI-driven data pipelines and architectures for scalable solutions.

Students & New Graduates: Build valuable AI and data science skills to thrive in an increasingly data-driven world.

In the price

What the price includes

  • Self-paced online course with the full learning material
  • The official AI CERTs® exam
  • Digital badge after passing the exam

Course content

Syllabus

Course Overview
  • Course Introduction Preview
Module 1: Foundations of Data Science
  • 1.1 Introduction to Data Science
  • 1.2 Data Science Life Cycle
  • 1.3 Applications of Data Science
Module 2: Foundations of Statistics
  • 2.1 Basic Concepts of Statistics
  • 2.2 Probability Theory
  • 2.3 Statistical Inference
Module 3: Data Sources and Types
  • 3.1 Types of Data
  • 3.2 Data Sources
  • 3.3 Data Storage Technologies
Module 4: Programming Skills for Data Science
  • 4.1 Introduction to Python for Data Science
  • 4.2 Introduction to R for Data Science
Module 5: Data Wrangling and Preprocessing
  • 5.1 Data Imputation Techniques
  • 5.2 Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
  • 6.1 Introduction to EDA
  • 6.2 Data Visualization
Module 7: Generative AI Tools for Deriving Insights
  • 7.1 Introduction to Generative AI Tools
  • 7.2 Applications of Generative AI
Module 8: Machine Learning
  • 8.1 Introduction to Supervised Learning Algorithms
  • 8.2 Introduction to Unsupervised Learning
  • 8.3 Different Algorithms for Clustering
  • 8.4 Association Rule Learning with Implementation
Module 9: Advance Machine Learning
  • 9.1 Ensemble Learning Techniques
  • 9.2 Dimensionality Reduction
  • 9.3 Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
  • 10.1 Introduction to Data-Driven Decision Making
  • 10.2 Open Source Tools for Data-Driven Decision Making
  • 10.3 Deriving Data-Driven Insights from Sales Dataset
Module 11: Data Storytelling
  • 11.1 Understanding the Power of Data Storytelling
  • 11.2 Identifying Use Cases and Business Relevance
  • 11.3 Crafting Compelling Narratives
  • 11.4 Visualizing Data for Impact
Module 12: Capstone Project - Employee Attrition Prediction
  • 12.1 Project Introduction and Problem Statement
  • 12.2 Data Collection and Preparation
  • 12.3 Data Analysis and Modeling
  • 12.4 Data Storytelling and Presentation
Optional Module: AI Agents for Data Analysis
  • 1. Understanding AI Agents
  • 2. Case Studies
  • 3. Hands-On Practice with AI Agents

Exam

About the exam

  • 50 Questions
  • 90 min Time limit
  • 70% Pass mark
  • Online proctored exam

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Private training for your team?

Any of our courses can be delivered as private, in-company training, tailored to your organisation, in English or Hungarian.

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