AI+ Data Practitioner™
Formerly known as AI+ Data™
Mastering AI, Maximizing Data: Your Path to Innovation
- 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
Duur: 5 dag(en)
- 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.
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Demand for Certified Experts:
Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
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Mitigating Data and AI Risks:
Poor handling of data and AI technologies can lead to inaccurate analysis and business risks. This certification helps professionals mitigate such challenges.
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Designing AI-Driven Data Strategies:
Certified professionals play a crucial role in designing AI-driven data strategies that optimize performance and align with regulatory standards.
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Career Advancement:
As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.
Inhoud
- Course Introduction Preview
- 1.1 Introduction to Data Science
- 1.2 Data Science Life Cycle
- 1.3 Applications of Data Science
- 2.1 Basic Concepts of Statistics
- 2.2 Probability Theory
- 2.3 Statistical Inference
- 3.1 Types of Data
- 3.2 Data Sources
- 3.3 Data Storage Technologies
- 4.1 Introduction to Python for Data Science
- 4.2 Introduction to R for Data Science
- 5.1 Data Imputation Techniques
- 5.2 Handling Outliers and Data Transformation
- 6.1 Introduction to EDA
- 6.2 Data Visualization
- 7.1 Introduction to Generative AI Tools
- 7.2 Applications of Generative AI
- 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
- 9.1 Ensemble Learning Techniques
- 9.2 Dimensionality Reduction
- 9.3 Advanced Optimization Techniques
- 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
- 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
- 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
- Google Colab
- MLflow
- Alteryx
- KNIME
Lesmethode
Instructor-led OR Self-paced course + Official exam + Digital badge
Kenmerken
Online proctored exam included, with one free retake.
Exam format:
50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Voorkennis
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Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.

