AI+ Data Practitioner™

• 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.

• Demand for Certified Experts:Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
• 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.
• 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.
• Career Advancement:As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.

Inhoud

1. 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
Tools you will explore
• 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

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