AI+ Data Practitioner™
- From 505 EUR + VAT
- Self-paced online Start anytime
- Duration 40 hours of content, self-paced
- Language English
- Topic AI Data & Robotics
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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