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AT-120
AI+ Data Practitioner™
Formerly known as AI+ Data™
Mastering AI, Maximizing Data: Your Path to Innovation
Mastering AI, Maximizing Data: Your Path to Innovation
AI Data & RoboticsAI System EngineerAI Technical
Enquire about this certificationAt a glance
What is included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Recommended background
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Examination
50 questions, 70% passing, 90 minutes, online proctored exam
About this certification
- 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
Curriculum
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
Who should enrol
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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