AI+ Data (AT-120 Self-Paced Training)
Ziele der Schulung
The AI+ Data™ certification is designed to equip professionals with advanced skills in data science and artificial intelligence, covering data science foundations, statistics, Python programming, data wrangling, generative AI, machine learning, and predictive analytics. It prepares learners to solve complex data challenges and gain practical experience through a capstone project emphasizing data-driven decision-making and compelling data storytelling. What Will You Learn? You’ll learn advanced data analysis techniques, how to employ generative AI and machine learning tools, strengthen programming proficiency in Python and R, and master data storytelling and decision-making.
Zielgruppe Seminar
- 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.
Voraussetzungen
- Basic understanding of computer science and statistics (beneficial but not mandatory)
- Keen interest in data analytics
- Willingness to learn programming languages such as Python and R
Seminarinhalt
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
Hinweise
Prüfung und Zertifizierung
- Duration: 90 minutes
- Passing Score: 70% (35/50)
- Format: 50 multiple-choice questions
- Delivery Method: Online via proctored exam platform
- Blueprint Includes: Foundations of Data Science, Statistics, Data Wrangling, EDA, Generative AI Tools, Machine Learning, Advanced Machine Learning, Decision-Making, Data Storytelling, Capstone Project.
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