AI+ Data Practitioner™

Ártól 200 000 Ft + Áfa
Önálló online Azonnal kezdhető
Időtartam 40 óra tananyag, önálló tempóban
Nyelv angol
Terület AI Data & Robotics

Kinek szól
Mit tartalmaz az ár
Tematika
A vizsgáról

Áttekintés

Miről szól a képzés

A kurzus, a tananyag és a vizsga angol nyelvű. Az alábbi leírás az AI CERTs® eredeti szövege.

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

Előfeltételek

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

Célcsoport

Kinek szól

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.

Az árban

Mit tartalmaz az ár

Önálló tempójú online kurzus, a teljes tananyaggal
A hivatalos AI CERTs® vizsga
Digitális badge a sikeres vizsga után

Tananyag

Tematika

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

Vizsga

A vizsgáról

50 Kérdés
90 perc Időkeret
70% Ponthatár

Online proctored exam

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