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AI+ Data Practitioner™

Kód kurzu: NPAT120

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Formerly known as AI+ Data™

Mastering AI, Maximizing Data: Your Path to Innovation

  • Core Knowledge Areas: Data Science foundations, Python programming, Statistics, and Data Wrangling techniques
  • Advanced Subject Exploration: Dive into Generative AI, Machine Learning frameworks, and Predictive Analytics methodologies
  • Capstone Application:Solve real-world challenges like employee attrition analysis using AI-driven approaches
  • Career Readiness: Build competencies for AI-powered data science roles through hands-on learning and mentorship support
75 EUR

92 EUR s DPH

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+420 731 175 867 edu@edutrainings.cz

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Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Garantovaný

Forma: Self-Paced

Dĺžka kurzu: 40 hodín

Jazyk: en

Cena bez DPH: 285 EUR

Registrovať

Počiatočný dátum: Na vyžiadanie

Garantovaný

Forma: Self-Paced

Dĺžka kurzu: 40 hodín

Jazyk: en

Cena bez DPH: 75 EUR

Registrovať

Počiatočný
dátum		 Miesto
konania		 Forma Dĺžka
kurzu		 Jazyk Cena bez DPH
G Na vyžiadanie Self-Paced 40 hodín en 285 EUR Registrovať
G Na vyžiadanie Self-Paced 40 hodín en 75 EUR Registrovať
G Garantovaný kurz

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Popis kurzu

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Advanced Data Analysis Techniques

Learners will acquire skills in managing, preprocessing, and analyzing data using statistical methods and exploratory techniques to uncover insights and patterns.

Programming and Machine Learning Proficiency

Students will develop strong programming skills necessary for data science, along with foundational and advanced machine learning techniques to build predictive models.

Application of Generative AI and Machine Learning

Learners will learn to employ generative AI tools and machine learning algorithms to derive deeper insights from data, enhancing their analytical capabilities.

Data-Driven Decision Making and Storytelling

Students who goes through this course will get the ability to make informed decisions based on data analysis and effectively communicate findings through compelling data storytelling.

Štruktúra kurzu

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Course Overview

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

Predpokladané znalosti

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  • Basic knowledge of computer science and statistics is beneficial but not mandatory
  • Keen interest in data analysis and problem-solving
  • Willingness to learn programming languages such as Python and R

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pruduktová podpora

+420 731 175 867

edu@edutrainings.cz