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Mastering AI, Maximizing Data: Your Path to Innovation Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling Advanced Topics: Dive into Gener...
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Successfully delivered 49 sessions for over 91 professionals

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Skills You’ll Gain

  • Data Visualization Techniques
  • Data Quality and Bias Mitigation
  • Deep Learning for Data Processing
  • Statistical Modeling
  • Big Data Technologies
  • 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.
  • Basic knowledge of computer science and statistics (beneficial but not mandatory).
  • Keen interest in data analysis.
  • Willingness to learn programming languages such as Python and R.
50 questions, 70% passing, 90 minutes, online proctored exam

  1. Course Introduction

  1. 1.1 Introduction to Data Science
  2. 1.2 Data Science Life Cycle
  3. 1.3 Applications of Data Science

  1. 2.1 Basic Concepts of Statistics
  2. 2.2 Probability Theory
  3. 2.3 Statistical Inference

  1. 3.1 Types of Data
  2. 3.2 Data Sources
  3. 3.3 Data Storage Technologies

  1. 4.1 Introduction to Python for Data Science
  2. 4.2 Introduction to R for Data Science

  1. 5.1 Data Imputation Techniques
  2. 5.2 Handling Outliers and Data Transformation

  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization

  1. 7.1 Introduction to Generative AI Tools
  2. 7.2 Applications of Generative AI

  1. 8.1 Introduction to Supervised Learning Algorithms
  2. 8.2 Introduction to Unsupervised Learning
  3. 8.3 Different Algorithms for Clustering
  4. 8.4 Association Rule Learning with Implementation

  1. 9.1 Ensemble Learning Techniques
  2. 9.2 Dimensionality Reduction
  3. 9.3 Advanced Optimization Techniques

  1. 10.1 Introduction to Data-Driven Decision Making
  2. 10.2 Open Source Tools for Data-Driven Decision Making
  3. 10.3 Deriving Data-Driven Insights from Sales Dataset

  1. 11.1 Understanding the Power of Data Storytelling
  2. 11.2 Identifying Use Cases and Business Relevance
  3. 11.3 Crafting Compelling Narratives
  4. 11.4 Visualizing Data for Impact

  1. 12.1 Project Introduction and Problem Statement
  2. 12.2 Data Collection and Preparation
  3. 12.3 Data Analysis and Modeling
  4. 12.4 Data Storytelling and Presentation

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

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Email: felice.deangelis@proficegroup.it

Whatsapp: +39 393 390 9401

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