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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 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

Price of the certification exam is included in the price of the course. Demand for Certified Experts:
Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
Mitigating Data and AI Risks:
Poor handling of data and AI technologies can lead to inaccurate analysis and business risks. This certification helps professionals mitigate such challenges.
Designing AI-Driven Data Strategies:
Certified professionals play a crucial role in designing AI-driven data strategies that optimize performance and align with regulatory standards.
Career Advancement:
As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.

Google Colab
MLflow
Alteryx
KNIME

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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

Cieľová skupina

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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