AI+ Data Practitioner™

AI+ Data Practitioner™

USD$495.00 (GST excl.)

Formerly known as AI+ Data™

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
For more information, see below
Duration
Instructor-Led: 5 days (live or virtual) 
Self-Paced: 40 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam

Overview

  • 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

Prerequisites

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

What you’ll learn

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.

Why this certification matters

  • 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.

Tools you’ll use

Google Colab
Google Colab
MLflow
MLflow
Alteryx
Alteryx
KNIME
KNIME

Learning Modules

14 modules

1Course Overview
Course Introduction Preview
2Module 1: Foundations of Data Science
1.1 Introduction to Data Science
1.2 Data Science Life Cycle
1.3 Applications of Data Science
3Module 2: Foundations of Statistics
2.1 Basic Concepts of Statistics
2.2 Probability Theory
2.3 Statistical Inference
4Module 3: Data Sources and Types
3.1 Types of Data
3.2 Data Sources
3.3 Data Storage Technologies
5Module 4: Programming Skills for Data Science
4.1 Introduction to Python for Data Science
4.2 Introduction to R for Data Science
6Module 5: Data Wrangling and Preprocessing
5.1 Data Imputation Techniques
5.2 Handling Outliers and Data Transformation
7Module 6: Exploratory Data Analysis (EDA)
6.1 Introduction to EDA
6.2 Data Visualization
8Module 7: Generative AI Tools for Deriving Insights
7.1 Introduction to Generative AI Tools
7.2 Applications of Generative AI
9Module 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
10Module 9: Advance Machine Learning
9.1 Ensemble Learning Techniques
9.2 Dimensionality Reduction
9.3 Advanced Optimization Techniques
11Module 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
12Module 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
13Module 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
14Optional Module: AI Agents for Data Analysis
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Format

Online, self-paced
Duration

Instructor-Led: 5 days (live or virtual) 
Self-Paced: 40 hours of content
Exam

50 questions, 70% passing, 90 minutes, online proctored exam
Included

Instructor-led OR Self-paced course + Official exam + Digital badge
Prerequisites

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

Projects & case studies
Outcome

Industry-recognized credential + hands-on experience

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