AI+ Data Practitioner™

Certificate Code: AT-120

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

Tagline: Formerly known as AI+ Data™<br> <br> Mastering AI, Maximizing Data: Your Path to Innovation

Course Overview:

Prerequisites:

Tools Used:

• Google Colab
• MLflow
• Alteryx
• KNIME

Modules:

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

What You’ll Learn:

• 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.

Career Opportunities:

• AI Data Scientist — Analyzes complex data to extract insights, builds predictive models, employs statistical methods, and communicates findings to influence decision-making.
• AI Machine Learning Engineer — Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.
• AI Engineer — Develops artificial intelligence solutions, programs neural networks, optimizes AI algorithms, ensures ethical AI deployment, and troubleshoots AI systems.
• AI Data Analyst — Interprets data, generates reports, identifies trends, supports business decisions with actionable insights, and utilizes visualization tools to present data.

Exam Blueprint:

• Foundations of Data Science – 5%
• Foundations of Statistics – 5%
• Data Sources and Types – 6%
• Programming Skills for Data Science – 10%
• Data Wrangling and Preprocessing – 10%
• Exploratory Data Analysis – 12%
• Generative AI Tools for Deriving Insights – 6%
• Machine Learning – 10%
• Advance Machine Learning – 10%
• Data-Driven Decision-Making – 10%
• Data Storytelling – 6%
• Capstone Project - Employee Attrition Prediction – 10%

Self-Study Materials:

• Videos: Engaging visual content to enhance understanding and learning experience.
• Podcasts: Insightful audio sessions featuring expert discussions and real-world cases.
• Audiobooks: Listen and learn anytime with convenient audio-based knowledge sharing.
• E-Books: Comprehensive digital guides offering in-depth knowledge and learning support.
• Labs: Interactive lab sessions to apply concepts and strengthen technical skills.
• Module Wise Quizzes: Interactive assessments to reinforce learning and test conceptual clarity.
• Additional Resources: Supplementary references and list of tools to deepen knowledge and practical application.

Frequently Asked Questions:

• Q: What are the key components of the AI+ Data Practitioner™ certification?
  A: The certification covers Data Science Foundations, Statistics, Programming, and Data Wrangling, along with advanced subjects such as Generative AI and Machine Learning.
• Q: How does this certification prepare participants for data challenges?
  A: The certification provides participants with the necessary tools and skills to handle complex data challenges, such as cleaning, transforming, and analyzing data.
• Q: What are the career opportunities after completing this certification?
  A: Graduates of the AI+ Data Practitioner™ certification program can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, AI Consultant, and other data-driven positions.
• Q: What skills will I gain from this certification?
  A: Participants will gain skills in data analysis, machine learning, data visualization, data wrangling, and predictive analytics, along with proficiency in Python and R.
• Q: Can I pursue this course while working full-time?
  A: Yes, the AI+ Data Practitioner™ certification is designed to be flexible and can be pursued while working full-time. The course materials are available online.

Certificate Features:

• High-Quality Video, E-book & Audiobook
• Modules Quizzes
• AI Mentor
• Access for Tablet & Phone
• Online Proctored Exam with One Free Retake
• Labs Practices