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Home › Courses › AI+ Data Practitioner™

AI+ Data Practitioner™

  • 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
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AI+ Data Practitioner™
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Level
beginner

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Duration
40 hours (5 Days)

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Format
Self-Paced Online

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Certification
AI CERTs®

Self-Paced Online

USD $ 495.00

Instructor-Led Online

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Data & Robotics AI System Engineer AI Technical All Courses Business Intelligence Analyst Data Engineer Data Science & AI Specialist Data Scientist English Financial Analyst Language Machine Learning Scientist Statistician
Program Name
AI+ Data Practitioner™
Duration
  • Instructor‑Led: 5 Days
  • Self‑Paced: 40 hours of content
Prerequisites
    • 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.
Exam Format
50 questions, 70% passing, 90 Minutes

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.

Certification 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

Finish the course and get certified

certificate

Industry Opportunities

Opportunity Image

AI Data Scientist

Analyzes complex data to extract insights, builds predictive models, employs statistical methods, and communicates findings to influence decision-making.

Opportunity Image

AI Machine Learning Engineer

Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.

Opportunity Image

AI Engineer

Develops artificial intelligence solutions, programs neural networks, optimizes AI algorithms, ensures ethical AI deployment, and troubleshoots AI systems.

Opportunity Image

AI Data Analyst

Interprets data, generates reports, identifies trends, supports business decisions with actionable insights, and utilizes visualization tools to present data.

Frequently Asked Questions

What are the key components of the AI+ Data Practitioner™ certification?
The certification covers Data Science Foundations, Statistics, Programming, and Data Wrangling, along with advanced subjects such as Generative AI and Machine Learning.
How does this certification prepare participants for data challenges?
The certification provides participants with the necessary tools and skills to handle complex data challenges, such as cleaning, transforming, and analyzing data.
What are the career opportunities after completing this certification?
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.
What skills will I gain from this certification?
Participants will gain skills in data analysis, machine learning, data visualization, data wrangling, and predictive analytics, along with proficiency in Python and R.
Can I pursue this course while working full-time?
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.

Prerequisites

  • 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.

Exam Details

Duration

90 Minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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-Paced Online
USD $ 495.00
Purchase Self-Paced Course
Instructor-Led Online
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Core AI Tools Covered

Google Colab

Google Colab

MLflow

MLflow

Alteryx

Alteryx

KNIME

KNIME