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AI Data & Robotics

AI+ Data™

  • 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

All prices are in NZD, ex GST (15%).

At a Glance: Course + Exam Overview

Category AI Data & Robotics
AI Technical
Program Name: AI+ Data™
Exam Format 50 questions, 70% passing, 90 Minutes Duration:
  • Instructor-Led: 5 Days
  • Self-Paced: 40 hours of content

🤝 You’re never on your own — Parasol Concierge Support provides integration guidance and expert VA assistance so you can focus on mastering skills while we handle the setup details.

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

  1. Course Introduction Preview

  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

Industry 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.

FREQUENTLY ASKED QUESTIONS

The certification covers Data Science Foundations, Statistics, Programming, and Data Wrangling, along with advanced subjects such as Generative AI and Machine Learning.

The certification provides participants with the necessary tools and skills to handle complex data challenges, such as cleaning, transforming, and analyzing data.

Graduates of the AI+ Data™ certification program can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, AI Consultant, and other data-driven positions.

Participants will gain skills in data analysis, machine learning, data visualization, data wrangling, and predictive analytics, along with proficiency in Python and R.

Yes, the AI+ Data™ 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%

Instructor-Led (Live Virtual/Classroom)

  • 5 days of intensive training with live demos
  • Real-time Q&A, peer collaboration, and hands-on labs
  • Led by AI Certified Trainers and delivered through Authorized Training Partners

Instructor-Led Course

🚀 Learn directly from certified AI trainers in live, interactive sessions. Get real-time guidance, practical exercises, and the accountability of a structured classroom experience. Perfect for professionals who want expert support and a clear path to certification.

✨ At Parasol Virtual, we go beyond certification. Our team supports your learning journey with guidance, resources, and local assistance to ensure your success. You’re not learning alone — you’re part of a global community with local support.

Request Virtual Training

Self-Paced Online

  • ~30 hours of on-demand video lessons, e-book, podcasts, and interactive labs
  • Learn anywhere, anytime, with modular quizzes to track progress

Self-Paced Course

🌐 Study anytime, anywhere with 24/7 access to course materials, practice tests, and resources. Move at your own speed while still working towards globally recognized certification. Designed for busy learners who value flexibility without compromising quality.

✨ At Parasol Virtual, we go beyond certification. Our team supports your learning journey with guidance, resources, and local assistance to ensure your success. You’re not learning alone — you’re part of a global community with local support.

Purchase Self-Paced Course

TECHNOLOGIES USED

Google Colab
Google Colab
MLflow
MLflow
Alteryx
Alteryx
KNIME
KNIME