Course 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

Certification overview:

Included Items: Instructor-led OR Self-paced course + Official exam + Digital badge

Certificate Duration:

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

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

Exam Format: 50 questions, 70% passing, 90 minutes, online proctored exam

Why this certification matters:

Demand for Certified Experts: Organisations 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 optimise 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.

Who should enrol:

Data Analysts & Scientists: Enhance data analysis capabilities using AI for predictive modelling 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 optimise 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.

Tools You'll Master

Google Colab

MLflow

Alteryx

KNIME

Certification Modules

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 Visualisation

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: Advanced Machine Learning

9.1 Ensemble Learning Techniques

9.2 Dimensionality Reduction

9.3 Advanced Optimisation 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 Visualising 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 Modelling

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

Frequently Asked Questions

What core industry problems does the AI+ Data Self-Paced Learning V3 training solve?

This course is engineered to help you solve real-world operational inefficiencies by integrating advanced machine learning tools into your daily workflow. You will learn to automate tedious data processing, construct predictive models, and optimize systemic outputs specific to this domain. By learning these practical automation skills, you immediately boost your daily professional output and elevate your long-term career value.

Are the tools and software platforms taught in this course accessible to beginners?

The curriculum is structured logically to support clean career progression, moving from essential concepts to advanced configurations. While technical tracks introduce deeper code integrations, our industry-specific modules utilize user-friendly enterprise interfaces and pre-trained prompt systems. This balanced approach ensures you can comfortably master the curriculum, acquire highly sought-after skills, and earn your digital record badge without frustration.

How will completing this self-paced course help upgrade my professional CV?

Completing this training demonstrates to modern employers that you possess practical, forward-looking expertise in artificial intelligence integration. Your profile will stand out with specialized technical capabilities, showing you can actively deploy automation, reduce operational costs, and manage digital workflows. This enables you to confidently pursue high-tier promotions, transition into modern tech roles, and take your career to the absolute next level.

AI+ Data Practitioner™

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£149.00

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