Learning Platforms

Coursera

Coursera believes that anyone, anywhere has the power to transform their lives through learning.

University-backed courses and professional certificates. Strong mix of theory and hands-on application. Ideal for building credibility.

Google Data Analytics Professional Certificate: Top rated for beginners
Excel Skills for Business, Essentials: Practical, Excel focused
Data Analysis with Python: Python beginner course
Data Visualisation with Tableau: Clean Tableau training
Introduction to Data Analytics: High-level intro, good orientation
SQL for Data Science: Simple, visual, and powerful

Data Analyst

IBM Data Engineering Certificate: Cloud, pipelines, ETL
Google Cloud Data Engineer Certificate Prep: Official GCP-backed certification
Data Warehousing for BI: Concept-heavy, strong for architecture
ETL and Data Pipelines with Shell, Airflow & Kafka: DevOps meets data
Managing Big Data with MySQL: Scalability and real database work
NoSQL Systems: Explore MongoDB and Cassandra

Data Engineer

Machine Learning: Gold standard, broad exposure
Deep Learning Specialisation: Includes CNNs, RNNs, and NLP
Applied Data Science with Python: Includes matplotlib, seaborn
AI For Everyone: Strategy-oriented, business context
Mathematics for Machine Learning: Great linear algebra & stats intro
Statistics with R Specialisation: Stats-heavy with R-based workflows

Data Scientist

Preparing for Google Cloud Cert: Feature Engineering, MLOps
Machine Learning in Production: MLOps, App Deployment
IBM AI Engineering: Prompt Engineering, Large Language Modelling
Microsoft AI & ML Engineering: Build, Deploy & Innovate
IBM Machine Learning: Practical Skills & Knowledge
Developing ML Solutions: Predictive modelling, cloud development

ML Engineer

Udemy

Udemy believes that skills are the key to unlocking potential, where anyone can adapt to change and thrive.

Affordable, high volume learning. Excellent for practical upskilling with lifetime access. Great value if you know what you’re looking for.

Microsoft Excel: Excel from Beginner to Advanced.
Tableau Ultimate Course: Zero to Hero.
The Complete SQL Bootcamp: SQLAlchemy, PostgreSQL focus.
Business Intelligence Analyst Course: SQL, Tableau, Power BI in one.
Power BI A-Z: Hands-On Power BI Training.
Data Analysis with Python and Pandas: Analyse data with Python's library.

Data Analyst

The Ultimate Hands-On Hadoop: Tame Big Data.
Apache Airflow: Learn to author, schedule and monitor data pipelines.
Build ETL Pipelines with Python: Write ETL pipelines using Python.
AWS Certified Data Engineer: Associate (DE-A01) Prep.
Kafka for Beginners: Deep dive in to Apache Kafka concepts.
Python for Data Engineering: Learn Python Programming Fundamentals.

Data Engineer

Python for Data Science and ML Bootcamp: Learn libraries, ML, and more.
Machine Learning A-Z: Hands-On Python & R.
Deep Learning with TensorFlow: Learn to use Python for Deep Learning.
Statistics for Data Science: Statistics you need in the office.
NLP with Transformers in Python: Learn next-generation NLP.
Data Science Mastery: Comprehensive ML and Data Science Projects.

Data Scientist

The AI Engineer Bootcamp: Python, Tranformers, real use cases.
AWS ML Engineer Associate hands-on: Prepare confidently.
Complete MLOps Bootcamp: Build scalable MLOps pipelines.
Mathematical Foundations of ML: Linear Algebra and Calculus.
Machine Learning A-Z: Master ML on Python and R.
LLM Engineering: Generative AI, RAG, LoRA and AI Agents.

ML Engineer

Datacamp

Datacamp's mission is to democratise data and AI skills for everyone, close the skill gaps grasp the future

Built specifically for data careers. Interactive, browser based coding with structured tracks. Great for beginners and intermediate learners.

Data Analyst with Python: Learn Python, pandas, and data visualisation.
Analysing Data with pandas: Manipulate and explore data using Pandas library.
Data Visualisation in Python: Build clear charts with Matplotlib and Seaborn.
Data Cleaning in Python: Fix messy data with cleaning techniques in pandas.
Joining Data in SQL: Master SQL joins for combining and analysing relational data.
Excel for Data Analysis: Take Excel skills further with pivots, lookups, and logic.

Data Analyst

Data Engineer with Python: Build pipelines, manage datasets, and master Python.
Intro to Airflow in Python: Automate and schedule data workflows.
Data Engineering for Everyone: High-level overview of tools and pipelines.
Streaming Data with Kafka in Python: Process data streams using Kafka.
Managing Data with SQL: Handle large-scale data using SQL.
ETL Pipelines with Python: Create ETL pipelines for transforming raw data.

Data Engineer

Data Scientce with Python: Master Python, stats, and ML to solve data problems.
Supervised Learning with scikit-learn: Build and evaluate predictive models.
Unsupervised Learning in Python: Reduce dimensions to uncover data patterns.
Introduction to Machine Learning: ML foundations: models and workflows.
Feature Engineering for ML: Transform data into features for model performance.
Bayesian Statistics in Python: Use probabilistic thinking to model uncertainty.

Data Scientist

ML Engineer

Understanding Machine Learning: Introduction to ML.
Preprocessing for ML in Python: Clean structured data for ML.
ML For Business: ML Applications in the business world.
Feature Engineering for ML: Design Features and improve performance.
End-to-End Machine Learning: Design, Train & Deploy Models.
Monitoring ML Models: Learn how to monitor models for data & concept drift.

Codecademy

Codecademy want to create a world where anyone can build something meaningful with technology

Hands-on, step-by-step learning in your browser. More interactive than video, less academic. Suited to self-paced, practical learners.

Data Analysis, Python: Data analysis skills in SQL and Python.
Learn SQL: Master querying databases and manipulating data.
Learn Python for Data Analysis: Explore, analyse, and summarise data.
Data Visualisation with Matplotlib: Turn raw data into clean, clear charts.
Statistics with Python: Understand statistical concepts to make data decisions.
Data Cleaning in Python: Fix messy data for analysis with practical Python tools.

Data Analyst

Data Engineer, Python & ETL: Build the foundations of data engineering.
Learn the Command Line: Navigate files, run scripts, and automate tasks.
Learn Intermediate SQL: Query complex datasets and build efficient SQL.
Data Processing with Pandas: Use Python and pandas to clean large datasets.
Learn Git & GitHub: Collaborate on code, track changes, and manage projects.
Data Architecture Concepts: How systems are structured, scaled, and secured.

Data Engineer

Data Scientist, Python: Master Python and ML to solve complex problems.
Learn Python 3: Build a solid coding foundation for all data science work.
ML Fundamentals: Core ML concepts like training, testing, and evaluation.
Natural Language Processing: Analyse and extract insights from text data.
Data Ethics and Bias: Learn how to responsibly collect, use, and model data.
Decision Trees and Random Forests: Build powerful models using algorithms.

Data Scientist

ML Engineer

Machine Learning Career Path: Become an ML Specialist.
ML & AI Engineering: Data & Programming Foundations.
Intro to ML with Regression: Build & implement linear regression models.
Build an ML Model: Learn to build ML models with Python.
Build Deep Learning Models: Use TensorFlow to build and tune ML Models.
ML Perceptrons: Level up your ML Skills.