Training Courses

All Coursera courses can be enrolled and taken for free allowing you to see the lecture content and questions. However, you will not recieve a certificate after completion, or be able to get answers to the questions. Paying for a courses works in a monthly subscription manner, but you will need to subscribe to each course inidvidually. Subscribing does not allow you access to all courses, just the specialization you have selected!

Watch this video to learn how to access each course for free.

Course	Contents	Information
Learn SQL Basics for Data Science Specialization	Use SQL commands to filter, sort, & summarise data; manipulate strings, dates, & numerical data from different sources for analysis Use the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result Assess and create datasets to solve your business questions and problems using SQL Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations	Audit for Free Beginner Level 100% Online Approx. 4 Months (5 hours/week)
Python for Everybody Specialization	Install Python and write your first program Use variables to store, retrieve and calculate information Describe the basics of the Python programming language Utilise the core programming tools such as functions and loops	Audit for Free Beginner Level 100% Online Approx. 8 Months
Data Science: Foundations using R Specialization	Use R to clean, analyse, and visualise data Use GitHub to manage data science projects Learn how to ask the right questions, obtain data, and perform reproducible research Set up R, R-Studio, GitHub and other useful tools	Audit for Free Beginner Level 100% Online Approx. 5 Months (8 hours/week)
Mathematics for Machine Learning Specialization	Implement mathematical concepts using real-world data Understand how orthogonal projects work Derive PCA from a projection perspective Master PCA	Audit for Free Beginner Level 100% Online Approx. 4 Months (4 hours/week)
Machine Learning	Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI) Applying machine learning algorithms to case studies This is one of the original and most highly rated/credited Machine Learning courses going. The course uses Octave/Matlab and is being updated soon to Python!	Audit for Free Assumed Beginner Level 100% Online Approx. 61 Hours
Deep Learning Specialization	Build and train deep neural networks, identify key architecture parameters, implement vectorised neural networks and deep learning to applications Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data Train test sets, analyse variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering	Audit for Free Intermediate Level (Intermediate Python, Linear Algebra & ML Basics 100% Online Approx. 5 Months (9 hours/week)
Machine Learning Specialization	Newly rebuilt, more relevant than ever, and expanded into 3 courses, the updated Specialization teaches foundational AI concepts through an intuitive visual approach, before introducing the code need to implement the algorithms and the underlying math	Andrew Ng's updated ML course now in Python Releases June 2022 Join Waitlist
Statistics with Python Specialization	Create and interpret data visualizations using the Python programming language and associated packages & libraries Apply statistical modelling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques) Apply and interpret inferential procedures when analysing real data Understand importance of connecting research questions to data analysis methods	Audit for Free Beginner Level 100% Online Approx. 3 Months (5 hours/week)
Data Analysis with R Specialization	Analyse and visualise data Fit, examine, and utilise regression models to examine relationships between multiple variables Perform hypothesis tests, interpret statistical results (e.g., p-values), and report the results of your analysis to clients Install and use R and R-Studio	Audit for Free Beginner Level 100% Online Approx. 5 Months (2 hours/week)
Bayesian Data Analysis Course by Aki Vehtari	This course has been designed with a strong emphasis in computational aspects of Bayesian data analysis and using the latest computational tools	Audit for Free Beginner / Intermediate Levels 100% Online Approx. ~3 Months (~2 hours/week)
Statistical Rethinking Course by Richard McElreath	This course teaches data analysis with a focus on scientific models, i.e., conceptual, causal models and precise questions about those models Practical examples are offered on the use Bayesian data analysis to connect scientific models to evidence	Audit for Free Intermediate Level 100% Online Approx. ~2,5 Months (~1-2 hours/week)