Early Bird: Save 10% when you enrol before 30th April.

Early Bird: Save 10% when you enrol before 30th April.

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Professional Academy Certificate in Data Analytics: Machine Learning

Live Expert-Led Learning Next Course Starts Jul 17th from €1,350

Continue your data science journey with this exciting course that will empower you to create your own machine learning solutions using Python. Discover supervised and unsupervised learning, neural networks, and ensemble methods as you learn to implement machine learning techniques against real-world datasets and present your results and insights. Position yourself to succeed in data-focused roles that involve creating, implementing, and evaluating machine learning models.

  • Bundle & Save. Book 2 or more courses and save 15%.

Hurry! Early Bird 10% off. Enrol before 30th April. Download the brochure now to reveal your discount code.

Choose Your Start Date

Browse our upcoming live online classes. Download your brochure to learn more.

Starts 17 Jul — Ends 04 Sep 24

Part-time / evening
  • LIVE ONLINE lectures with an Industry Expert
  • 8 weeks: One 1 hour lecture per week
  • Wednesdays, 6:30pm - 7:30pm
  • €1500, €1,350 with discount code

Secure your place with a 50% deposit and the rest after your course commences. Save an additional 5% when you pay in full at time of booking.

Why UCD Professional Academy?

  • Valuable, trusted certification
  • Industry expert lecturers
  • Flexible learning options

Data Analytics with Machine Learning Course Modules

This interactive and hands-on course takes your data analysis skills to the next level, giving you a strong understanding of machine learning concepts, including neural networks. You will implement machine learning techniques using Python and the scikit-learn library, covering concepts such as data pre-processing, model selection, evaluation, and hyperparameter tuning. Using DataCamp’s world-class learning platform, you will create advanced visualisations of data employing tools like Bokeh, matplotlib, and seaborn to effectively communicate your insights to stakeholders. You will apply the CRISP-DM methodology to real-world datasets, including problem formulation, data preparation, model building, evaluation, and deployment. This course will inspire you to use your creativity and innovative thinking to unlock the full potential of data-driven decision making.

1. Introduction To Machine Learning

Learn what machine learning is, the types of algorithms it uses, and what a machine learning workflow looks like. Discover the scikit-learn library.

  • Definition and applications
  • Types of algorithms
  • The machine learning workflow
  • Introduction to scikit-learn library

2. Advanced Statistical Learning

Look at advanced statistical learning - data visualisation, hypothesis testing, correlation, and regression analysis.

  • Interactive data visualisation with Bokeh
  • Statistical hypothesis testing
  • A/B testing
  • Correlation and regression analysis

3. Supervised Learning

Understand supervised learning - where the training data provided works as the supervisor that teaches the machine to predict the output correctly - and the different types of supervised learning: regression and classification.

  • Linear regression
  • Classification algorithms: logistic regression and KNN
  • Model selection and evaluation

4. Unsupervised Learning

Understand unsupervised learning – where the model itself finds the hidden patterns and insights from the given data – and the types of supervised learning: clustering and association.

  • Understanding unsupervised learning
  • Clustering algorithms: K-means and hierarchical clustering
  • Dimensionality reduction: PCA and t-SNE

5. Model Evaluation & Hyperparameter Tuning

Learn to evaluate and tune the hyperparameters of a model – i.e., the parameters that are explicitly defined by the user to control the learning process – so that you can improve the learning of the model. Explore model selection, evaluation, and optimisation.

  • Cross-validation and holdout method
  • Bias-variance trade-off
  • Hyperparameter tuning with grid search and randomised search
  • Feature selection and engineering

6. Ensemble Methods

Learn how to build and evaluate ensemble models, which can help to improve the accuracy and robustness of machine learning models.

  • Combining models: voting, bagging, and boosting
  • Random forests
  • Gradient boosting machines
  • Stacking and blending

7. Neural Networks

Look at artificial neural networks (ANNs) and their components (including activation functions and loss functions), as well as how to train them using backpropagation. Explore different types of ANNs.

  • Artificial Neural Networks (ANNs)
  • Activation functions and loss functions
  • Backpropagation and training ANNs
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

8. Final Project

Using a dataset of your choosing, consolidate your understanding of the course materials and demonstrate your ability to apply machine learning techniques to solve a practical problem.

  • Apply CRISP-DM methodology to a real-world dataset
  • Implementing the machine learning techniques learned
  • Presenting results and insights
  • Tips

€49,000 / Annual

Data Analyst Salary in Ireland

The average Data Analyst salary in Ireland is €49,000 per year. Entry level positions start at €35,000 per year while more experienced developers make up to €56,000 per year.

Salary data source: indeed.com, 11th January 2023


Salary data source: indeed.com, 11th January 2023


Access to thousands of journals, articles and papers. Free of charge.

Students taking part in this course will now have access to the EBSCO Online Library, free of charge, for the full duration of the course. Here you can browse thousands of relevant journals, articles and other reliable academic and commercial texts like the Harvard Business Review, Bloomberg Businessweek and Forbes Magazine, to supplement your learning and assignments.

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Frequently Asked Questions

Is this course right for me?

This course is ideal for anyone who has already built a skillset importing, cleaning, joining, and manipulating datasets, for example by completing our Professional Academy Certificate in Data Analytics: Visualisation. A knowledge of Python is essential, as is previous experience working with packages such as pandas and NumPy for data import, cleaning, manipulation, and visualisation. You may be a data analyst, data scientist, business analyst, or other professional who regularly works with data and wants to improve their ability to create predictive models and extract insights from data. The course does not cover other programming languages such as R or Julia, or advanced topics in NLP or CV, big data processing, or distributed computing frameworks like Apache Spark. There is a large self-guided portion to this course – you will be expected to attend eight hours of live online lectures and complete 32 hours of online learning on the DataCamp platform (see below), plus time for exercises and assignments.

How will this course help with my career?

This course will help you advance your career by demonstrating your practical knowledge of machine learning algorithms, experience in using machine learning libraries such as scikit-learn and TensorFlow, and proficiency using Python for data analysis and machine learning applications. These skills are transferable across diverse data-centric roles, making you a competitive candidate for positions ranging from data analyst to machine learning engineer. Data science skills such as the ones learnt on this course can be leveraged by professionals from fields as diverse as human resources, finance, sales, manufacturing, healthcare, marketing, and education. Data analytics and machine learning techniques help you make better decisions and improve outcomes in any role.

What is the online learning experience like?

Outside of the online tutorial sessions, you will need to complete some self-study work using the DataCamp platform. This platform, where you will transform your data skills, is trusted by over six million learners globally. All your learning happens in-browser, so there’s no need to install heavy or costly software programmes.

Your learning is supported by weekly live expert mentor sessions, delivered using Zoom. During these classes, your teachers will use technology interactively to ensure an engaging learning experience. When appropriate, students will be encouraged to activate their microphones so that they can ask questions and communicate with other students.

What is the student experience like?

Student care is a high priority at UCD Professional Academy, which is why our Student Services team is on hand to support you throughout your time with us. They will respond to any queries you have, help you with any technical issues, and facilitate your learning experience at every point. All students are given access to our Student Portal, where you can see your timetable, access all your study materials, and manage your account.

How is this course assessed?

Your progress through the course will be marked by milestones that are reviewed and evaluated by the instructor, who will provide you with feedback. Your formal, summative assessment is through a final project, with supporting documentation, which is the application of machine learning models to a real-world dataset. You can use any dataset that is relevant to your work or a personal interest, as long as it does not contain personal information. The final project should demonstrate your ability to apply the concepts and techniques learned in the course and to communicate the results and insights effectively.

What are the benefits of a Professional Academy Diploma?

UCD Professional Academy Diplomas and Certificates are designed to give your career an advantage. Developed in conjunction with industry thought leaders our courses teach practical, applied skills to support you to achieve your career and business goals. Professional Academy Diplomas are suitable for career minded learners wishing to advance their professional skills and prospects rather than their academic credentials.

The Professional Academy is an independent wholly owned part of UCD designed to address the need for skills development in the workforce. Courses tend to be short, designed and delivered by industry practitioners, and are not part of nor do they lead to a traditional University award such as a degree or a masters. They are widely accepted by employers and many students are sponsored to study by their organisation.

For full details of UCD Professional Academy’s Certification & Governance please visit https://www.ucd.ie/professionalacademy/governance/

How do I get my Professional Academy Diploma?

Your UCD Professional Academy Diploma will be issued electronically on a secure platform, with a link that you can share with employers and others wishing to verify your credentials. You can also add this certification to your LinkedIn profile.

This Professional Academy Diploma is not on the National Framework of Qualifications as a credit-bearing course. This is not unusual for professional courses of this nature, where the need is for fresh and actionable skills immediately applicable in the workplace.

What payment options are available?

A place on any of our part-time & on demand courses can be secured with a 50% deposit with the remaining balance due within 30 days of the course commencing.

For full-time Bootcamp courses, 100% of the course fee should be paid before starting.

Please note that standard terms and conditions apply, which you can review here: https://www.ucd.ie/professionalacademy/terms-and-conditions/

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Live Classes

  • Weekly live online classes delivered via Zoom
  • Access to DataCamp's learning platform
  • Complete in 8 weeks
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