Hurry! Save 10% on upcoming courses! Enrol before 12th June.
Hurry! Save 10% on upcoming courses! Enrol before 12th June.
Choose Your Start Date
Browse our upcoming live online classes. Download your brochure to learn more.
Starts 21 Jun — Ends 04 Oct 23
- Live online lectures with an industry expert, plus additional self-directed learning
- 16 weeks: One 1 hour lecture & 4 hours of self-study per week
- Wednesdays, 6:30pm - 7:30pm
Secure your place with a 10% deposit and pay the rest in instalments. Flexible payment options available.
Why UCD Professional Academy?
- Valuable, trusted qualification
- Industry expert lecturers
- Flexible learning options
€69,000 / Annual
Senior Data Scientist Salary in Ireland
The average data scientist salary in Ireland is €69,000 per year. Entry level positions start at €45,000 per year while more experienced workers make up to €75,000 per year.
Salary data source: indeed.com, 11th January 2023
Salary data source: indeed.com, 11th January 2023
Data Analytics Essentials Course Modules
With a blend of self-study on DataCamp’s world class learning platform, and live expert online mentoring, you’ll learn to write efficient Python code - you’ll work with challenging data, including date and time data, text data, and web data using APIs. You will know how to troubleshoot your code and use statistical and machine learning techniques to train decision trees and use natural language processing. Augment your skills with a toolbox to perform supervised, unsupervised, and deep learning.
Or browse our wide range of expert-led online courses and grow your career potential today.
1. Python Data Science Toolbox (Part 1)
Take your Python skills to the next level. Move on from functions and the library to writing your own functions and solving the specific problems your data throws up.
- Writing your own functions
- Arguments and scope
- Lambda functions and error-handling
2. Python Data Science Toolbox (Part 2)
Continue to build your Python data science skills. Learn about iterators and list comprehensions, working through a case study to apply all the techniques you have learned so far.
- Using iterators in PythonLand
- List comprehensions and generators
- Bringing it all together!
3. Cleaning Data In Python
You can only effectively analyse data if it has been properly cleaned. Learn to carry out this vital task as you identify, diagnose, and treat various data cleaning problems.
- Common data problems
- Text and categorical data problems
- Advanced data problems
- Record linkage
4. Regular Expressions In Python
Understand concepts of string manipulation and regular expressions. Learn how to split strings, join them back together, interpolate them, as well as detect, extract, replace, and match strings using regular expressions.
- Basic concepts of string manipulation
- Formatting strings
- Regular expressions for pattern matching
- Advanced regular expression concepts
5. Working With Dates & Times In Python
Learn how to ensure your analyses don’t get tripped up when dates and times are used, such as day and month boundaries, time zones, or daylight-saving time.
- Dates and calendars
- Combining dates and times
- Time zones and daylight saving
- Dates and times in pandas
6. Writing Functions In Python
Learn to go from exploratory scripts to writing complex and beautiful functions that are ready to deploy. Learn best practice around how to write maintainable reusable functions with good documentation.
- Best practice
- Context managers
- More on decorators
7. Exploratory Data Analysis In Python
Discover the tools you need to clean and validate data, to visualise distributions and relationships between variables, and to use regression models to predict and explain.
- Read, clean, and validate
- Multivariate thinking
8. Statistical Thinking In Python (Part 1)
Learn about the principles of statistical inference. Start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you.
- Graphical exploratory data analysis
- Quantitative exploratory data analysis
- Discrete variables
- Continuous variables
9. Statistical Thinking In Python (Part 2)
Expand and hone your hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing, working with real datasets as you learn.
- Parameter estimation by optimisation
- Bootstrap confidence intervals
- Introduction to hypothesis testing
- Hypothesis test examples
- Case study
10. Supervised Learning With Scikit-Learn
Use scikit-learn, one of the most popular and user-friendly machine learning libraries, to build predictive models, tune their parameters, and determine how well they will perform with unseen data.
- Fine-tuning your model
- Pre-processing and pipelines
11. Unsupervised Learning In Python
Extract insights from unlabelled datasets using scikit-learn and SciPy. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorisation.
- Clustering for dataset exploration
- Hierarchical clustering and t-SNE
- Decorrelating and dimension reduction
- Discovering interpretable features
12. Introduction To Deep Learning In Python
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 - the latest version of a cutting-edge library for deep learning in Python.
- Basics of deep learning and neural networks
- Optimising a neural network with backward propagation
- Building deep learning models with Keras
- Fine-tuning Keras models
13. Machine Learning With Tree-Based Models In Python
Learn how to use tree-based models and ensembles for regression and classification. Understand how to tune the most influential hyperparameters in order to get the most out of your models.
- Classification and regression trees
- The bias-variance trade-off
- Bagging and random forests
- Model tuning
14. Cluster Analysis In Python
Master unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library. Learn to apply various clustering algorithms on data, visualise the clusters formed and analyse results.
- Introduction to clustering
- Hierarchical clustering
- K-means clustering
- Clustering in real world
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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.Download Brochure
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Frequently Asked Questions
Is this course right for me?
You do not need any prior qualification in data analysis, but we do have the following entry requirements:
Basic Computer Skills (Mandatory),
Skills to Install Software from Online Websites (Mandatory),
Understanding of Data and Charts in Excel (Or any related Software to create Charts/graphics)
Exposure to any programming language (Mandatory),
If you don’t have any experience in coding, you may need to spend extra time on self-study. You’re encouraged to discuss this with one of our Education Consultants before booking your place on the course.
You will need a computer capable of running a development environment: a modern 64-bit CPU, with at least 4GB of RAM and 5GB of available storage running a recent or preferably the latest version of Windows, Linux or macOS. If you use a company computer, you may need to check with your IT department to ensure you have access and permissions to install the required software.
Data analytics is an exciting career; your role isn’t simply to organise but to question, predict, and strategise.
How will this course help with my career?
According to LinkedIn, data scientist jobs have seen a 37% hiring growth over the last three years. Data analytics is a highly sought-after skill in the public and private sectors. You will find opportunities in every industry - global tech companies, banking, healthcare, retail, etc.
Combine your critical thinking skills with management and leadership skills and there’s no limit to how high you could potentially climb.
What is the online learning experience like?
Our online experience is designed to be just as interactive, supportive, and inspiring as the UCD Professional Academy campus experience.
Online courses can be accessed from any computer or laptop with an internet connection.
The DataCamp platform, where you will transform your data skills is trusted by over 6 million learners globally. All of your learning happens in-browser, so there’s no need for installation of heavy and costly software programmes.
Your learning is supported by weekly live expert mentor sessions, lectures are 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.
Explain the UCD Professional Academy and DataCamp collaboration to me?
DataCamp is a world class learning platform for building data skills online. Unlike other courses, which require you to download bulky and costly software everything happens via your browser, meaning you’ll have access to the very tools to master python skills anytime, anywhere. Check out Datacamp's case study on UCD Professional Academy collaboration.
A weekly one hour mentor session with a data analytics industry expert supports your learning. This helps you take your knowledge and apply it to real world business scenarios to give you practical, applicable skills. Our mentors have first hand knowledge of how to build and develop a career in this exciting field.
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?
There is no final exam. You will be assessed based on your submission of a report-based project, which will evaluate your understanding of the concepts you have learned. Your project will analyse an open source data set – you will retrieve, manipulate, and process a data set, working with it through the various stages of the course.
What are the benefits of a Specialist Certificate?
UCD Professional Academy Specialist Certificates and awards are extended courses 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. Specialist Certificates 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 Awards & Governance please visit https://www.ucd.ie/professionalacademy/governance/
How do I get my Professional Academy Certificate?
Your UCD Professional Academy Certificate 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 qualification to your LinkedIn profile.
What payment options are available?
A place on any of our programmes can be secured with a 10% deposit.
For full-time (boot camp) programmes, the balance must be paid in full before course commencement.
For part-time programmes students will be required to pay 40% of the fees before the start of the course, with the remaining balance due within 30 days of the course commencing.
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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- Weekly Live tutorial classes
- Access to DataCamp's learning platform
- Complete in 16 Weeks