Data Science Bootcamp
This 18-week Bootcamp teaches you the skills needed to deliver data science projects effectively. You will learn the core skills and concepts in data science, whilst advancing to topics like deep learning and natural language processing, to enable you to meet modern business needs. Every week, there will be two evening sessions that blend lecture and labs to ensure you get practical experience that will help you become a successful Data Scientist.
Apply your skills and knowledge with organisations including Dolmen Design, Epic Museum and Met Eireann on the Action Project. This is where you, your team and the project partner will build actionable solutions to data challenges over 6 weeks, gathering insights and visualising the results to provide tangible business value.
Who is the Bootcamp for?
This unique accelerated Bootcamp covers the fundamentals that everyone will need in order to be successful in data science – data mining, data visualisation, data exploration, machine learning, AI and work practices. You will work on real datasets, applying the latest techniques for arriving at conclusions and making predictions.
The demand for data science skills has never been higher. Increasingly, Software Engineers, Web Developers and Product Managers are embedding analytics and data mining capabilities within their deployments.
This Bootcamp is ideal for people looking to acquire the data science skills needed to stay ahead of the game – typically IT or finance professionals, Risk Analysts, Accountants, Business & Data Analysts, Researchers and Academics seeking to learn more, upskill and gain a competitive advantage from their data.
Info & Cost
By the end of the 18 weeks, you will be:
- Comfortable with a robust data science process and be able to implement the process into your own projects.
- Able to analyse data using popular platforms (R, Power BI) and produce quality reports and conclusions.
- Benefit from practical industry-led workshops that cover the most popular technologies and platforms (R, Power BI) for data science and their application.
- Well-versed in multiple models/algorithms that can be applied to make predictions and be able to identify and apply the right ones for different data science challenges.
- Knowledgeable about techniques for working with and making predictions based on non-tabular data.
- Understand deep learning and natural language processing topics and their applications.
- Apply learning to your own dataset and other predefined dataset challenges.
- Aware of further resources for continued self-learning.
By exploring your data, you will uncover hidden trends and insights. Our faculty are skilled practitioners in R, the most prolific programming language for data science applications, and they will upskill you in bespoke packages for outlier detection, duplicate removal, generating frequency tables and more. Getting to know your data doesn’t require in-depth programming skills – R is straightforward and we will teach you the basics before the course starts, so that you can hit the ground running and build impeccable models by the end.
Increasingly, ML is being employed by engineers in chatbots, automated vehicles, image detection, personal assistants and a plethora of finance and marketing avenues. Software engineers and data analysts are bringing machine learning and artificial intelligence solutions to bear, automating the analysis process. Our course will teach you the core statistical principles and algorithms for ML, up to and including the basics of constructing convolutional neural networks (CNNs).
There is an increasing need to extract value from data investments and data visualisation enables you to present your new knowledge clearly and persuasively. Participants will learn about new tools and techniques that deliver state-of-the-art data visualisations quickly and easily. You will learn from best practice and develop a workflow that takes you from raw data to impressive visuals using the latest tools including Charticulator and Data Illustrator.
DATA ENGINEERING AND MACHINE LEARNING OPERATIONS
Learn how to build and deliver end-to-end data and machine learning pipelines that take dirty or distributed data, consolidate it, build relevant features, train and retrain models, and publish for use in applications. The exploratory data analysis and machine learning workflows built throughout the course will be turned into scalable solutions that can fit into a production software environment. In practice, this means you will be able to lead and manage your own data science projects.
Meet the Faculty
Steph is one of only 58 individuals in the world to be recognised with Microsoft’s Artificial Intelligence Most Valued Professional award and in 2019 she was also named the ‘Most Innovative Woman in Artificial Intelligence’ (UK). She is the founder of Locke Data, a UK-based data science consultancy, and Nightingale HQ.
Dr. Finn Macleod
Dr. Finn Macleod is a former mathematician with a PhD in predictive complexity. He has built, sold and designed dashboards for clients such as Thomson-Reuters, Formula 1 (via Meshh) and Heineken.
Mick is a Quantitative Analyst working on data science projects in financial services. He advises and assists financial service companies in managing and implementing data-driven processes within their organisations.
Aoife is the Managing Director of data consultancy The Analytics Store. Her expertise ranges from telling stories with data visualisations, to machine learning, to developing analytics strategy, and just about everything else in between.
Dr. Francesca Bonin
Francesca Bonin is a Research Scientist at IBM Research Ireland, working on AI and Natural Language Processing (NLP). In IBM Research AI, she has been part of the Project Debater team, developing breakthrough AI technologies for the last IBM AI Grand Challenge.
Fintan is the CEO and Co-Founder of Ubotica Technologies, a Computer Vision and AI start-up based in DCU Alpha, along with a design centre in Spain. Ubotica is developing enabling technologies and solutions for decision making ‘on the edge’, targeting Earth Observation and Industry 4.0 applications.
Talent Garden is the leading European digital innovation hub and co-working ecosystem. Founded in Brescia, Italy, Talent Garden has now grown its network across 18 European cities and is hosting over 3,500 digital professionals in 26 campuses across 8 countries.
Unlike real estate driven international co-working operators, Talent Garden puts education and innovation at its core in developing its tech community. It focuses on new ways to transform and connect the flexible work and education environments that are being demanded by digital entrepreneurs and businesses undergoing a digital transformation.
Date: Coming soon 2020
Time: Every Thursday & Friday evening (6.15pm – 9.15pm)
Duration: 18 weeks
Why join Talent Garden Innovation School?
GET FUTURE READY
More than 50% of companies suffer from a digital talent gap within their teams, and 29% of employees believe their skills are redundant, or will be within the next two years. We are here to upgrade your knowledge, career opportunities and toolbox for your future work challenges and help you and your company grow.
Our faculty come from a wide range of backgrounds including IT, Business, Emerging Media, Education, Design, HR, Psychology, Philosophy, Creative Arts and many other related areas. We are educators who are committed to the student learning experience and to taking an ‘action learning’ approach to teaching. We do this by delivering interactive lectures and practical workshops that are grounded in theory, but focused on real-world application.
COMMUNITY OF ENTREPRENEURS
Our Innovation School is located at Talent Garden Dublin in Glasnevin, a 3,200sqm campus that is home to 350 entrepreneurs. The campus is designed to enhance creativity and provide the ideal setting for professionals to work and learn but also facilitates great networking opportunities with other digital and tech professionals.