Data Science Boot Camp - 18 Week Course - Part Time

PART-TIME BOOTCAMP

DATA SCIENCE

Futureproof your skillset with Machine Learning, R, Data Visualisation and PowerBI

WHY JOIN THIS COURSE?

This 18-week bootcamp teaches you the skills you need to deliver data science projects effectively. You 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, you’ll do two evening sessions that blend lecture and labs to ensure you get practical experience that will help you become a successful data scientist.

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.

WHO IS THIS COURSE FOR?

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 data science skills needed to stay ahead of the game – typically IT or finance professionals – risk, accountants, analysts – as well as business & data analysts, researchers, academics and software engineers seeking to learn more, upskill and gain a competitive advantage from their data.

LEARNING OUTCOMES

• Comfortable with a robust data science process and able to implement the process in 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 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 application
• Apply learning to your own dataset and other pre-defined dataset challenges
• Aware of further resources for continued self-learning

Key learning areas

DATA EXPLORATION

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 you can hit the ground running and build impeccable models by the end.

MACHINE LEARNING

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).

DATA VISUALISATION

There is an increasing need to extract value from data investments. Data Visualisation enables you to present your new knowledge clearly and persuasively. Participants learn about new tools and techniques that deliver state-of-the-art data visualisations quickly and easily. You 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.

For more information or to apply click here.