Having an understanding of the internal mechanisms data science platforms use to manage large data collections is critical to success in this unit. It is particularly relevant to students intending careers as Software Engineers, Data Scientists, Database Administrators or Big Data Platform specialists. The unit builds upon second year DATA2001 – ‘Data Science – Big Data and Data Diversity’, and assumes students have an understanding of SQL and the principles of data analysis.

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In this unit we will study the methods and technologies used to analyse and process large datasets, as well as the ways in which they are stored and accessed. We will consider a range of statistical tools to support data exploration, including descriptive statistics, time series analysis and regression techniques. We will also explore methods for data visualization and how they can be used to support decision making.

It is important for any aspiring data scientist to understand the fundamentals of the machine learning algorithms that are used in the field of artificial intelligence. Having an understanding of how these algorithms work will enable you to be more effective in your data science projects, and to build models that are more accurate and useful. This unit will give you an understanding of the basics of neural networks, classification algorithms and reinforcement learning.

This course will equip you with the skills you need to become a successful data scientist, and will cover the key topics in data science, including data discovery, data quality, predictive modelling and machine learning. You will also learn about best practices in data management, and will develop your programming skills by using a range of languages and frameworks.

The course will be delivered by world-class practitioners, with a strong emphasis on hands-on practical experience. The lecturers are all experienced data scientists who have contributed to the development of machine learning algorithms, and will share their knowledge with you in a highly interactive environment. They will help you to apply your new-found skills to real-world problems, and to develop an innovative and creative approach to data science. You will leave the course with a clear understanding of how to implement your own machine learning solutions in your own data science project. This will give you the confidence to tackle any data science problem you are faced with in the future. You will be able to develop your own machine learning algorithms and apply them to a variety of problems, from data cleaning to prediction and forecasting. You will also be able to write your own Python programs to perform these tasks, and create your own machine learning datasets.