Curriculum for Big Data Analytics
The curriculum consists of six courses (5 ECTS credits each) that focus on how to program intelligent services using analytical and machine learning methods. Each of the courses are thought through solving programming problems were the students can make use of Arcada’s Nvidia-sponsored Big Data Lab. The lab enables students to learn how to make use of GPU-accelerated models for processing big data.
Due to the ongoing pandemic we aim to enable students to study the programme at distance. We will provide lecture streams for our students, at least during the autumn term and are monitoring the situation for the spring term as well. Note, that we ask students to actively participate during lectures and therefore request that they have a working system, including a microphone and possibly a camera.
Introduction to Analytics (Module 1)
The aim of the course is to introduce the student to the different concepts of implementing an analytics process. Students learn the process of problem solving in analytics from data understanding and preprocessing, through modelling choices and implementation until the interpretation, visualization and utilization of the analysis. We will look at typical real-life
applications of analytics. The course will provide hands-on lectures to performing the steps of modeling and analysis.
Machine Learning for Predictive Problems (Module 2)
You learn a practical approach for predicting with Machine Learning with all the steps starting from data acquisition and preparation, search for optimal parameters, to comparison of different methods and evaluation of results. You can employ a linear model for regression and classification, train neural networks, build data features with deep learning, represent and process natural text with numerical methods.
Visual Analytics (Module 3)
You learn how to lead in turbulent times through data-driven management. You also learn to understand how to become an agent of change for transforming data into insights. People are often visual beings and therefore the focus of the course is on reducing information, through algorithms, that can then be visualized. You develop an understanding of visual analytical methods as a communication medium for business intelligence.
Machine Learning for Descriptive Problems (Module 4)
You learn to efficiently handle massive datasets and extract hidden knowledge from data. You understand how to employ classification and clustering algorithms on three different types of data: text, streaming and graph. You acquire knowledge of methods and programming tools for processing big data on distributed/cloud systems.
Big Data Analytics (Module 5)
You are given an overview of machine learning and how to utilize big data. The methods for descriptive and predictive modelling are introduced for small data, and you are then given an explanation for how similar models can be modified to work with big data. You will be introduced to the analytical process; big data tooling, data-related requirement handling, domain knowledge, modelling and verification of results.
Analytical Service Development (Module 6)
You develop an understanding for planning the analytical process; data-related requirement handling, domain knowledge/modelling expertise and verification of results. Each student completes an industry cap-stone project as part of the course.