Today the same amount of information is created by computers and users around the world in 48 hours, as was created from the beginning of humanity until year 2003. Software encompasses an increasing amount of industries, and as Internet of Things (IoT) solutions become more common, big data analytics must be used to process the data they generate. To find patterns in and being able to handle this enormous amount of data opens for better and more precise fact-based decision-making. Companies and organisations have already realized the opportunities this brings and there is a continuously growing demand of trained big data analytics developers today.
Big Data Analytics specialisation studies gives you an in-depth understanding of how to make use of data in order to create insights. The intended student for these specialisation studies is someone with a programming background who wants to understand how to employ machine learning methods in a business environment. The program is arranged so that you rapidly gain a broad understanding of the essential concepts of big data analytics; descriptive and predictive modelling, as well as optimization. The program emphasizes the importance of understanding how to build analytical solutions with production level code.
As a student you will be involved in projects connected to real-world problems that includes elements of both group work and individual achievements. The focus on real-world problems emphasizes disruptive problem solving through analytics service development. Communication and business acumen is emphasized both through data visualization and traditional pitching.
Are you looking for our Master's degree programme in Big Data Analytics? Read more here.
Study alongside work
The specialisation studies in Big Data Analytics are tailored so that you can attend them alongside full-time employment.
This programme will cover the following subjects.
- Introduction to Analytics, 5 ECTS
- Machine Learning for Predictive Problems, 5 ECTS
- Visual Analytics, 5 ECTS
- Machine Learning for Descriptive Problems, 5 ECTS
- Big Data Analytics, 5 ECTS
- Analytical Service Development, 5 ECTS
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 or computational resources from the Finnish Supercomputing Center (CSC). These tools will enable students to learn how to make use of GPU-accelerated models for processing big data.
|Autumn 2022||Introduction to Analytics|
|Autumn 2022||Machine Learning for Predictive Problems|
|Winter 2022-2023||Visual Analytics|
|Spring 2023||Machine Learning for Descriptive Problems|
|Spring 2023||Big Data Analytics|
|Spring 2023||Analytical Service Development|
Lectures will be held every other week on Thursdays and Fridays from 13 to 18. Presently, the lectures are planned to be carried out mainly on-site, however, we are continuously monitoring the authorities guidelines about COVID-19.
For more information about these modules see the curriculum below.
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.
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.
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.
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.
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.
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.
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.