Big data analytics: Unleashing the art and science of analysing diverse data sources
|Study location||Netherlands, Maastricht|
|Type||Summer Courses, full-time|
|Nominal duration||3 weeks (9 ECTS)|
|Tuition fee||€1,699 one-time|
Enrolled as an Undergraduate student or Undergraduate diploma
Basic knowledge and experience using R for quantitative data analysis.
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
The language of the course is English, so we expect a fluent level and the ability to follow and participate in class.
In today’s information age, the massive amount of data constantly generated has made big data analysis a critical tool for businesses, governments, and researchers seeking to gain insights from vast and complex data sources. The ability to harness and analyse big data has become increasingly important as the amount of data being generated continues to grow at an unprecedented rate. This course will provide an introduction to big data analysis and provide students with hands-on experience with analysing various types of big data. Students will gain a comprehensive understanding and hands-on experience with data harvesting, data cleaning, data wrangling, and data visualization, as well as a variety of analytical techniques such as descriptive statistics, machine learning and natural language processing methods, and time series modelling. They will learn how to collect and clean data from various sources and pre-process data using techniques such as stemming, lemmatization, and stop word removal. They will also learn how to visualize data using a range of tools and packages. The course will cover hardware and software considerations and introduce strategies for handling large datasets on regular computers, which is essential for working with big data. By the end of the course, students will have a solid foundation in big data analysis techniques and will be able to apply these techniques to real-world data and will be well-prepared for further study and research in this area.
• Collect, clean, and pre-process real-world data from various sources;
• Understand the hardware and software considerations involved in working with Big Data;
• Learn strategies for handling large datasets on regular computers;
• Gain hands-on experience in working with real-world data and using R;
• Visualize data using a range of tools and software solutions;
• Learn and apply descriptive statistics techniques to analyse trends and patterns in Big Data;
• Learn and apply natural language processing techniques to analyse Big Data.
• Learn and apply machine learning techniques to analyse Big Data;
• Conduct independent, data-driven research based on Big Data analytics
• Dasgupta, N. (2018). Practical big data analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R. Packt Publishing Ltd.
• Hvitfeldt, E., & Silge, J. (2021). Supervised machine learning for text analysis in R. CRC Press.
• Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. “ O’Reilly Media, Inc.”.
▪ Lectures ▪ PBL ▪ Work in subgroups
▪ Assignment ▪ Final paper ▪ Oral exam ▪ Presentation
Dr. Eliyahu V. Sapir
Start Date: 7 August 2023
End Date: 26 August 2023
Central European Time
Central European Time