IBS' conversion master's programme welcomes students from all areas who would like to become professionals with strong business foundations. Get extensive data analytics skills to improve your employability and become a more valuable member of the organisation that you work for!

The goal of the MSc in IT for Business Data Analytics programme is to (re-)train professionals who will be able to occupy junior and mid-level data analyst positions, thanks to their ability to import, inspect, clean, transform, validate, model, or interpret collections of data with regard to the business goals of the company as well as to their ability to use different algorithms and IT tools as demanded by the situation and the current data.

Those graduating from the programme can prepare reports in the form of visualizations such as graphs, charts, and dashboards. The programme is therefore a conversion programme aimed at those seeking career advancement or career change and one that will train data professionals with strong business foundations.

The schedule makes it possible for students even with a full-time job to participate as classes start at 17:00 on weekdays!

Please find the curriculum here.*

* IBS reserves the right to change the curriculum.

Please find the programme specification here.

**The start of each MSc programme depends on a sufficient number of students (at least 15 students per programme).

*** i.e.: TOEFL 79, Duolingo 120, Medium of Instruction certificate etc. Please find further details here .

Note: Classes that feature coding will be delivered online, so please make sure you have access to a PC or Laptop with administrator rights, minimum Core i5 or similar AMD processor, minimum 8 GB RAM and at least 40 GB free storage, Microsoft Windows 10 or Linux operation system. A webcam and high-speed internet access (minimum 5 Mbit/sec) is required.

Should you have any questions, concerns, kindly contact us at info@ibsbudapest.com.

Python for data analytics, data mining and machine learning (with optional modules on R, text mining and natural language processing, and advanced forecasting), databases for data science and analytics, business intelligence for data-driven management (using Tableau and/or Power BI), skills for data analysts (GitHub for collaboration, basics of survey research and A/B testing, communication skills), business skills (with optional modules on human-resource management, marketing, team management, and market and competition analysis), decision-making and analytical skills, etc.

Please find the degree sample here.

On successful completion of the programme, students are expected to gain knowledge and understanding of

• the tools used to transform large amounts of raw data into relevant and helpful business information;
• the methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset;
• the techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships;
• the process of revealing data issues using quality indicators, measures and metrics in order to plan data cleansing and data enrichment strategies according to data quality criteria;
• the techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources;
• standardized computer languages for retrieval of information from a database and of documents containing the needed information;
  the visual representation and interaction techniques, such as histograms, scatter plots, surface plots, tree maps and parallel coordinate plots, that can be used to present abstract numerical and non-numerical data, in order to reinforce the human understanding of this information;
• core business principles, enabling graduates to effectively align data analytics strategies with organizational goals.

On successful completion of the programme, students should be able to:

• Analyse big data. Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
• Apply statistical analysis techniques. Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyze data, uncover correlations and forecast trends.
• Handle data samples. Collect and select a set of data from a population by a statistical or other defined procedure.
• Manage data. Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardization, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialized ICT tools to fulfill the data quality criteria.
• Normalise data. Reduce data to their accurate core form (normal forms) in order to achieve such results as minimization of dependency, elimination of redundancy, increase of consistency.
• Report analysis results. Produce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as potential interpretations of the results.