• EnglantiErinomainen
  • SuomiÄidinkieli
  • SaksaErinomainen
  • RuotsiHyvä
  • VenäjäTyydyttävä
  • RanskaAlkeet
  • ItaliaAlkeet
  • KiinaAlkeet


ProjektijohtaminenData ScienceRiskien hallintaRiskianalyysitTiimityöskentelyKansainvälinen turvallisuustoimintaLogistinen turvallisuustoimintaTurvallisuuskoulutusTietoturvallisuustoimintaTurvallisuusasiantuntijuusSustainabilityKestäväkehitysToimitusketjujen hallintaSupply Chain SecurityRikostorjuntaTuoteväärennöksetAnalyyttinen ajatteluComplianceVastuullisuusraportointiRiskiprofilointiSertifioinnit


The Data Scientist’s Toolbox
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Getting and Cleaning Data
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
R Programming
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
The Investigation of Transnational and Organized Intellectual Property (IP) Crime
The course aims to provide officers with the knowledge to enable them confidently approach the investigation of IP Crime.