Tokyo is the capital of Japan. It has a population of around 8.9 million people but it has the largest populous metropolitan area in the world at a whopping 13 million people. Tokyo is split into 23 separate wards which operate and are governed as separate cities. The city also hosts 51 of the Fortune Global 500 companies, a record of any city in the world. Because of its population, Tokyo has a vast amount of schools. Public elementary and primary schools are run by the local wards, secondary schools are run by the Tokyo Metropolitan Government Board of Education and there are also many private schools. Some universities provide classes which are taught in English and teach the Japanese language to students such as the Sophia University, International Christian University and Waseda University. There are a number of large, prestigious universities in Tokyo such as University of Tokyo, Keio University and Hitotsubashi University. University of Tokyo is the highest rated university in Asia and enrols around 30,000 students a year. It was established in 1877 and provides a number of undergraduate and graduate programs in law, science, engineering and more. A small percentage of people in Japan speak fluent English, around 3%. English is taught in schools, however it is mostly just reading and writing skills. The unemployment rate in Japan runs at about 3.4%. The unemployment rate in Tokyo runs roughly the same at a rate of 3.5%.
Programming Training | F# For Data Scientist Training in Tokyo
F# for Data Scientist Training in Tokyo introduces F# from setup, literals, strings, bindings, and functions to loops, pattern matching, and exception handling. Learners gain experience with types, collections, records, object programming, computation expressions, queries, reflection, and type providers for data-focused development.
- Learn about computation expressions for encoding context-sensitive computations.
- Understanding events to associate function calls with user actions in GUI programming.
- Become familiar with the discriminated unions that are useful for heterogeneous data.
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