All students on degrees with Russian take both modules on the Year 2A Russian Language programme:

SERS0016: Comprehension of Russian (Year 2A) (Russian-English Translation, Aural Comprehension)
SERS0018: Use of Russian (Year 2A) (Grammar, Working with Texts, Writing Skills, Oral)

The two compulsory modules build on the thorough base in Russian achieved in the Year 1A Russian language programme through further study at intermediate level and leading towards an upper intermediate level of Russian.

Aims:

To develop students' command of Russian at an intermediate level and leading towards an upper intermediate level.
To develop skills in reading, translating and listening
To consolidate and further develop a knowledge of grammar and syntax, lexis, register and style, as well as language awareness and language learning skills To acquaint students with a wide range of authentic language materials, to develop skills of working with texts, and to enhance students' knowledge and understanding of Russian life and culture through the materials studied.

Aims - This module aims to teach basic concepts of programming using Python. It complements STAT0004 by focusing more generally on algorithms and programming...

Aims - This module aims to teach basic concepts of programming using Python. It complements STAT0004 by focusing more generally on algorithms and programming logic with illustrative algorithms for data manipulation. The module also introduces concepts that aid software engineering and effective computation, such as notions of error handling and object-oriented programming. It is intended for first and second year students registered on the undergraduate degree programmes offered by the Department of Statistical Science, or jointly with other departments.

Objectives - On successful completion of the module, a student should be able to make effective use of a Python programming environment, produce algorithms for basic data science tasks, with a respective implementation in Python, productively organise code and make use of existing libraries, develop a general understanding of procedural and object-oriented programming that goes beyond just Python coding.

Applications - An understanding of computational thinking, and in particular an ability to program, are essential skills for data scientists. This module introduces techniques that will provide students with a solid basis for conducting practical data analyses in a wide variety of application areas.

Content - Introduction to Python. Elementary data types, arrays and matrices, principles of data manipulation. Control structures: if/then, loops. Illustrative numerical and string manipulation algorithms. Functions, packages and scope. Debugging and exception handling. Built-in data structures: Python collections. File handling. Examples of packages for data science: pandas and numpy. Principles of object-oriented programming.

Key texts - Available from ReadingLists@UCL.