Bachelor
2021/2022
Programming and Data Processing
Type:
Elective course
Area of studies:
Economics
Delivered by:
International College of Economics and Finance
When:
1 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Georgi Zhulikov
Language:
English
ECTS credits:
4
Contact hours:
72
Course Syllabus
Abstract
a. Prerequisites High school informatics: ● solid user-level understanding of the Windows (or other desktop) operating system, including user interface and navigation, file system, running and installing applications, using standard applications: text editor, browser, mail client, etc. ● programming basics (any high-level language): types and variables, flow control, simple data structures b. Abstract In the modern, highly technological world computer skills have become essential for specialists in all fields. Programming in particular has gone beyond its traditional borders of being just a prerogative of IT specialists, turning into an element of computer literacy. In the last 10 years programming languages and tools have evolved significantly, which now enables people even without a solid technical background to successfully master related skills. The present course is offered to 1st year ICEF students as an alternative to the entry level “Information Computer Systems” course. Students wishing to enter the course can take a test to verify the required knowledge and skills (see prerequisites). The main part of the course is focused on programming and data processing techniques using the Python language. It is complemented by two additional parts. First, introduction to some of the basic competencies of working with software which are necessary for efficient performance in academic activities, as part of the HSE educational project “Data Culture”. Second, a blended part on Excel, featuring data processing techniques that can be useful in later ICEF courses and economics-related applications. The course is not part of the University of London international programme.
Learning Objectives
- Provide students with knowledge of fundamental programming principles and the corresponding practical skills
Expected Learning Outcomes
- Understand the fundamentals of the Python data model
- Applies pivot tables to solve business problems
- Applies Python standard functions for input and output, including formatted output
- Applies techniques for data cleansing
- Applies the conditional operator to solve branching tasks
- Applies the loop operator to process sequences of numbers or strings
- Can set up a working environment on a preferred operating system
- Can use internet services for academic activities, including business correspondence via email, formulating efficient search queries
- Creates, edits and formats presentations in MS PowerPoint.
- Creates, edits and formats texts in MS Word.
- Identifies text and binary files
- Identifies Web APIs and their formats
- Knows main data serialization formats
- Knows the additional Python features related to linear collections
- Knows the basics of modern computer architecture
- Knows the common code templates for loops
- Knows the ecosystem of external Python packages
- Knows the range of financial formulas in Excel
- Navigates the basic Excel screen
- Understands the architecture of the modern Web
- Understands the use-cases of associative containers
- Uses formulas, functions, subtotals and text formatting
- Uses functions from standard Python modules
- Uses lambda functions, iterables and generators in Python programs
- Uses lookup Excel functions
- Uses speaker notes, charts, slide library, imports data from external sources,
- Uses standard functions of the str class to perform basic text processing
- Uses standard Python packages for automated data acquisition from the Web
- Uses styles, paragraph formats, themes, tables, graphics.
- Uses text files for application data storage
- Uses the Python list collection with its standard functions
- Uses the Python shell mode for simple arithmetic computations on integer numbers
- Uses the standard dict and set classes
- Writes modular code using functions
Course Contents
- Part 1. Introduction
- Introduction to computer architecture and programming. Python basics
- Program flow
- Structuring program code. Functions and modules
- Python data model. Data structures
- Advanced Python programming techniques
- File input-output
- Automated data acquisition
- Python extras
- Excel: Navigation and basic workbook functionality
- Excel: Lookups and data cleansing
- Excel: Logical functions and pivot tables
- Excel: Basic statistical forecasting, financial formulas
Interim Assessment
- 2021/2022 1st moduleFor final grade determnation see module 2.
- 2021/2022 2nd module0.1 * Quiz + 0.2 * Midterm exam + 0.45 * Final exam + 0.1 * Online test + 0.15 * Homework assignments
Bibliography
Recommended Core Bibliography
- Ben Stephenson. (2019). The Python Workbook : A Brief Introduction with Exercises and Solutions (Vol. 2nd ed. 2019). Springer.
- Downey, A. (2015). Think Python : How to Think Like a Computer Scientist (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1105725
- Walkenbach, J. (2016). Excel 2016 Bible. Indianapolis, IN: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1079681
Recommended Additional Bibliography
- Lutz, M. (2008). Learning Python (Vol. 3rd ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415392