2026/2027
Сбор и анализ данных с использованием Python
ID 1163322
Статус:
Маго-лего
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
1 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Токман Мария Александровна
Язык:
английский
Кредиты:
3
Контактные часы:
28
Course Syllabus
Abstract
This practice-oriented course covers the full data research cycle — from web data collection to visual storytelling. It is built around four interconnected modules: Python programming fundamentals, web scraping, exploratory data analysis, and data visualization.
Learning Objectives
- This course aims to equip students with practical skills for the complete data analysis workflow using Python. Students will learn to develop web scrapers with requests and BeautifulSoup to collect data from static websites and APIs, and to clean, preprocess, and transform raw datasets using pandas for further analysis. The course also focuses on conducting exploratory data analysis, gaining experience with visualization tools in Python, and organizing reports with visual findings. By the end of the course, students will be able to design and implement a complete, reproducible data pipeline from data collection to final presentation, documented in Jupyter Notebook, while critically interpreting results and formulating evidence-based conclusions for business or research contexts.
Expected Learning Outcomes
- Write Python code using variables, loops, conditionals, functions, and built-in data structures
- Read, write, and process data from text, CSV, and JSON files with proper error handling
- Develop web scrapers to collect structured and unstructured data from static websites using requests and BeautifulSoup
- Collect data from REST APIs, handling authentication, parameters, and JSON responses
- Clean, preprocess, and transform raw data using pandas, handling missing values, duplicates, outliers, and inconsistent data types for further analysis.
- Conduct exploratory data analysis (EDA) by computing descriptive statistics, aggregating data, and extracting meaningful patterns
- Create static and interactive visualizations using matplotlib, seaborn, and plotly to effectively communicate analytical insights
- Design and implement a complete data pipeline from data collection to final presentation, documenting the process in a reproducible Jupyter Notebook
Course Contents
- Python Basics for Data Analysis
- Data Collection / Web Scraping
- API Data Collection
- Data Cleaning and Preprocessing with pandas
- Exploratory Data Analysis
- Data Visualization and Interpretation
- Practical Case: Full Data Workflow
Assessment Elements
- Project Progress ReviewThis is an intermediate milestone for the final team project (up to 4 members per team). Teams present their progress on data collection, demonstrating their scraping and API scripts in a reproducible Jupyter Notebook.
- AssignmentsAssignments are conducted during each seminar and include active participation, student engagement, quizzes, and short tests. Each seminar offers the opportunity to earn from 0 to 5 points. The final assignment score is calculated by dividing the total points earned across 6 seminars by 3. The maximum score is 10 (achieved by scoring 5 points in all 6 seminars).
- Final ProjectThis is a team project (up to 4 members per team). Create a fully functional data analysis project that includes web scraping, data cleaning, exploratory analysis, visualization, and reporting, with all steps recorded in a reproducible Jupyter Notebook.
Interim Assessment
- 2026/2027 1st module0.2 * Project Progress Review + 0.5 * Final Project + 0.3 * Assignments
