Бакалавриат
2020/2021
Методы и инструменты анализа данных
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс по выбору (Управление логистикой и цепями поставок в бизнесе)
Направление:
38.03.02. Менеджмент
Где читается:
Высшая школа бизнеса
Когда читается:
3-й курс, 4 модуль
Формат изучения:
с онлайн-курсом
Преподаватели:
Кузнецова Юлия Александровна
Язык:
английский
Кредиты:
9
Контактные часы:
30
Course Syllabus
Abstract
The course focuses on the practical application of data analysis tools. All classes are conducted in a computer lab and include a brief review of the necessary theoretical principles, the method implementation in software and the solution of practical tasks.
Learning Objectives
- The course aims to study modern methods and tools for analysis, visualization, and forecasting data.
Expected Learning Outcomes
- Creates Markdown report to present data analysis results.
- Applies Python language for data processing.
- Uses Python for data loading, cleaning and preparation.
- Aggregates data using Python.
- Creates data visualizations using Python.
- Knows basic statistic, econometric, and machine learning models and uses Python for model building.
Course Contents
- Introduction to data analysis methods and toolsPython ecosystem. Jupyter notebooks. Markdown for reporting. Python language basics. Basic data structures, functions. Numpy arrays. Data processing with Pandas.
- Data loading, cleaning and preparationHandling missing data, data transformation, string manipulation. Data aggregation and group operations.
- Plotting and data visualizationPlotting with matplotlib, pandas and seaborn. Line plots, bar plots, histograms, scatter plots, boxplots.
- Introduction to modeling libraries in PythonModeling with statsmodels, scikit-learn.
Interim Assessment
- Interim assessment (4 module)0.15 * Assignment 1 + 0.15 * Assignment 2 + 0.15 * Assignment 3 + 0.3 * Final Examination + 0.25 * Home task
Bibliography
Recommended Core Bibliography
- Embarak O. Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems. - Apress, 2018.
- McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
- Nelli, F. (2018). Python Data Analytics : With Pandas, NumPy, and Matplotlib (Vol. Second edition). New York, NY: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1905344
Recommended Additional Bibliography
- Груздев А. В. , Хейдт М. - Изучаем Pandas - Издательство "ДМК Пресс" - 2019 - 700с. - ISBN: 978-5-97060-670-4 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/131693