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Бакалаврская программа «Управление цепями поставок и бизнес-аналитика»

Data Analysis Methods and Tools

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
9
Кредиты
Статус:
Курс по выбору
Когда читается:
3-й курс, 4 модуль

Преподаватель


Кузнецова Юлия Александровна

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

Learning Objectives

  • The course aims to study modern methods and tools for analysis, visualization, and forecasting data.
Expected Learning Outcomes

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

Course Contents

  • Introduction to data analysis methods and tools
    Python ecosystem. Jupyter notebooks. Markdown for reporting. Python language basics. Basic data structures, functions. Numpy arrays. Data processing with Pandas.
  • Data loading, cleaning and preparation
    Handling missing data, data transformation, string manipulation. Data aggregation and group operations.
  • Plotting and data visualization
    Plotting with matplotlib, pandas and seaborn. Line plots, bar plots, histograms, scatter plots, boxplots.
  • Introduction to modeling libraries in Python
    Modeling with statsmodels, scikit-learn.
Assessment Elements

Assessment Elements

  • non-blocking Assignment 1
  • non-blocking Assignment 2
  • non-blocking Assignment 3
  • non-blocking Home task
  • blocking Final Examination
Interim Assessment

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

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