• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Магистратура 2019/2020

Анализ данных в Python

Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус: Курс по выбору (Экономика: исследовательская программа)
Направление: 38.04.01. Экономика
Когда читается: 1-й курс, 4 модуль
Формат изучения: с онлайн-курсом
Прогр. обучения: Экономика: исследовательская программа
Язык: английский
Кредиты: 3
Контактные часы: 4

Course Syllabus

Abstract

In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!
Learning Objectives

Learning Objectives

  • Learn how to analyze data using Python
  • This course will take you from the basics of Python to exploring many different types of data.
Expected Learning Outcomes

Expected Learning Outcomes

  • learn how to import data sets
  • learn how to clean and prepare data for analysis
  • learn how to manipulate pandas DataFrame
  • learn how to summarize data
  • learn how to build machine learning models using scikit-learn
  • learn how to build data pipelines
  • learn how to data Analysis with Python is delivered through lecture, hands-on labs, and assignment
Course Contents

Course Contents

  • Module 1 - Importing Datasets
    Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets
  • Module 2 - Cleaning and Preparing the Data
    Identify and Handle Missing Values Data Formatting Data Normalization Sets Binning Indicator variables
  • Module 3 - Summarizing the Data Frame
    Descriptive Statistics Basic of Grouping ANOVA Correlation More on Correlation
  • Module 4 - Model Development
    Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared and MSE for In-Sample Evaluation Prediction and Decision Making
  • Module 5 - Model Evaluation
    Model Evaluation Over-fitting, Under-fitting and Model Selection Ridge Regression Grid Search Model Refinement
Assessment Elements

Assessment Elements

  • non-blocking All Review Questions
  • non-blocking The Final Exam
    Final scoring based on a cumulative grade from online course ( "Analyzing Data with Python" from edX)
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.5 * All Review Questions + 0.5 * The Final Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Romano, F. (2015). Learning Python. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1133614

Recommended Additional Bibliography

  • Программирование на PYTHON. Т. 1: ., Лутц, М., 2013
  • Программирование на PYTHON. Т. 2: ., Лутц, М., 2013