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Regular version of the site

Data Analysis in Python

2019/2020
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
2 year, 4 module

Instructor

Программа дисциплины

Аннотация

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!
Цель освоения дисциплины

Цель освоения дисциплины

  • Learn how to analyze data using Python
  • This course will take you from the basics of Python to exploring many different types of data.
Результаты освоения дисциплины

Результаты освоения дисциплины

  • 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
Содержание учебной дисциплины

Содержание учебной дисциплины

  • 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
Элементы контроля

Элементы контроля

  • All Review Questions (неблокирующий)
    Though Review Questions and the Final Exam have a passing mark of 50% each, the only grade that matters is the overall grade for the course. Review Questions have no time limit. You are encouraged to review the course material to find the answers as this counts for 50% of your mark.
  • The Final Exam (неблокирующий)
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (4 модуль)
    0.5 * All Review Questions + 0.5 * The Final Exam
Список литературы

Список литературы

Рекомендуемая основная литература

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

Рекомендуемая дополнительная литература

  • Изучаем Python, Лутц М., Киселева А., 2014