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Data Analysis in Python

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 3 модуль

Course Syllabus

Abstract

This course will introduce students to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.
Learning Objectives

Learning Objectives

  • introduce students to the basics of the python programming
Expected Learning Outcomes

Expected Learning Outcomes

  • will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Course Contents

Course Contents

  • Jupyter Notebook
  • DataFrame structures
  • Manipulating DataFrames
  • Statistical techniques
Assessment Elements

Assessment Elements

  • non-blocking online
  • non-blocking exam
  • non-blocking online
  • non-blocking exam
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.5 * exam + 0.5 * online
Bibliography

Bibliography

Recommended Core Bibliography

  • Nelli, F. (2015). Python Data Analytics : Data Analysis and Science Using Pandas, Matplotlib and the Python Programming Language. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1056488

Recommended Additional Bibliography

  • CONTENTS 1 Blender/Python Documentation 3. (2011). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3109D75A