Master
2019/2020
Data Analysis in Python
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type:
Elective course (Economics: Research Programme)
Area of studies:
Economics
Delivered by:
Department of Applied Economics
Where:
Faculty of Economic Sciences
When:
1 year, 4 module
Mode of studies:
distance learning
Instructors:
Konstantin Lvovich Polyakov
Master’s programme:
Academic Economics
Language:
English
ECTS credits:
3
Contact hours:
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
- 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
- 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
- Module 1 - Importing DatasetsLearning 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 DataIdentify and Handle Missing Values Data Formatting Data Normalization Sets Binning Indicator variables
- Module 3 - Summarizing the Data FrameDescriptive Statistics Basic of Grouping ANOVA Correlation More on Correlation
- Module 4 - Model DevelopmentSimple 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 EvaluationModel Evaluation Over-fitting, Under-fitting and Model Selection Ridge Regression Grid Search Model Refinement
Assessment Elements
- All Review Questions
- The Final ExamFinal scoring based on a cumulative grade from online course ( "Analyzing Data with Python" from edX)
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