XBUS-504-01.Data_Analysis_I_Statistics

Quickstart

$ git clone https://github.com/georgetown-analytics/XBUS-504-01.Data_Analysis_I_Statistics.git
$ cd XBUS-504-01.Data_Analysis_I_Statistics/
$ pip install -r requirements.txt
$ cd notebooks/
$ jupyter notebook 0_Config_Test.ipynb

Course Details

The fields of statistics and probability were founded on empirical analysis of data (e.g. human height). Data scientists must possess a strong foundation in statistics and probability to uncover patterns and build models, algorithms, and simulations. This course reviews the basics of descriptive and inferential statistics, distributions, probability, and regression with a specific focus on application to real data sets.

Course Objectives

Upon successful completion of the course, students will:

Explain descriptive and inferential statistics
Compute measures of central tendency, variance, and probabilities
Produce and interpret meaningful and accurate summary statistics for a given data set
Conduct hypothesis tests and understand the difference between Type I and Type II errors
Develop single and multivariate regression models
Differentiate between correlation and causation

Notes

Enrollment in this course is restricted. Students must submit an application and be accepted into the Certificate in Data Science in order to register for this course.

Current Georgetown students must create an application using their Georgetown NetID and password. New students will be prompted to create an account.

Course Prerequisites

Course prerequisites include:

A bachelor's degree or equivalent
Completion of at least two college-level math courses (e.g. statistics, calculus, etc.)
Successful completion of Data Wrangling (XBUS-503)
Basic familiarity with programming or a programming language
A laptop for class meetings and coursework