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Statistics for Data Science

Version 1.0

Course Duration

12 Hours

Course Summary

This course introduces the basics of carrying out a statistical analysis in Python. It covers exploratory data analysis and constructing and interpreting linear and generalized linear models.

Course Objective

By the end of the course students will be comfortable implementing and interpreting linear models and generalized linear model in Python.

Lead Developer

Laurie Baker, Dan Lewis

Course Reviewer(s)

Pending

Intended Audience

This course is intended for those interested in learning and applying linear and generalized linear modelling to their work.

Learning Objective Detail

Chapter 1: Exploratory Data Analysis

By the end of Chapter 1, learners should know:

  • What is tidy data?
    • What is a variable, value, and observation?
    • Several python commands to explore the structure of the data
    • What is the difference between a continuous and categorical variable?
    • What is variation and covariation?
  • Where Exploratory Data Analysis fits within data analysis?
    • How to use plots to explore variation in continuous variable and categorical variables
    • How to use plots to explore covariation between two categorical variables, two continuous variables or a categorical and continuous variable.

Chapter 2: Model Basics

By the end of Chapter 2, learners should know:

  • Model Basics

    • What is a model family and fitted model?
    • What is the difference between a response and an explanatory variable?

  • Model Construction

    • How to construct a linear model in python?
    • What are the slope and intercept in a linear model?
    • Picking out key information from the model table
    • How to extract specific parameters from the model object.

  • Assessing Model Fit

    • How to inspect model residuals to assess model fit?
    • How to pick out key information from the table from a fitted model.
    • How to use Adjusted R-squared and AIC to compare models.

Chapter 3: Generalized Linear Models

By the end of Chapter 3, learners should know

  • What is probability?

  • What is a random variable?

  • What a probability distribution is and how it differs for continuous vs. discrete random variables?

  • Be familiar with several common probability distributions used to model variation in the response variable

    • Binomial
    • Normal
    • Poisson
    • Negative Binomial

  • How to implement a generalized linear model in python.

Chapter 4: Bayesian Analysis

By the end of Chapter 4, learners should know

  • What is Baye's rule and how it is used in Bayesian statistics?
  • How Bayesian and Frequentist schools of thought differ?
  • How to implement a simple Bayesian linear model in python.

Course Type

  • E learning - Not Available
  • Self learning - Available
  • Face to face - Available

Skill Level

Participants should be familiar with Python but do not need any prior statistical training.

Pre requisite summary

Introduction to Python.

Pre-requisites

Package requirements: import numpy as np # linear algebra import pandas as pd # data processing import matplotlib.pyplot as plt # data plotting import seaborn as sns # data visualisation and plotting import statsmodels.api as sm # statistical modelling package import statsmodels.formula.api as smf # statistical modelling package with R-like formulas import scipy.stats as stats import math

from statsmodels.genmod.generalized_linear_model import GLM # importing packages to run GLM from statsmodels.genmod import families # importing families for exponential families from scipy.stats import binom # to illustrate the binomial distribution. from sklearn import datasets, linear_model # fetching iris dataset and linear model functions from sklearn.metrics import mean_squared_error, r2_score