Statistics & Mathematics for Data Science in Python

Course Curriculum

Chapter 1: Google Colab for Data Science

Lecture 1: Course Overview

Lecture 2: Introduction

Lecture 3: Google Drive & Colab Introduction

Lecture 4: Documentation Exploration

Lecture 5: Importing Data from Google Drive to Pandas DataFrame

Lecture 6: Importing Data from OneDrive to Pandas DataFrame

Lecture 7: Sharing a Colab Notebook

Lecture 8: Summary

Chapter 2: Vocabulary & Descriptive Statistics

Lecture 1: Introduction

Lecture 2: Introduction to General Statistical Vocabulary

Lecture 3: Variable Types within Data

Lecture 4: Summarizing Data with Counts

Lecture 5: Measures of Center, Essential Analytics

Lecture 6: Correlation Coefficient

Lecture 7: Summary

Chapter 3: Distribution Types

Lecture 1: Introduction

Lecture 2: Introduction to Probability Distributions

Lecture 3: Uniform Distribution

Lecture 4: Binomial Distribution

Lecture 5: Poisson Distribution

Lecture 6: Normal Distribution

Lecture 7: Fitting Distributions – Advanced

Lecture 8: Summary

Chapter 4: Inferential Statistics with Visualizations

Lecture 1: Introduction

Lecture 2: Bar Charts

Lecture 3: Histograms

Lecture 4: Box Plots

Lecture 5: Scatter Plots

Lecture 6: Advanced Visualizations

Lecture 7: Summary

Chapter 5: Confidence Intervals & Hypothesis Testing

Lecture 1: Introduction

Lecture 2: Seaborn Sample Data & Fitting

Lecture 3: Introduction to Confidence Intervals & Tests

Lecture 4: Assuming Normality

Lecture 5: Normal Data:Probability Plots with Means

Lecture 6: Normal Data: Categorical Confidence Intervals

Lecture 7: Normal Data: Quantitative Confidence Intervals

Lecture 8: ANOVA

Lecture 9: Non-Normal Data & Bootstrap

Lecture 10: Summary

Chapter 6: Regression & Predictions

Lecture 1: Introduction

Lecture 2: Preparation Part 1: Loading & Exploring Diamonds Data

Lecture 3: Preparation Part 2: Categorical Coding & Data Splitting

Lecture 4: Linear Regression

Lecture 5: Polynomial Regression

Lecture 6: Ridge Regression

Lecture 7: Lasso Regression

Lecture 8: ElasticNet Regression

Lecture 9: Random Forest Regression

Lecture 10: Model Comparison Tool

Lecture 11: Model Hyper Tuning & Optimization

Lecture 12: Summary

Chapter 7: Classification Modeling

Lecture 1: Introduction

Lecture 2: Preparation Part 1: Loading & Exploring Penguins Data

Lecture 3: Preparation Part 2: Cleaning & Preparing Penguins Data

Lecture 4: Naive Bayes

Lecture 5: Logistic Regression

Lecture 6: K-Nearest Neighbors

Lecture 7: SVM

Lecture 8: Random Forest

Lecture 9: Model Comparison Tool

Lecture 10: Model Hyper Tuning & Optimization

Lecture 11: Summary

Chapter 8: Natural Language Processing

Lecture 1: Introduction

Lecture 2: Data Loading & Exploration

Lecture 3: NLTK to Examine Text

Lecture 4: For Loop Creation of 8.2

Lecture 5: Movie Reviews Text Analysis & Frequency

Lecture 6: Finding Features of Textual Data

Lecture 7: Naive Bayes with NLTK

Lecture 8: Cosine Similarity Between Texts

Lecture 9: Summary

Chapter 9: Project

Lecture 1: Project Resource File