Python for Time Series Data Analysis

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview – PLEASE DO NOT SKIP THIS LECTURE

Lecture 2: Course Curriculum Overview

Lecture 3: FAQ – Frequently Asked Questions

Chapter 2: Course Set Up and Install

Lecture 1: Installing Anaconda Python Distribution and Jupyter

Chapter 3: NumPy

Lecture 1: NumPy Section Overview

Lecture 2: NumPy Arrays – Part One

Lecture 3: NumPy Arrays – Part Two

Lecture 4: NumPy Indexing and Selection

Lecture 5: NumPy Operations

Lecture 6: NumPy Exercises

Lecture 7: NumPy Exercise Solutions

Chapter 4: Pandas Overview

Lecture 1: Introduction to Pandas

Lecture 2: Series

Lecture 3: DataFrames – Part One

Lecture 4: DataFrames – Part Two

Lecture 5: Missing Data with Pandas

Lecture 6: Group By Operations

Lecture 7: Common Operations

Lecture 8: Data Input and Output

Lecture 9: Pandas Exercises

Lecture 10: Pandas Exercises Solutions

Chapter 5: Data Visualization with Pandas

Lecture 1: Overview of Capabilities of Data Visualization with Pandas

Lecture 2: Visualizing Data with Pandas

Lecture 3: Customizing Plots created with Pandas

Lecture 4: Pandas Data Visualization Exercise

Lecture 5: Pandas Data Visualization Exercise Solutions

Chapter 6: Time Series with Pandas

Lecture 1: Overview of Time Series with Pandas

Lecture 2: DateTime Index

Lecture 3: DateTime Index Part Two

Lecture 4: Time Resampling

Lecture 5: Time Shifting

Lecture 6: Rolling and Expanding

Lecture 7: Visualizing Time Series Data

Lecture 8: Visualizing Time Series Data – Part Two

Lecture 9: Time Series Exercises – Set One

Lecture 10: Time Series Exercises – Set One – Solutions

Lecture 11: Time Series with Pandas Project Exercise – Set Two

Lecture 12: Time Series with Pandas Project Exercise – Set Two – Solutions

Chapter 7: Time Series Analysis with Statsmodels

Lecture 1: Introduction to Time Series Analysis with Statsmodels

Lecture 2: Introduction to Statsmodels Library

Lecture 3: ETS Decomposition

Lecture 4: EWMA – Theory

Lecture 5: EWMA – Exponentially Weighted Moving Average

Lecture 6: Holt – Winters Methods Theory

Lecture 7: Holt – Winters Methods Code Along – Part One

Lecture 8: Holt – Winters Methods Code Along – Part Two

Lecture 9: Statsmodels Time Series Exercises

Lecture 10: Statsmodels Time Series Exercise Solutions

Chapter 8: General Forecasting Models

Lecture 1: Introduction to General Forecasting Section

Lecture 2: Introduction to Forecasting Models Part One

Lecture 3: Evaluating Forecast Predictions

Lecture 4: Introduction to Forecasting Models Part Two

Lecture 5: ACF and PACF Theory

Lecture 6: ACF and PACF Code Along

Lecture 7: ARIMA Overview

Lecture 8: Autoregression – AR – Overview

Lecture 9: Autoregression – AR with Statsmodels

Lecture 10: Descriptive Statistics and Tests – Part One

Lecture 11: Descriptive Statistics and Tests – Part Two

Lecture 12: Descriptive Statistics and Tests – Part Three

Lecture 13: ARIMA Theory Overview

Lecture 14: Choosing ARIMA Orders – Part One

Lecture 15: Choosing ARIMA Orders – Part Two

Lecture 16: ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part One

Lecture 17: ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part Two

Lecture 18: SARIMA – Seasonal Autoregressive Integrated Moving Average

Lecture 19: SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART ONE

Lecture 20: SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART TWO

Lecture 21: SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART 3

Lecture 22: Vector AutoRegression – VAR

Lecture 23: VAR – Code Along

Lecture 24: VAR – Code Along – Part Two

Lecture 25: Vector AutoRegression Moving Average – VARMA

Lecture 26: Vector AutoRegression Moving Average – VARMA – Code Along

Lecture 27: Forecasting Exercises

Lecture 28: Forecasting Exercises – Solutions

Chapter 9: Deep Learning for Time Series Forecasting

Lecture 1: Introduction to Deep Learning Section

Lecture 2: Perceptron Model

Lecture 3: Introduction to Neural Networks

Lecture 4: Keras Basics

Lecture 5: Recurrent Neural Network Overview

Lecture 6: LSTMS and GRU

Lecture 7: Keras and RNN Project – Part One

Lecture 8: Keras and RNN Project – Part Two

Lecture 9: Keras and RNN Project – Part Three

Lecture 10: Keras and RNN Exercise

Lecture 11: Keras and RNN Exercise Solutions

Lecture 12: BONUS: Multivariate Time Series with RNN

Lecture 13: BONUS: Multivariate Time Series with RNN