Last updated 7/2020

MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Language: English | Size: 6.20 GB | Duration: 15h 21m

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!

What you’ll learn

Pandas for Data Manipulation

NumPy and Python for Numerical Processing

Pandas for Data Visualization

How to Work with Time Series Data with Pandas

Use Statsmodels to Analyze Time Series Data

Use Facebook’s Prophet Library for forecasting

Understand advanced ARIMA models for Forecasting

Requirements

General Python Skills (knowledge up to functions)

Description

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.We’ll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we’ll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.Then we’ll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.Afterwards we’ll get to the heart of the course, covering general forecasting models. We’ll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.Afterwards we’ll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.This course even covers Facebook’s Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.So what are you waiting for! Learn how to work with your time series data and forecast the future!We’ll see you inside the course!

Overview

Section 1: Introduction

Lecture 1 Course Overview – PLEASE DO NOT SKIP THIS LECTURE

Lecture 2 Course Curriculum Overview

Lecture 3 FAQ – Frequently Asked Questions

Section 2: Course Set Up and Install

Lecture 4 Installing Anaconda Python Distribution and Jupyter

Section 3: NumPy

Lecture 5 NumPy Section Overview

Lecture 6 NumPy Arrays – Part One

Lecture 7 NumPy Arrays – Part Two

Lecture 8 NumPy Indexing and Selection

Lecture 9 NumPy Operations

Lecture 10 NumPy Exercises

Lecture 11 NumPy Exercise Solutions

Section 4: Pandas Overview

Lecture 12 Introduction to Pandas

Lecture 13 Series

Lecture 14 DataFrames – Part One

Lecture 15 DataFrames – Part Two

Lecture 16 Missing Data with Pandas

Lecture 17 Group By Operations

Lecture 18 Common Operations

Lecture 19 Data Input and Output

Lecture 20 Pandas Exercises

Lecture 21 Pandas Exercises Solutions

Section 5: Data Visualization with Pandas

Lecture 22 Overview of Capabilities of Data Visualization with Pandas

Lecture 23 Visualizing Data with Pandas

Lecture 24 Customizing Plots created with Pandas

Lecture 25 Pandas Data Visualization Exercise

Lecture 26 Pandas Data Visualization Exercise Solutions

Section 6: Time Series with Pandas

Lecture 27 Overview of Time Series with Pandas

Lecture 28 DateTime Index

Lecture 29 DateTime Index Part Two

Lecture 30 Time Resampling

Lecture 31 Time Shifting

Lecture 32 Rolling and Expanding

Lecture 33 Visualizing Time Series Data

Lecture 34 Visualizing Time Series Data – Part Two

Lecture 35 Time Series Exercises – Set One

Lecture 36 Time Series Exercises – Set One – Solutions

Lecture 37 Time Series with Pandas Project Exercise – Set Two

Lecture 38 Time Series with Pandas Project Exercise – Set Two – Solutions

Section 7: Time Series Analysis with Statsmodels

Lecture 39 Introduction to Time Series Analysis with Statsmodels

Lecture 40 Introduction to Statsmodels Library

Lecture 41 ETS Decomposition

Lecture 42 EWMA – Theory

Lecture 43 EWMA – Exponentially Weighted Moving Average

Lecture 44 Holt – Winters Methods Theory

Lecture 45 Holt – Winters Methods Code Along – Part One

Lecture 46 Holt – Winters Methods Code Along – Part Two

Lecture 47 Statsmodels Time Series Exercises

Lecture 48 Statsmodels Time Series Exercise Solutions

Section 8: General Forecasting Models

Lecture 49 Introduction to General Forecasting Section

Lecture 50 Introduction to Forecasting Models Part One

Lecture 51 Evaluating Forecast Predictions

Lecture 52 Introduction to Forecasting Models Part Two

Lecture 53 ACF and PACF Theory

Lecture 54 ACF and PACF Code Along

Lecture 55 ARIMA Overview

Lecture 56 Autoregression – AR – Overview

Lecture 57 Autoregression – AR with Statsmodels

Lecture 58 Descriptive Statistics and Tests – Part One

Lecture 59 Descriptive Statistics and Tests – Part Two

Lecture 60 Descriptive Statistics and Tests – Part Three

Lecture 61 ARIMA Theory Overview

Lecture 62 Choosing ARIMA Orders – Part One

Lecture 63 Choosing ARIMA Orders – Part Two

Lecture 64 ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part One

Lecture 65 ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part Two

Lecture 66 SARIMA – Seasonal Autoregressive Integrated Moving Average

Lecture 67 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART ONE

Lecture 68 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART TWO

Lecture 69 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART 3

Lecture 70 Vector AutoRegression – VAR

Lecture 71 VAR – Code Along

Lecture 72 VAR – Code Along – Part Two

Lecture 73 Vector AutoRegression Moving Average – VARMA

Lecture 74 Vector AutoRegression Moving Average – VARMA – Code Along

Lecture 75 Forecasting Exercises

Lecture 76 Forecasting Exercises – Solutions

Section 9: Deep Learning for Time Series Forecasting

Lecture 77 Introduction to Deep Learning Section

Lecture 78 Perceptron Model

Lecture 79 Introduction to Neural Networks

Lecture 80 Keras Basics

Lecture 81 Recurrent Neural Network Overview

Lecture 82 LSTMS and GRU

Lecture 83 Keras and RNN Project – Part One

Lecture 84 Keras and RNN Project – Part Two

Lecture 85 Keras and RNN Project – Part Three

Lecture 86 Keras and RNN Exercise

Lecture 87 Keras and RNN Exercise Solutions

Lecture 88 BONUS: Multivariate Time Series with RNN

Lecture 89 BONUS: Multivariate Time Series with RNN

Section 10: Facebook’s Prophet Library

Lecture 90 Overview of Facebook’s Prophet Library

Lecture 91 Facebook’s Prophet Library

Lecture 92 Facebook Prophet Evaluation

Lecture 93 Facebook Prophet Trend

Lecture 94 Facebook Prophet Seasonality

Section 11: BONUS SECTION: THANK YOU!

Lecture 95 BONUS LECTURE

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