#### Section 1 : Introduction

 Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 2 About Certification Pdf Lecture 3 Course Curriculum Overview 4:2 Lecture 4 About Proctor Testing Pdf

#### Section 2 : Course Set Up and Install

 Lecture 5 Installing Anaconda Python Distribution and Jupyter 15:48

#### Section 3 : NumPy

 Lecture 6 NumPy Section Overview 0:38 Lecture 7 NumPy Arrays - Part One 10:40 Lecture 8 NumPy Arrays - Part Two 8:4 Lecture 9 NumPy Indexing and Selection 12:11 Lecture 10 NumPy Operations 6:40 Lecture 11 NumPy Exercises 1:13 Lecture 12 NumPy Exercise Solutions 7:0

#### Section 4 : Pandas Overview

 Lecture 13 Introduction to Pandas 1:4 Lecture 14 Series 9:55 Lecture 15 DataFrames - Part One 13:18 Lecture 16 DataFrames - Part Two 11:3 Lecture 17 Missing Data with Pandas 8:21 Lecture 18 Group By Operations 5:37 Lecture 19 Common Operations 9:15 Lecture 20 Data Input and Output 10:12 Lecture 21 Pandas Exercises 3:0 Lecture 22 Pandas Exercises Solutions 13:52

#### Section 5 : Data Visualization with Pandas

 Lecture 23 Overview of Capabilities of Data Visualization with Pandas 1:35 Lecture 24 Visualizing Data with Pandas 19:18 Lecture 25 Customizing Plots created with Pandas Lecture 26 Pandas Data Visualization Exercise 3:24 Lecture 27 Pandas Data Visualization Exercise Solutions 7:25

#### Section 6 : Time Series with Pandas

 Lecture 28 Overview of Time Series with Pandas 1:4 Lecture 29 DateTime Index 10:15 Lecture 30 DateTime Index Part Two 11:42 Lecture 31 Time Resampling 12:5 Lecture 32 Time Shifting 5:30 Lecture 33 Rolling and Expanding 9:33 Lecture 34 Visualizing Time Series Data Lecture 35 Visualizing Time Series Data - Part Two 13:3 Lecture 36 Time Series Exercises - Set One 1:18 Lecture 37 Time Series Exercises - Set One - Solutions 4:33 Lecture 38 Time Series with Pandas Project Exercise - Set Two Lecture 39 Time Series with Pandas Project Exercise - Set Two - Solutions 15:14

#### Section 7 : Time Series Analysis with Statsmodels

 Lecture 40 Introduction to Time Series Analysis with Statsmodels 1:14 Lecture 41 Introduction to Statsmodels Library 13:12 Lecture 42 ETS Decomposition 10:20 Lecture 43 EWMA - Theory 4:27 Lecture 44 EWMA - Exponentially Weighted Moving Average 14:1 Lecture 45 Holt - Winters Methods Theory 9:38 Lecture 46 Holt - Winters Methods Code Along - Part One 10:21 Lecture 47 Holt - Winters Methods Code Along - Part Two 15:40 Lecture 48 Statsmodels Time Series Exercises 2:38 Lecture 49 Statsmodels Time Series Exercise Solutions 6:13

#### Section 8 : General Forecasting Models

 Lecture 50 Introduction to General Forecasting Section 3:36 Lecture 51 Introduction to Forecasting Models Part One 13:14 Lecture 52 Evaluating Forecast Predictions 8:55 Lecture 53 Introduction to Forecasting Models Part Two 11:13 Lecture 54 ACF and PACF Theory 10:11 Lecture 55 ACF and PACF Code Along 16:49 Lecture 56 ARIMA Overview 13:45 Lecture 57 Autoregression - AR - Overview 5:53 Lecture 58 Autoregression - AR with Statsmodels 26:37 Lecture 59 Descriptive Statistics and Tests - Part One 8:21 Lecture 60 Descriptive Statistics and Tests - Part Two 20:39 Lecture 61 Descriptive Statistics and Tests - Part Three 7:23 Lecture 62 ARIMA Theory Overview 6:7 Lecture 63 Choosing ARIMA Orders - Part One 6:31 Lecture 64 Choosing ARIMA Orders - Part Two 13:54 Lecture 65 ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One 12:25 Lecture 66 ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two 26:46 Lecture 67 SARIMA - Seasonal Autoregressive Integrated Moving Average 17:45 Lecture 68 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE 7:24 Lecture 69 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO 22:2 Lecture 70 SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3 20:32 Lecture 71 Vector AutoRegression - VAR 5:52 Lecture 72 VAR - Code Along Lecture 73 VAR - Code Along - Part Two 15:43 Lecture 74 Vector AutoRegression Moving Average - VARMA 2:51 Lecture 75 Vector AutoRegression Moving Average - VARMA - Code Along 9:20 Lecture 76 Forecasting Exercises 2:2 Lecture 77 Forecasting Exercises - Solutions 8:55

#### Section 9 : Deep Learning for Time Series Forecasting

 Lecture 78 Introduction to Deep Learning Section 4:24 Lecture 79 Perceptron Model 5:6 Lecture 80 Introduction to Neural Networks 6:29 Lecture 81 Keras Basics 15:21 Lecture 82 Recurrent Neural Network Overview 7:41 Lecture 83 LSTMS and GRU 10:6 Lecture 84 Keras and RNN Project - Part One 12:5 Lecture 85 Keras and RNN Project - Part Two 11:4 Lecture 86 Keras and RNN Project - Part Three 25:18 Lecture 87 Keras and RNN Exercise Lecture 88 Keras and RNN Exercise Solutions 13:17

#### Section 10 : Facebook's Prophet Library

 Lecture 89 Overview of Facebook's Prophet Library 3:16 Lecture 90 Facebook's Prophet Library 16:30 Lecture 91 Facebook Prophet Evaluation 16:8 Lecture 92 Facebook Prophet Trend 4:32 Lecture 93 Facebook Prophet Seasonality 5:31