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