Python For Time Series Data Analysis

Free Download Python For Time Series Data Analysis
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
Python Developers interested in learning how to forecast time series data
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