Python for Time Series Data Analysis
4.6 (3,270 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16,902 students enrolled

Python for Time Series Data Analysis

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!
Bestseller
4.6 (3,270 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16,902 students enrolled
Created by Jose Portilla
Last updated 7/2020
English
English [Auto], French [Auto], 4 more
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  • Spanish [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 15.5 hours on-demand video
  • 3 articles
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 95 lectures 15:21:14
+ Introduction
3 lectures 13:50

Quick Check In!

Course Overview Check
1 question
Course Curriculum Overview
04:08
FAQ - Frequently Asked Questions
02:55
+ Course Set Up and Install
1 lecture 15:54
Installing Anaconda Python Distribution and Jupyter
15:54
+ NumPy
7 lectures 47:04
NumPy Section Overview
00:44
NumPy Arrays - Part One
10:45
NumPy Arrays - Part Two
08:10
NumPy Indexing and Selection
12:16
NumPy Operations
06:46
NumPy Exercises
01:18
NumPy Exercise Solutions
07:05
+ Pandas Overview
10 lectures 01:26:36
Introduction to Pandas
01:10
Series
10:01
DataFrames - Part One
13:24
DataFrames - Part Two
11:09
Missing Data with Pandas
08:26
Group By Operations
05:43
Common Operations
09:21
Data Input and Output
10:18
Pandas Exercises
03:07
Pandas Exercises Solutions
13:57
+ Data Visualization with Pandas
5 lectures 42:06
Overview of Capabilities of Data Visualization with Pandas
01:41
Visualizing Data with Pandas
19:24
Customizing Plots created with Pandas
09:59
Pandas Data Visualization Exercise
03:30
Pandas Data Visualization Exercise Solutions
07:32
+ Time Series with Pandas
12 lectures 01:41:17
Overview of Time Series with Pandas
01:10
DateTime Index
10:19
DateTime Index Part Two
11:48
Time Resampling
12:10
Time Shifting
05:36
Rolling and Expanding
09:39
Visualizing Time Series Data
11:14
Visualizing Time Series Data - Part Two
13:09
Time Series Exercises - Set One
01:25
Time Series Exercises - Set One - Solutions
04:39
Time Series with Pandas Project Exercise - Set Two
04:48
Time Series with Pandas Project Exercise - Set Two - Solutions
15:20
+ Time Series Analysis with Statsmodels
10 lectures 01:28:54
Introduction to Time Series Analysis with Statsmodels
01:21
Introduction to Statsmodels Library
13:19
ETS Decomposition
10:27
EWMA - Theory
04:34
EWMA - Exponentially Weighted Moving Average
14:07
Holt - Winters Methods Theory
09:44
Holt - Winters Methods Code Along - Part One
10:32
Holt - Winters Methods Code Along - Part Two
15:46
Statsmodels Time Series Exercises
02:44
Statsmodels Time Series Exercise Solutions
06:20
+ General Forecasting Models
28 lectures 05:46:18
Evaluating Forecast Predictions
09:03
Introduction to Forecasting Models Part Two
11:20
ACF and PACF Theory
10:16
ACF and PACF Code Along
16:54
ARIMA Overview
13:52
Autoregression - AR - Overview
05:58
Autoregression - AR with Statsmodels
26:43
Descriptive Statistics and Tests - Part One
08:26
Descriptive Statistics and Tests - Part Two
20:45
Descriptive Statistics and Tests - Part Three
07:29
ARIMA Theory Overview
06:14
Choosing ARIMA Orders - Part One
06:38
Choosing ARIMA Orders - Part Two
14:00
ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One
12:32
ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two
26:53
SARIMA - Seasonal Autoregressive Integrated Moving Average
17:50
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE
07:30
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO
22:09
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3
20:39
Vector AutoRegression - VAR
05:58
VAR - Code Along
18:44
VAR - Code Along - Part Two
15:49
Vector AutoRegression Moving Average - VARMA
02:57
Vector AutoRegression Moving Average - VARMA - Code Along
09:26
Forecasting Exercises
02:09
Forecasting Exercises - Solutions
09:01
+ Deep Learning for Time Series Forecasting
13 lectures 02:12:39
Introduction to Deep Learning Section
04:30
Perceptron Model
05:12
Introduction to Neural Networks
06:35
Keras Basics
15:26
Recurrent Neural Network Overview
07:47
LSTMS and GRU
10:11
Keras and RNN Project - Part One
12:10
Keras and RNN Project - Part Two
11:10
Keras and RNN Project - Part Three
25:19
Keras and RNN Exercise
03:59
Keras and RNN Exercise Solutions
13:22
BONUS: Multivariate Time Series with RNN
00:46

Quick check

Quick Check on MultiVariate Time Series Notebook and Data
1 question
BONUS: Multivariate Time Series with RNN
16:12
+ Facebook's Prophet Library
5 lectures 46:25
Overview of Facebook's Prophet Library
03:21
Facebook's Prophet Library
16:37
Facebook Prophet Evaluation
16:14
Facebook Prophet Trend
04:37
Facebook Prophet Seasonality
05:36
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!

Who this course is for:
  • Python Developers interested in learning how to forecast time series data