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30-Day Money-Back Guarantee

This course includes:

  • 15.5 hours on-demand video
  • 3 articles
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Programming Languages Time Series Analysis

Python for Time Series Data Analysis

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!
Bestseller
Rating: 4.6 out of 54.6 (4,130 ratings)
21,619 students
Created by Jose Portilla
Last updated 7/2020
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

11 sections • 95 lectures • 15h 21m total length

  • Preview06:47
  • Course Overview Check
    1 question
  • Course Curriculum Overview
    04:08
  • FAQ - Frequently Asked Questions
    02:55

  • Installing Anaconda Python Distribution and Jupyter
    15:54

  • 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

  • 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

  • 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

  • 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

  • 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

  • Preview03:42
  • Preview13:21
  • 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

  • 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 on MultiVariate Time Series Notebook and Data
    1 question
  • BONUS: Multivariate Time Series with RNN
    16:12

  • 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

Featured review

Neelabh Pant
Neelabh Pant
27 courses
7 reviews
Rating: 5.0 out of 5a year ago
Jose is a good teacher and has explained concepts very well. There are some lectures (like Prophet) where he could have spent more time but knowing it is such a new topic it was a good effort and I definitely learnt a lot. So thanks Jose

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 698,236 Reviews
  • 2,139,288 Students
  • 31 Courses

  Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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