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Python for Time Series Data Analysis is a tutorial from Udemy that teaches you how to use Python, Pandas, Numpy, and Statsmodels to predict and analyze time series data. A time series is a sequence of random variables sampled at fixed intervals. This course will show you all the tips for using Python to analyze time series and introduce you to other essential tools.

At the beginning of the course, you\’ll learn how to work with and edit data with the NumPy and Pandas libraries and the Python language, and then learn how to visualize and work with time data. You then start learning about the Statsmodels library and learn about its time series analysis tools. The tutor at the end will also teach you how to build predictive models and introduce you to deep learning techniques.

Courses taught in this course:

  • Use Pandas to edit data
  • Use NumPy and Python for numerical processing
  • Use Pandas to visualize
  • Working with time series data
  • Using Statsmodels for time series analysis
  • Introduction to ARIMA models

Python for Time Series Data Analysis course specifications:

  • English language
  • Duration: 15 hours and 21 minutes
  • Number of courses: 95
  • Level of education: Intermediate
  • Tutor: Jose Portilla
  • File format: mp4

Course headings

95 lectures 15:21:14

3 lectures 13:50

Course Set Up and Install
1 lecture 15:54

7 lectures 47:04

Pandas Overview
10 lectures 01:26:36

Data Visualization with Pandas
5 lectures 42:06

Time Series with Pandas
12 lectures 01:41:17

Time Series Analysis with Statsmodels
10 lectures 01:28:54

General Forecasting Models
28 lectures 05:46:18

Deep Learning for Time Series Forecasting
13 lectures 02:12:39

Facebook\’s Prophet Library
5 lectures 46:25

1 lecture 00:10

Course prerequisites

  • General Python Skills


Python for Time Series Data Analysis

Sample movie

Installation guide

View with your favorite Player after Extract.

Subtitle: No

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 549 MB

File password (s):


1.53 GB

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