Time Series Analysis with Python 3.x

Time Series Analysis with Python 3.x, available at $34.99, has an average rating of 3.6, with 24 lectures, 5 quizzes, based on 17 reviews, and has 78 subscribers.

You will learn about Key pandas concepts and techniques for time-based analysis Study and work with important components of time series data such as trends, seasonality, and noise Apply commonly used machine learning models for analysis How to de-trend and de-seasonlize time series data Manipulate data with AR, MA, and ARMA Decompose time series data into its components for efficient analysis Create an end-to-end anomaly detection project based on time series This course is ideal for individuals who are This course is for anyone interested in time-based data. or If you are a Python developer and want to conduct analysis based on time series data, then this course is for you. It is particularly useful for This course is for anyone interested in time-based data. or If you are a Python developer and want to conduct analysis based on time series data, then this course is for you.

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Summary

Title: Time Series Analysis with Python 3.x

Price: $34.99

Average Rating: 3.6

Number of Lectures: 24

Number of Quizzes: 5

Number of Published Lectures: 24

Number of Published Quizzes: 5

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Key pandas concepts and techniques for time-based analysis
Study and work with important components of time series data such as trends, seasonality, and noise
Apply commonly used machine learning models for analysis
How to de-trend and de-seasonlize time series data
Manipulate data with AR, MA, and ARMA
Decompose time series data into its components for efficient analysis
Create an end-to-end anomaly detection project based on time series

Who Should Attend

This course is for anyone interested in time-based data.
If you are a Python developer and want to conduct analysis based on time series data, then this course is for you.

Target Audiences

This course is for anyone interested in time-based data.
If you are a Python developer and want to conduct analysis based on time series data, then this course is for you.

Time series analysis encompasses methods for examining time series data found in a wide variety of domains. Being equipped to work with time-series data is a crucial skill for data scientists. In this course, you’ll learn to extract and visualize meaningful statistics from time series data. You’ll apply several analysis methods to your project. Along the way, you’ll learn to explore, analyze, and predict time series data.

You’ll start by working with pandas’ datetime and finding useful ways to extract data. Then you’ll be introduced to correlation/autocorrelation time-series relationships and detecting anomalies. You’ll learn about autoregressive (AR) models and Moving Average (MA) models for time series, and explore anomalies in detail. You’ll also discover how to blend AR and MA models to build a robust ARMA model. You’ll also grasp how to build time series forecasting models using ARIMA. Finally, you’ll complete your own project on time series anomaly detection.

By the end of this practical tutorial, you’ll have acquired the skills you need to perform time series analysis using Python.

Please note that this course assumes some prior knowledge of Python programming; a working knowledge of pandas and NumPy; and some experience working with data.

About the Author

Karen J. Yang has been a data engineer, an author, and a passionate computer science self-learner for 7 years. She has 6 years’ experience in Python programming and big data processing. Her recent interests include cloud computing.

She holds a PhD in Political Science from Ohio State University and loves working with data to gather meaningful information by performing analysis and research. This interest led her to publish data analysis research papers on Inferential Data Analysis on Tooth Growth and Predicting Activity for Samsung SensorData. She is also a published author of the ‘Apache Spark in 7 Days’ course.

Course Curriculum

Chapter 1: Setting Up and Learning Ways to Get Data

Lecture 1: The Course Overview

Lecture 2: Installation

Lecture 3: Pandas Operations

Lecture 4: Working with Pandas Datetime

Lecture 5: Getting Data

Chapter 2: Time Series Data and Relationships

Lecture 1: Importing Time Series in Python

Lecture 2: Modelling and Decomposing Time Series Based on Trend and Seasonality

Lecture 3: Approaches to Detrend and Deseasonalize a Time Series

Lecture 4: Correlation: Relationship Between Series

Lecture 5: Autocorrelation: Relationship Within Series

Chapter 3: Operating with Time Series Models

Lecture 1: Stationarity in Time Series

Lecture 2: Autoregression (AR) and Moving Average (MA) Models

Lecture 3: Estimating an AR Model

Lecture 4: Estimating an MA Model

Lecture 5: Building an ARMA Model

Chapter 4: Working with Various ML Models for Time Series Analysis

Lecture 1: How to Work with ML Models for Time Series Analysis

Lecture 2: Time Series Analysis Using Decision Tree

Lecture 3: Analysis Using Random Forest

Lecture 4: Gradient-Boosted for Series Analysis

Lecture 5: Handling Missing Values

Chapter 5: Completing Your Project on Anomaly Detection

Lecture 1: How to Work with Cointegration Models

Lecture 2: Using Granger Causality Test

Lecture 3: Performing Forecasting and Analysis Using ARIMA

Lecture 4: Interpretation of Results

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 4 votes
4 stars: 7 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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