Time Series Analysis in Python

Name: Time Series Analysis in Python
Brand: Careervira
Price: 62 GBP
Availability: InStock
Rating: 1 (1 reviews)

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DataCamp

Course Report

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Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Many domains can have time series data, such as stock prices or climate data. Data scientists must learn how to effectively work with these data. This course will show you how to perform time series analysis using Python. You will be able identify and use multiple time series models including cointegration, moving average, autoregressive and moving average. These models can also be forecasted, estimated, and simulated using Python's statistical libraries. These models can be used in many applications with a particular emphasis on finance.

Course Overview

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Manipulating Time Series Data in Python

What You Will Learn

In this course you'll learn the basics of analyzing time series data

This course will introduce you to time series analysis in Python

You'll learn about several time series models ranging from autoregressive and moving average models to cointegration models

Course Content

Expand all sections

Module 1: Correlation and Autocorrelation

Module 2: Some Simple Time Series

Module 3: Autoregressive (AR) Models

Module 4: Moving Average (MA) and ARMA Models

Course Instructors

Rob Reider

Consultant at Quantopian and Adjunct Professor at NYU

Rob is an Adjunct Professor at NYU's Courant Institute where he co-teaches a course on Times Series Analysis and Statistical Arbitrage. He is also currently a Consultant to Quantopian. He has been a ...