Time Series Analysis with Python

with Viktor Mehandzhiyski

Introducing you to the world of time series and exploring how to utilize Python in order to analyze and model such data. We will also discuss volatility and making forecasts about the future.

9 hours 89 lessons
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89 High Quality Lessons
59 Practical Tasks
9 Hours of Content
Certificate of Achievement

Course Overview

Data Science mainly relies on working with two types of data - cross-sectional and time series. This course will help you master the latter by introducing you to the ARMA, seasonal, integrated, MAX and volatility models, as well as show you how to forecast them into the future.

Topics covered

data analysisData processingProgrammingPythonTheory

What You'll Learn

This course will introduce you to the time series type of data in data science. It will help you master the relevant models and forecasting technologies.

Distinguish between time series and cross-sectional data 
Create a time series object in Python 
Examine and visualize some important types of time series 
Learn the rules of manual model selection 
Work with different time series models 
Make forecasts about the future 


Student feedback


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Pros: 1- The course builds up the concepts of Time Series in a fantasic gradation. 2- The course is split into small digestable chuncks of videos, which makes it easy to follow. Cons: 1- it is a little bit old, as statsmodels library has been updated since the time when the course was recorded, and there is no updated notes about the workarounds in the attached code. 2- Almost no response for students questions.
The Course is overall good but there is forecasting missing. You have presented how to do it, but there is no example of any forecast which has great accuracy. This means, I understand how to forecast but can't forecast with good accuracy. So please add a case where we get the accuracy correct and get the hang of it. But otherwise, I have learned a lot and enjoyed the course.
Very good introduction to time series analysis and forecasting using Python. There is a balance presentation between theoretical part and practical part (python). Hope you are going to have more advance in time series analysis such as multivariate time series analysis (VAR, VECM), non-linear model or non-stationary panel data, etc. in the future.
Brilliant course covers everything as I am a junior analyst working with support team for commodities trading. Crucial information for Quants analysis is given here which is what I needed!
I enjoyed this course since I am working on a project requiring data science skills particularly in time series of a minimum of 7 years. Timely indeed!
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Viktor Mehandzhiyski

“Time series lies at the intersection of two favorite topics of mine - finance and data science. In this course, we will provide you with all the tools you need to analyze sequential data. Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting - you'll find it all here!”

Viktor Mehandzhiyski

Content Creator at 365 Data Science

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Time Series Analysis with Python

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