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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Time series includes a variety of methods for figuring out time-series data that are available in different domains. If you want to be a data scientist or belong to this field, then you must have grips on time series analysis. In this time series analysis with python 3.x online program. You will learn industry-required crucial data extracting and visualizing understandable information from time-series data. You will explore many methods to complete your project with the greatest output. If you enroll in this vital online course in Python time series. Then you will master this specialization in 6 months. And by the end of the course, you will have complete knowledge about time series analysis with python. 

Why should you enroll in the python time series course?

If you want to learn Pandas for data visualization and manipulation.

  • Want to add on new and updated skill to your CV
  • Ready to gain some industrial-level concepts like Numpy and python numerical processing.
  • If you are willing to understand and use python time series concepts effectively in your project.
  • And want to learn some other advanced topics about time series with python then you must take this course.

So, this is a powerful but easy-to-understand online program. That helps you to build up and master time-series analyzing concepts in detail. Thus, if you want to be a data scientist or an existing professional, you should enroll in this course. And those students who want to be data scientists. Or want to learn about the python time series at an affordable rate. Then this course is for them as well.

Course Content

Total: 23 lectures
  • Installation
  • Pandas Operations
  • Working with Pandas Datetime
  • Getting Data
  • Importing Time Series in Python
  • Modelling and Decomposing Time Series Based on Trend and Seasonality
  • Approaches to Detrend and Deseasonalize a Time Series
  • Correlation: Relationship Between Series
  • Autocorrelation: Relationship Within Series
  • Stationarity in Time Series
  • Autoregression (AR) and Moving Average (MA) Models
  • Estimating an AR Model
  • Estimating an MA Model
  • Building an ARMA Model
  • How to Work with ML Models for Time Series Analysis
  • Time Series Analysis Using Decision Tree
  • Analysis Using Random Forest
  • Gradient-Boosted for Series Analysis
  • Handling Missing Values
  • How to Work with Cointegration Models
  • Using Granger Causality Test
  • Performing Forecasting and Analysis Using ARIMA
  • Interpretation of Result


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