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Data Science Anywhere
Experience project-based learning to master Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, and ARIMA models.
Updated on Sep, 2026
Programming, Programming Languages, Python
Duration - 7.5 hours
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Time Series Analysis with Python course will help you learn to work with a number of Python libraries, providing you with complete training. You will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.
The course starts with programming in Python which is the essential skill required and then we will explore the fundamental time series theory to help you understand the modeling that comes afterward.
Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills.
This course covers all types of modeling techniques for forecasting and analysis. With these tools, we will master the most widely used models out there:
Multiplicative Model
AR (autoregressive model)
Simple Moving Average
Weighted Moving Average
Exponential Moving Average
ARMA (autoregressive-moving-average model)
ARIMA (autoregressive integrated moving average model)
Master basic to advanced Time Series methods.
Learn auto-regressive methods,
Learn Time Series Visualization in Python.
ARMA, ARIMA, SARIMA in Python.
Auto ARIMA in Python.
Basic knowledge of Statistics.
Basic understanding of Python.
Should have a Gmail Account and should be able to open Google Drive.
Check out the detailed breakdown of what’s inside the course
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