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Time Series Analysis with Python
Gain insights into time series analysis using Python. Explore pandas and NumPy for data manipulation, visualize trends, learn ARIMA modeling, and employ machine learning for forecasting.
4.7
38 Lessons
2 Projects
10h
Updated 4 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of time series data analysis concepts, such as stationarity, autocorrelation and seasonality
- Working knowledge of Python libraries for time series data analysis, such as Pandas and NumPy
- Hands-on experience analyzing and forecasting time series data using Python
- Ability to use statistical modeling techniques, such as ARIMA, to forecast time series data
- Familiarity with advanced techniques for time series data analysis, such as machine learning algorithms and neural networks
Learning Roadmap
1.
Introduction to Time Series
Introduction to Time Series
Get familiar with time series concepts, examples, datasets, and analysis techniques in Python.
2.
Python Basics for Time Series
Python Basics for Time Series
Walk through Python's pandas, datetime handling, visualization techniques, and outlier treatment.
3.
Time Series Analysis
Time Series Analysis
5 Lessons
5 Lessons
Examine core concepts of trends, seasonal patterns, autocorrelation, and stationarity in time series data.
4.
Basic Time Series Forecasting
Basic Time Series Forecasting
9 Lessons
9 Lessons
Enhance your skills in time series forecasting with concepts like seasonality, moving averages, and SARIMAX.
5.
Advanced Time Series Forecasting
Advanced Time Series Forecasting
4 Lessons
4 Lessons
Take a closer look at advanced time series forecasting techniques like Prophet, LSTM, and Bayesian methods.
6.
Forecast Evaluation
Forecast Evaluation
5 Lessons
5 Lessons
Implement model evaluation, split data, set baselines, and detect forecast drift in time series.
7.
Practical Examples
Practical Examples
3 Lessons
3 Lessons
Master the steps to analyze and forecast energy consumption, weather data, and stock prices.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
This course is an introduction to time series data analysis and forecasting with Python. Time series data is prevalent in many fields, including finance, economics, and meteorology. In this course, you will learn how to use Python's popular pandas and NumPy libraries to manipulate, visualize, and analyze time series data.
The course covers topics such as time series decomposition, stationary and non-stationary data, autocorrelation and partial autocorrelation, and modeling techniques like ARIMA. You will learn how to implement these techniques in Python and how to use them to make forecasts. Moreover, you will also be introduced to some advanced techniques including machine learning algorithms.
By the end of the course, you will have a solid understanding of time series data analysis and forecasting with Python. You will be able to import, clean, and manipulate time series data, use statistical modeling techniques to make forecasts, and apply machine learning algorithms to time series data.
ABOUT THE AUTHOR
Arthur Mello
Data scientist and educator
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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