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Time Series Analysis with Python

Name: Time Series Analysis with Python
Price: 59 USD
Author: Arthur Mello

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.

Created by Arthur Mello

What's included

38 lessons across 8 modules
12 quizzes with instant feedback
in preconfigured environments
with scoring rubrics
on every lesson

Join 3.1 million developers at

What you'll learn

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

38 Lessons1 Project12 Quizzes1 Assessment

Introduction to Time Series

Get familiar with time series concepts, examples, datasets, and analysis techniques in Python.

Course Contents

Time Series Analysis

Examples of Time Series Data

Get Time Series Datasets

Time Series Properties

Quiz: Introduction to Time Series

Python Basics for Time Series

Walk through Python's pandas, datetime handling, visualization techniques, and outlier treatment.

pandas

Working with Date and Datetime Data

Visualizing Time Series Data

Outliers

Quiz: Python Basics for Time Series

Time Series Analysis

5 Lessons

Examine core concepts of trends, seasonal patterns, autocorrelation, and stationarity in time series data.

Basic Time Series Forecasting

9 Lessons

Enhance your skills in time series forecasting with concepts like seasonality, moving averages, and SARIMAX.

Time Series Forecasting with Prophet in Python

Project

Premium

Advanced Time Series Forecasting

4 Lessons

Take a closer look at advanced time series forecasting techniques like Prophet, LSTM, and Bayesian methods.

Forecast Evaluation

5 Lessons

Implement model evaluation, split data, set baselines, and detect forecast drift in time series.

Time Series Analysis and Forecasting

Assessment

Practical Examples

3 Lessons

Master the steps to analyze and forecast energy consumption, weather data, and stock prices.

Final Project

Project

Conclusion

Wrap up your understanding of key time series concepts and forecasting methods.

Wrap Up

Time Series Analysis

Mock Interview

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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

Learn more about Arthur

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