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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.

Beginner

38 Lessons

10h

Certificate of Completion

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.
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This course includes

1 Project
1 Assessment
73 Playgrounds
12 Quizzes
Course Overview
What You'll Learn
Course Content
Apply Your Skills
Recommendations

Course Overview

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 l...Show More
This course is an introduction to time series data analysis and forecasting with Python. Time series data is prevalent in many f...Show More

TAKEAWAY SKILLS

Data Visualisation

Python Programming

Data Manipulation

Data Cleaning

Data Plotting

Data Science

Data Statistics

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
An understanding of time series data analysis concepts, such as stationarity, autocorrelation and seasonality

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Course Content

1.

Introduction to Time Series

6 Lessons

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

2.

Python Basics for Time Series

5 Lessons

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

3.

Time Series Analysis

5 Lessons

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

4.

Basic Time Series Forecasting

9 Lessons

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

5.

Advanced Time Series Forecasting

4 Lessons

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

6.

Forecast Evaluation

5 Lessons

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

8.

Practical Examples

3 Lessons

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

10.

Conclusion

1 Lessons

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

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