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.
This course is an introduction to time series data analysis and forecasting with Python. Time series data is prevalent in many f...Show More
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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TAKEAWAY SKILLS
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.
7.
Practical Examples
3 Lessons
Master the steps to analyze and forecast energy consumption, weather data, and stock prices.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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