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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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
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
Show more
TAKEAWAY SKILLS
Data Visualisation
Python Programming
Data Manipulation
Data Cleaning
Data Plotting
Data Science
Data Statistics
Course Content
38 Lessons1 Project12 Quizzes1 Assessment
1.
Introduction to Time Series
6 Lessons
Get familiar with time series concepts, examples, datasets, and analysis techniques in Python.
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