Curated learning path for Time Series Analysis. Build practical skills through expert-selected courses.
Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Follow these courses in order to complete the learning path. Click on any course to enroll.
This online diploma course provides an in-depth understanding of forecasting fashion trends and making predictions for collections. It covers macro and micro trends, the influence of digital platforms and AI, and how to use quantitative data for trend analysis.
This course introduces you to working with time series data, starting from basics like trends and seasonality and moving to advanced forecasting techniques. It covers both classical statistical models and modern machine learning approaches, including deep learning architectures like RNNs and LSTMs.
A highly respected and comprehensive online textbook on time series forecasting that covers a wide range of topics with examples in R.
Learn the basics of time series analysis in Python, including concepts like autocorrelation, stationarity, and how to use the statsmodels library for modeling and forecasting.
Learn the fundamentals of time series analysis using the R programming language, covering data manipulation, visualization, and modeling.
A comprehensive skill track that covers various aspects of time series analysis in Python, from manipulation and visualization to statistical modeling and machine learning.
This free course for beginners covers the basics of sentiment analysis using Python, including text pre-processing, vectorization, and modeling. It has a high number of learners.
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