GARCH Models in R
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
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Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
In this course youll learn how to use data science for several common marketing tasks.
Predict employee turnover and design retention strategies.
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Learn to create animated graphics and linked views entirely in R with plotly.
Learn to detect fraud with analytics in R.
Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Learn the fundamentals of valuing stocks.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Learn to analyze and model customer choice data in R.
Learn defensive programming in R to make your code more robust.
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Use C++ to dramatically boost the performance of your R code.
Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.