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Advanced Data Analysis with Haskell


Advanced Data Analysis with Haskell

Advanced Data Analysis with Haskell

Advanced Data Analysis with Haskell
HDRips | MP4/AVC, ~211 kb/s | 1280x720 | Duration: 04:04:39 | English: AAC, 128 kb/s (2 ch) | 643 MB
Genre: Development / Programming
Learn advanced data analysis techniques to gain insights into real-world data sets using Haskell.


-Visualize and harvest information from data
-Understand Regression analysis, perform multivariate regression, and untangle different varieties of clusters
-Explore the power of non-strict semantics, strong static typing, and control constructs and make data analysis simpler

Every business and organization that collects data is capable of tapping into its own data to gain insights on how to improve. Haskell is a purely functional and lazy programming language that is well suited to handling large data analysis problems. This video picks up where Beginning Haskell Data Analysis takes off. This video series will take you through the more difficult problems of data analysis in a conversational style.

You will be guided on how to find correlations in data, as well as multiple dependent variables. You will be given a theoretical overview of the types of regression and we'll show you how to install the LAPACK and HMatrix libraries. By the end of the first part, you'll be familiar with the application of N-grams and TF-IDF.

Once you've learned how to analyze data, the next step is organizing that data with the help of machine learning algorithms. You will be briefed on the mathematics and statistical theorems such as Baye's law and its application, as well as eigenvalues and eigenvectors using HMatrix.

By the end of this course, you'll have an understanding of data analysis, different ways to analyze data, and the various clustering algorithms available. You'll also understand Haskell and will be ready to write code with it.







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Advanced Data Analysis with Haskell

Advanced Data Analysis with Haskell


Advanced Data Analysis with Haskell

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