<p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <h1>Style and Approach</h1> <p>Each video guides you through the journey of a problem, a mathematical definition of the problem, an algorithmic approach to solving the problem, and the detailed Haskell approach to solving that problem. Each video builds a little on the video before it and at a conversational pace. We use the Jupyter notebook system that allows us to easily create and share notebooks of our analysis work. You can download the same notebooks that were created in each of our videos.</p>