Bayesian statistics is a system for describing epistemological uncertainty using the mathematical language of probability. In the 'Bayesian paradigm,' degrees of belief in states of nature are specified; these are non-negative, and the total belief in all states of nature is fixed to be one.  

As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.  

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Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics.

Deep learning (also known as deep structuredlearning or hierarchical learning) is part of a broader family of machine learning methods based on artificialneural networks. Learning can be supervised, semi-supervised or unsupervised. Deep Learning Discussions: Reinforcement Learning, GAN, CNN, LSTM, Embedding, Transfer Learning, Regularization, Dropout, etc.  

A list of discussions of the mathematical background necessary to get up and running in data science practical/research work. Discussions: differential equations, Optimizations, Integral, Logics, Metric (topology), Linear Algebra, etc.  

Qualitative analysis uses subjective judgment based on unquantifiable information. Qualitative analysis deals with intangible and inexact information that can be difficult to collect and measure. Machines struggle to conduct qualitative analysis as intangibles can't be defined by numeric values. Discussions: Coding, Sampling, Interview, FGD, Content Analysis, Narrative Analysis, Discourse Analysis, Framework Analysis, Grounded Theory, etc.  

Statistical methods are a key part of of data science, yet very few data scientists have adequate proper understanding of statistics concepts. This would results in incorrect interpretations/decisions and other disaster in the real life situations. Discussions: p-value, correlations, metric/distance, descriptive statistics, hypothesis testing, distributions, evaluations, hybrid/ensemble methods, data transformation, kernel, and much more.

Data visualization is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data.^[1] To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.^[2] Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. Discussions: Geometry, Distance, UMAP/t-SNE, pyplot/seaborn/matplotlib, MDS, unfolding/manifolds, etc.