, Distribution Fitting
, Free Throws
, Credit Score
When we need to discover the information hidden in vast amounts of data, or make smarter decisions to deliver even better products, data scientists hold the key to the answers you need.
Basic familiarity with the programming language used on the job is a prerequisite for quickly getting up to speed.
Every programmer should be familiar with data-sorting methods, as sorting is very common in data-analysis processes.
Cauchy distribution is the distribution of the ratio of two independent normally distributed Gaussian random variables. As one of the most widely used distributions, it is important for all Data Scientists to be familiar with it.
Exponential distribution is the probability distribution that describes the time between events in a process in which events occur continuously and independently at a constant average rate. As one of the most widely used distributions, it is important for all Data Scientists to be familiar with it.
Normal distribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known. As one of the most widely used distributions, it is important for all Data Scientists to be familiar with it.
Binomial distribution is the discrete probability distribution of the number of successes in a sequence of independent yes/no experiments, each of which yields success with a given probability.
Probability theory is the foundation of most statistical and machine-learning algorithms.
An important concept, p-value is defined as the probability of obtaining a result equal to or "more extreme" than what was actually observed, when the null hypothesis is true.
Data aggregation is the process of gathering and summarizing information in a specified form. It is a common component of most statistical analysis processes.
Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring within a fixed interval of time and/or space, if these events occur with a known average rate and independently of the time since the last event. As one of the most widely used distributions, it is important for all Data Scientists to be familiar with it.
An important Data Science algorithm, the k-nearest neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression.