Statistics is the backbone of data interpretation and analysis in data science. While working with big data, data scientists employ descriptive statistics and the use of probability through Bayesian methods. The application of tools such as Excel in data analysis helps present data in a format that is easy for viewers to understand. When it comes to data interpretation possibilities and the usage of valuable insights for implementing proper decisions in computer science, linear models and statistical methods are inestimable. Data science statistics provide knowledge vital for comprehending and applying the analysis results to develop efficient tactics.

Important Statistics Concepts in Data Science

Elite Data Science is an online learning portal that provides basic concepts every data scientist should know, including probability distribution, significance, hypothesis testing, and regression.

1. Probability Theory

Probability is a theory in mathematics that deals with the chance or the extent of the occurrence of an event. A random experiment is defined as a physical process whose result is unpredictable before it happens but is easy to know after the process, like flipping a coin. Probability is a numerical value which ranges from zero to one and implies the chance of occurrence of a particular event. The probability value is usually stated on a scale of zero to one, where the higher the probability (the closer to one), the more likely the event. The chance of flipping a coin is 0/1.

2. Descriptive Statistics

These statistics, summaries, and data descriptions, as well as the data visualization, are well carried out. Much raw information is challenging to review, distilling to summary form, not to mention communicating. When estimating descriptive statistics, it is possible to organize the data correctly.

Some key concepts in descriptive statistics cover the normal distribution, also called the bell curve, central tendencies including the mean median and mode, 25%, 75% quartiles, and variance, as well as the standard deviation and modality and measure of skewness and kurtosis.

Statistical Features

Statistical features are the most basic analysis gears scientists employ to study data. The features arrange the data given and determine the most minor and most significant value, the middle value, commonly referred to as the median, and the quartiles. Quartiles demonstrate how much of the data is beneath 25%, 50% and 75%. Mean, Mode, Bias and other simple facts about the statistics are different aspects of the statistical features.

1. Bayesian Statistics 

The International Society for Bayesian Analysis explains the Bayes Theorem: By using the above methodology, the current knowledge regarding the model parameters is indicated by putting a probability distribution on the parameters, called the prior distribution in the Bayesian paradigm.

The prior distribution remains a scientist’s current state of information regarding a specific subject. This new information is stated in terms of the likelihood, which calculates “the product of the probability density of the observed data given the model parameters.” This information is combined with the prior, resulting “in a new probability statement known as the posterior distribution.”

2. Dimensionality Reduction

The University of California Merced states that dimensionality reduction reduces the dimensions of your data set. It achieves this by addressing issues in data sets of considerable sizes that are absent in smaller ones. According to the overlapped contextualization, there are too many variables involved. The more features are added to a data set, the more samples scientists require to have all the feature combinations in the data set. Thus, experimenting becomes more difficult. Reducing dimensionality has several advantages, such as reduced storage requirements and computing time, elimination of redundant data, and development of accurate models.

3. Probability Distributions

The probability distribution based on Investopedia is defined as all possible values of a random variable and the probability of each value ranging from zero to one. Therefore, probability distributions are used in data science to determine the likelihood of specific values and occurrences.

A probability distribution also has shape and other quantified things, including mean, variance, skewness, and kurtosis. The expected value is the mean of the distribution of the given random variable. The variance is the measure of the dispersion of the values of a random variable from the mean. The measure of spread one will come across frequently in future uses is called the standard deviation, which is the square root of variance.

4. Over- and Under-Sampling

Not all the given data sets are equal in size. The technique data scientists use to change the ratio of large-size and small-size data values is called resampling. Over

sampling and sampling. Over-sampling is implemented when the current data is inadequate. Under-sampling is used when a section of the data is over-sampled. Some of the methods used in under-sampling techniques involve the identification of overlapping and redundant data, where only some of the data is used.

Conclusion

Statistics is one of the subsets or steps of the data science process, which gives the critical methodologies and methods needed to convert data into insight. Thus, structuring data collection, cleaning, exploration, inference, modelling, evaluation, and communication is essential when solving real-life business problems and helping organizations make accurate decisions. Thus, constant improvement in statistics for data science and familiarization with new statistical methods are crucial to success in the continuous field of change.