Data Science foundation

Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization.

As such, it incorporates skills from computer science, mathematics, statistics, information visualization, graphic design, complex systems, communication and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human-computer interaction: users should be able to intuitively control and explore data.

In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities

Comments

Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also refer...

What is difference between "inplace = True" and "inplace = False?

Both inplace= true and inplace = False are used to do some operation on the data but: When inplace = True is used, it performs operation on data and nothing is returned. df.some_operation(inplace=True) When inplace=False is used, it performs operation on data and returns a new copy of data. df = df.an_operation(inplace=False)

Levenshtein distance

In information theory, linguistics and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965. Levenshtein distance may also be referred to as edit distance, although that term may also denote a larger family of distance metrics known collectively as edit distance. It is closely related to pairwise string alignments.

Data Bizx

Search This Blog