25 Terms Every Data Scientist Should Know

Datascience is also known as data-driven science, the use of scientific methods, processes, and systems to extract knowledge or information from data in a variety of ways, ie structured or unstructured. Machine learning is a type of artificial intelligence system that makes the computer capable of learning alone, that is, without being programmed explicitly.

Why seek a deeper understanding of scientific data by studying a special, or simply want to get an overview of the smart field, mastering the correct term would quickly track your success in travel and career professionals. the education you.

1. Business Intelligence (BI). BI is the process of analyzing and generating historical data reports to guide future decision making. BI helps leaders make better strategic decisions to move forward with determining what has happened in the past using data such as sales statistics and operational metrics.

2. Engineering data. Data engineers build infrastructure through which data is collected, cleaned, stored and prepared for the use of data by scientists. Good engineer is invaluable, and building a team of scientific data without them is the "carriage in front of the horse" approach.

3. Scientific decision. In the context of scientific data, scientists' decisions apply mathematics and technology to solve business problems and increase behavioral science and design thinking (a process that aims to better understand the end user).

4. Artificial Intelligence (AI). The computer's artificial intelligence system can perform tasks that normally require human intelligence. This does not necessarily mean that the replication of the human mind, but implies the use of human reason as a model to provide good service or improve the product, such as voice recognition, decision making and translated language.

5. Learning Machine. A subset of AI, the learning machine, refers to the process by which a system learns from the entered data, identifying patterns in the data that are then applied to the new problems of people or requests. This allows scientists to teach the computer to perform tasks, rather than scheduling them to perform each task step by step. We will use, for example, to understand consumer preferences and buying patterns to recommend products on Amazon or search curricula to identify candidates for the highest potential job based on keywords and phrases.

6. Supervised learning. This special type of machine learning consists of scientific data that act as guides to teach the desired conclusion to the algorithm. For example, the computer learns to identify the animals being trained in the image data correctly defined by each species and the characteristics of the animal.

7. Classification example of a supervised learning algorithm places a piece of new data in a pre-existing category, according to the layout of the category already known. For example, you can use it to determine if you are likely to spend more than $ 20 online for a customer, depending on the similarity with other customers who have surpassed the previous number.

8. The cross-validation method is to validate automatic learning models of stability or accuracy. Although there is a wide variety of cross-validation, one of the most basic is to divide a series of exercises into your training and two algorithms are in one part before applying it to the second part. Because you know what the result should receive, you can evaluate the validity of our model.

9. Grouping is the classification, but without the appearance of the area. With the clustering algorithm that receives the inserted data and finds similarities in the data of the same, categorizing the data points to agree.

10. Deep learning. A more advanced form of machine learning, system learning and refers to multiple input / output layers, as opposed to the surface layer of the input / output. In the study, there are several input / output rounds required to help the team solve complex real-world data problems. A deep dive can be found here.

11. linear regression. of linear regression in the relation between two variables, adjusting a linear equation to the observed data. In doing so, an unknown variable can be predicted from variables related to known. A simple example is the relationship between height and weight of an individual.

12. A / B tests Commonly used in product development, testing, A / B is a randomized test in which to test two variants to determine the best course of action. For example, as it is known, it tried several shades of Google's blue to determine which tone got the most clicks.

13. Hypothesis testing. Test the hypothesis that the use of statistics to determine the probability that the null hypothesis is correct. It is often used in clinical research.

14. Statistical power. Statistical power is likely to make a decision about the right to reject the null hypothesis when the null hypothesis is false. In other words, the probability of the study detecting whether there is an influence effect can be detected. A high statistical power means less likely to erroneously conclude that they have a variable effect.

15. Standard errors. The standard error is a measure of the accuracy of the statistical estimate. A large sample size of standard error decreases.

16. Causal inference is the process of verifying that there is a relationship between cause and effect in a given situation, the purpose of analyzing many data in the social and health sciences. They generally require not only good data and algorithms but also experience in the subject.

17. The exploratory data analysis(EDA). EDA is often the first step in analyzing datasets. With EDA techniques, data scientists can summarize the key features of a dataset and report on the development of a more complex model or the next logical step.

18. The data view. A key component of scientific data, data visualizations that are visual representations of text-based information, and better detection and recognition of patterns, trends and correlations. This helps people understand the meaning of the data by putting it in a visual context.

19. R. R is a programming language and software environment for statistical computation. R language widely used between statistics and data mining for software development and statistical analysis of data.

20. Python is a programming language for the general purpose programming language and is used to manipulate and store data. Many websites are subject to traffic, like YouTube, which is done using Python.

21. SQL. Structured query language or SQL is another programming language used to perform tasks such as updating or retrieving data to a database.

22. ETL. ETL is the type of data integration that refers to three stages (extraction, transformation, loading) to mix data from multiple sources. It is often used to build a data warehouse. An important aspect of data storage is that it consolidates data from multiple sources and transforms it into a common utility format. For example, ETL normalizes data from various departments and business processes to be consistent and standardized.

23. GitHub. GitHub is a code for exchanging and publishing services, as well as a community of developers. It provides access control and many collaboration functions, such as error tracing, resource requests, task management, and wikis for each project. GitHub offers both repositories and private accounts are free, usually used to host the open source project software.

24. Data Models define how datasets are connected to each other and how they are processed and stored inside a system. The data model that shows the structure of a database, even in relation to the limitations of scientific data, helps to understand how data can be stored and manipulated better.

25. Data warehouse. A data warehouse is a repository in which all data collected by an organization is stored in use as a guide to make management decisions.

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