Learn to speak like a Data Scientist by knowing the data science terms
In order to be able to work in the field of Data Science, you first must understand some of the common terms. Digital Workshop Center has compiled some of the most commonly used data science vocabulary used in the field to help you get started:
- Algorithm: a set of rules used to make a calculation or solve a problem.
- Artificial intelligence – the ability of machines to use logic and advanced computation to learn and make decisions similar to humans
- Classification: a prediction method that assigns each data point to a predefined category, e.g., a type of operating system.
- Data lake – a low-cost, reliable storage solution that’s accessible from anywhere
- Database – a system where data can be stored, accessed, and transformed
- Data warehouse – the place where companies mine data for business intelligence
- Feature: also known as an independent variable or a predictor variable, a feature is an observable quantity,recorded and used by a prediction model. You can also engineer features by combining them or adding new information to them.
- Heteroscedasticity, heteroscedastic data – data that moves quickly and changes often
- Image recognition – the use of computers to understand, identify and classify objects
- Machine learning – an application of computer science to train machines using advanced models to understand patterns in data
- Model: a mathematical representation of a real world process; a predictive model forecasts a future outcome based on past behaviors.
- Natural language processing – the use of computers to interpret and respond to spoken human language
- Overfitting: a situation in which a model that is too complex for the data has been trained to predict the target. This leads to an overly specialized model, which makes predictions that do not reflect the reality of the underlying relationship between the features and target.
- Regression: a prediction method whose output is a real number, that is, a value that represents a quantity along a line. Example: predicting the temperature of an engine or the revenue of a company.
- Target: in statistics, it is called the dependent variable; it is the output of the model or the variable you wish to predict.
- Test set: a data set, separate from the training set but with the same structure, used to measure and benchmark the performance of various models.
- Training: the process of creating a model from the training data. The data is fed into the training algorithm, which learns a representation for the problem and produces a model. Also called “learning.”
- Training set: a dataset used to find potentially predictive relationships that will be used to create a model.
- Visualization – a method to understand the value of data visually and intuitively
Data Science Tools
In addition to the terms used in data science, it is important to know what tools are commonly used today. According to a poll by Business Broadway of data science professionals, “the top used tool in 2017 was Python (60% of respondents said they used this in the previous year), followed by R (46%) and SQL (42%). The top 10 tools are rounded out by TensorFlow, Amazon Web Services, Unix shell / awk, Tableau, C/C++, NoSQL and MATLAB/Octave.”
To help you understand the differences, here are some quick definitions of data science tools:
Python is a general purpose programming language and has many uses other than data science. This means you don’t need to know every aspect of Python to find success in the data science field. Python is also a high-level programming language, which means that is one of the more effective programs in terms of computer use. It is simple, user-friendly, and easy to understand. In addition, Python handles different data structures very well and has powerful statistical and data visualization libraries.
Bank of America uses Python to crunch financial data. Facebook turns to the Python library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications. In short, Python has incredible potential and the power of what it can do is still growing.
“R” is considered the gold standard of languages within the data science world. The entire language was built with data and analysis in mind. The popularity of “R” keeps growing because of its ease of use and how it is built to work with statistics. Python and R are considered rivals in some way, but it is crucial for any serious data scientist student to be able to understand and use both languages.
Some of the most popular features within R include how it handles data visualization, data wrangling, specificity, and machine learning.
Standard Query Language or “SQL” is the most popular language to retrieve, update, or delete information from a database. A variety of established database products support SQL, including products from Oracle and Microsoft. It is widely used in both industry and academia, often for enormous, complex databases.
In a distributed database system, a program often referred to as the database’s “back end” runs constantly on a server, interpreting data files on the server as a standard relational database. Programs on client computers allow users to manipulate that data, using tables, columns, rows, and fields. To do this, client programs send SQL statements to the server. The server then processes these statements and returns result sets to the client program.
For any work using data, a deep understanding of SQL is essential.
Go farther with Data Science
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