Data Science, Data Engineering, Machine Learning, Deep Learning…. Do you know what these terms mean and what’s the difference between each one of them? Below we have chosen the 15 most frequently used Data Science terms and we will briefly explain to you what each term means.
1. Data Science
Data Science is a field that combines programming skills and knowledge of mathematics and statistics to derive insights from data. In short: Data Scientists work with large amounts of data, which are systematically analyzed to provide meaningful information that can be used for decision making and problem solving. A Data Scientist has a high level of technical skills and knowledge, usually with expertise in programming languages such as R and Python. They help organizations collect, compile, interpret, format, model, predict, and manipulate all types of data in a wide variety of ways.
Algorithms are repeatable sets, usually expressed mathematically, of instructions that humans or machines can use to process given data. Typically, algorithms are constructed by feeding them data and adjusting variables until the desired result is achieved. Thanks to breakthrough developments in Artificial Intelligence, machines now typically perform this task of combining, as they can do it much faster than a human.
3. Data Analytics
Data Analytics involves answering questions generated for better business decision-making. Existing information is used to determine usable data. Data analysis is an ongoing process in which data is collected and analyzed continuously. An essential component of ensuring data integrity is the accurate evaluation of research results.
4. Data mining
Data mining is the process of sorting large data sets to identify patterns and relationships that can help solve business problems. Data mining techniques and tools can be used to predict future trends and make more informed business decisions. Data mining is a component of Data Analysis and one of the core disciplines of Data Science.
The data mining process can be divided into these four main stages:
identify and assemble relevant data for an analytics application. The data may be located in different source systems that contain a mix of structured and unstructured data.
The data exploration
stage includes a set of steps to get the data ready to be mined. It summarizes the steps of data exploration, profiling, and pre-processing, followed by data cleansing work to fix errors and other data quality issues.
Now it is time to implement one or more algorithms to do the mining/modeling
. In Machine Learning applications, the algorithms typically must be trained on sample data sets.
On to the application of deploying the models
and communicating the findings to business executives and users, often through Data Visualization and the use of data storytelling.
5. Big Data
The term "Big Data" has emerged as an ever-increasing amount of data has become available. Today's data differs from that of the past not only in the amount but also in the speed at which it is available. It is data with such large size and complexity that none of the traditional data management tools can store it or process it efficiently.
Big data benefits:
6. Artificial intelligence (AI)
- Big Data can produce more complete answers, because you have more information
- More precisely defined answers through confirmation of multiple data sources
The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans which goes almost as far as an imitation. John McCarthy also offers the following definition: "It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."
7. Machine Learning
Machine Learning is a technique that allows a computer to learn from data without using a complex set of different rules. It is a subset of AI in which algorithms learn from historical data to predict outcomes and uncover patterns. It's also the process that drives many of the services we use today - recommendation systems like those from Netflix, YouTube, and Spotify; search engines like Google; social media feeds like Facebook and Twitter; voice assistants like Siri and Alexa, etc. With each click or other activity, you give a machine learning material to further process into information, which it can use to make a highly educated decision on what to show you next.
8. Deep learning
Deep Learning is a Machine Learning technique inspired by the neural network of our brain. It gives machines the ability to find even the smallest patterns in a data set with many layers of computational nodes working together to search through data and deliver a final result in the form of a prediction.
Natural language processing (NLP) is an intersection between the fields of Computer Science, linguistics, and Artificial Intelligence. It helps computers communicate with people in their language and perform other language-related tasks. NLP enables computers to read text, listen to the speech, interpret speech, and determine which parts are important. The goal is to create the widest possible communication between humans and computers via speech. This should enable both machines and applications to be controlled and operated by natural language.
Python is one of the most popular programming languages today, however, it is best known as a versatile language that allows it to be very useful for analyzing data. The language creators focused on making a language that is easy to learn and user-friendly, therefore it is also a very common first programming language to learn. Furthermore, the easily understandable syntax of Python allows for quick, compact, and readable implementation of scripts or programs, in comparison with other programming languages.
For many reasons, the fastest-growing programming languages globally: its ease of learning, the recent explosion of the Data Science field, and the rise of Machine Learning. Python also supports Object-Oriented and Functional Programming styles, which facilitate building automated tasks and deployable systems. There are plenty of Python scientific packages for Data Visualization, Machine Learning, Natural Language Processing, and more.
R is an open-source implementation of the statistical programming language S which was developed at Bell Labs in the 1970s. Most of its underlying source code has been written in C and Fortran. R allows its users to manipulate R objects from these languages as well (including C++) for computationally intensive tasks. It is essentially a highly extensible and flexible environment for performing statistical computations and data analysis.
R is the language of choice for statistical analysis, which is a very important feature in Data Science. R’s popularity comes from the fact that most statistical methods developed in research environments lead to the production of ready-to-use freely available R packages. R’s popularity has led Microsoft to develop Microsoft R Open: The Enhanced R, Distribution, and Oracle to develop Oracle R Enterprise. From our partner companies, we have learned that along with Python, R remains the language of choice for Data Scientists in the insurance and pharmaceutical sectors.
SQL (Structured Query Language) is the language to query and manipulate data in RDMS (Relational Database Management Systems) and is, for this reason, very relevant in the field of Data Science. RDMS are columns and rows to store data within a structured format and are a potent tool to store massive amounts of information. Some common database management systems that use SQL are: Sybase, Oracle, Microsoft SQL Server, Access, etc.
13. NumPy & Pandas
NumPy is the fundamental package for Scientific Computing with Python, adding support for large, multi-dimensional arrays, along with an extensive library of high-level mathematical functions. Pandas is a library built on top of NumPy for data manipulation and analysis. The library provides data structures and a rich set of operations for manipulating numerical tables and time series.
14. Web Scraping
Web scraping pulls data from the source code of a website. This requires a script that identifies the information a user wants and transfers it to a new file. Usually, software that simulates human browsing on the Internet is used for this purpose to collect specific information from various websites. Web scraping is also referred to as web data extraction, screen scraping or web harvesting.
APIs provide users with a set of functions used to interact with the features of a specific service or application. Facebook, for example, provides developers of software applications with access to Facebook features through its API. By hooking into the Facebook API, developers can allow users of their applications to log in using Facebook, or they can access personal information stored in their databases.
We hope our glossary helps you navigate through all these terms in Data Science. If you want to learn more about Data Science, check out our Data Science Bootcamp
where you will learn everything needed to become a professional Data Scientist.