GROUP 1.
DATA SCIENCE LANGUAGES
PROGRAMMING LANGUAGE
A programming language is a formal language comprising a set of instructions that produce various kinds of output. These languages are used in computer programs to implement algorithms and have multiple applications. There are several programming languages for data science as well. Data scientists should learn and master at least one language as it is an essential tool to realize various data science functions.
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There are two types of programming languages – low-level and high-level.
TYPES
01 / LOW - LEVEL
Low-level languages are relatively less advanced and the most understandable languages used by computers to perform different operations. These include assembly language and machine language. While assembly language deals with direct hardware manipulation and performance issues, a machine language is basically binaries read and executed by a computer. An assembler software converts the assembly language into machine code. Low-level programming languages are faster and more memory efficient as compared to their high-level counterparts.
02 / HIGH - LEVEL
The second type of programming languages provides a stronger abstraction of details and programming concepts. Such high-level languages can create code that is independent of the computer type. Moreover, they are portable, closer to human language, and immensely useful for problem-solving instructions.
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Therefore, many data scientists use high-level programming languages. Those aspiring to enter the field may consider specializing in a data science language to start their journey.
examples of programming languages:
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Python
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JavaScript
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Java
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C/C++
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R
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SQL
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MATLAB
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Scala
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SAS
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Julia
1. Python
Python has the highest popularity among data scientists. This is due to its wide range of uses. It is often the go-to choice for a range of tasks for domains, such as, machine learning, deep learning, artificial intelligence and other popular forms of technology.
These tasks are made easier due to Python’s powerful data science libraries. Some of the more popular libraries include Keras, Scikit-Learn, matplotlib, and tensorflow. Python can also support very important tasks, such as data collection, analysis, modeling, and visualisation which are all key factors to work with in big data. You will never be left without an answer when using Python. This language has a large community for support which is another reason it holds a vital place among the top tools for data science.
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Best used for: Python is best used for automation. Automating tasks is extremely valuable in data science and will ultimately save you a lot of time, and provide valuable data.
Pros/Cons: The biggest pro of Python is it’s popularity among data scientists. This wide popularity means that there is endless support and a lot of resources available to continue your education. It’s wide range of open source tools for visualization and machine learning also make Python extremely useful and popular.
INTERFACE
2. SQL
SQL is a very important language to learn in order to be a great data scientist. It is so important because a data scientist needs SQL in order to handle structured data. SQL gives you access to data and statistics which makes it a very useful resource for data science.
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A database is necessary for data science, thus making using a database language such as SQL a necessity. Anyone dealing with big data will need to have a sound knowledge of SQL in order to query databases.
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Best used for: SQl is the standard and most widely used programming languages for relational databases.
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Pros/Cons: SQL is a non-procedural language, this means that it does not require the use of traditional programming logic. This makes using SQL much easier because you don't have to be an expert coder.
SQL has a difficult interface that can make users uncomfortable when using the database. Some versions of SQL can be very costly and due to hidden business rules, complete control of the database is not always given.
INTERFACE
3. R
R is quickly rising the ranks as one of the most popular programming languages for data science, and for good reason. R is a highly extensible and easy to learn language that fosters an environment for statistical computing and graphics.
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All of this makes R an ideal choice for data science, big data, and machine learning. R is a powerful scripting language. This being so, means that R can handle large and complex data sets. This combined with it’s ever growing community makes it a top tier option for an aspiring data scientist.
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Best used for: R is best used in the world of data science. It is especially powerful when performing statistical operations.
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Pros/Cons: R has numerous pros including being open-source, large amount of support, multiple packages, quality plotting and graphing as well as various machine learning operations.
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The biggest downside of using R is security. R lacks basic security and as such it can not be embedded into a web application.
INTERFACE
4. JUPYTER NOTEBOOK
Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document.
Computational notebooks have been around for decades, but Jupyter in particular has exploded in popularity over the past couple of years.
This rapid uptake has been aided by an enthusiastic community of user–developers and a redesigned architecture that allows the notebook to speak dozens of programming languages — a fact reflected in its name, which was inspired, according to co-founder Fernando Pérez, by the programming languages Julia (Ju), Python (Py) and R.
