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Intro
Data science is a discipline that combines statistical knowledge with the ability to program and analyze data. To become a data scientist, it is important to learn more than one programming language so that you can choose the one best suited for each problem and context. But which programming languages are most used and in demand by companies in data science? Let's look at them together.
#Python
Python is one of the most popular and widely used languages in the data science world. It is an open source, dynamic, generic, object-oriented language that has been around since 1991. It is also easy to use, read, and write, and has an extensive standard library that offers many features. In addition,
Python has many external libraries dedicated to data science, such as NumPy, SciPy, Pandas, Matplotlib, and Plotly, which make it easy to manipulate, visualize, and model data.
Python is also the language of choice for machine learning and artificial intelligence, thanks to frameworks such as TensorFlow, PyTorch and Scikit-learn.
So we are talking about a versatile, powerful and flexible language that can be used for various applications and purposes in data science. To date, Python is the most widely used language in the AI & Big Data School study tracks of the international Geeks Academy network.
#R
R is another widely used language in data science, especially for statistical and graphical analysis. This programming language was developed by Ross Ihaka and Robert Gentleman in 1993, as a direct descendant of the older S. R is an open source language, offering a large catalog of statistical and graphical methods, as well as an active and collaborative community to help create and share new packages and features. R is ideal for exploring, cleaning, and visualizing data, as well as for testing hypotheses and creating predictive models. R handles algebraic matrices well and has strong data visualization capabilities. R is therefore a rich, comprehensive and specialized language for data science.
#SQL
SQL (Structured Query Language) is a language for defining and managing relational databases, i.e., those databases that organize data into tables connected by logical relationships. SQL has been around since 1974 and has maintained its operating principles over time while evolving with new features and standards.
This query language is very clear and intuitive, allowing databases to be queried to extract, filter, aggregate, and manipulate data in an efficient and scalable manner.
SQL is indispensable for working with large amounts of structured or semi-structured data, which are the basis of many applications and services in data science, and it integrates well with other programming languages, such as Python or R, through dedicated modules.
#Java
Java is a general-purpose, object-oriented, compiled programming language that has been around since 1995. Java is one of the most widely used languages in the world because of its portability across different platforms, its robustness, its security, and its performance. It is used primarily for web, desktop, and mobile application development, but it also has relevance in data science.
In fact, Java offers several advantages for producing ETL (Extract Transform Load) codes, which are those codes that deal with transferring data from a source to a destination after transforming it according to rules.
Worth mentioning is the fact that Java offers several algorithms for machine learning and analysis of distributed data on clusters of machines, thanks to frameworks such as Hadoop or Spark.
#Scala
Scala is a multi-paradigm programming language, combining elements of object-oriented and functional languages. Scala has been around since 2004 and is based on the Java Virtual Machine (JVM), which means it can interact with Java code and take advantage of its libraries. Scala is perfect for data scientists working with large data sets that require efficient memory management and concurrency. Scala works well on both object-oriented approaches and functional programming paradigms, offering concise and expressive syntax. Scala is the primary language for using Spark, one of the most popular frameworks for parallel distributed data processing.
In conclusion, we can say that there is no single perfect language for data science, but that each language has its own strengths and weaknesses depending on the problem to be solved and the context in which it operates. Therefore, a good data scientist should be able to master more than one language and know how to choose the most suitable one in each situation.
Since 2016, Geeks Academy has been leading the way with its #newskilling, #upskilling, and #reskilling pathways in Data Science. Here is a list of the training opportunities: choose your course