Python, R or SQL?.If you are passionate about the world of data analysis and data science, sooner or later you will be faced with a very important decision: which programming language to learn? This constantly changing field offers a wide range of options, but three languages stand out for their popularity and versatility: Python, R and SQL.
Python has become the go-to language for data analysis due to its clear and concise syntax, its large user community and its wide variety of specialized libraries (such as NumPy, Pandas and Scikit-learn) that facilitate tasks such as data manipulation, machine learning and visualization.
R is the language par excellence for statistics and data visualization. Its statistical approach and powerful graphical tools (such as ggplot2) make it an ideal choice for researchers and data scientists looking to explore and visualize data in depth.
SQL is the standard language for interacting with relational databases. Although it is not a generalist programming language, it is essential for any data analyst who needs to extract, transform and analyze large volumes of information stored in databases.
Which one should you learn first? The choice will depend on your interests and will. Python is an excellent starting point thanks to its versatility and popularity. SQL is complementary and will allow you to work with databases efficiently.
R can be an interesting option if you are interested in delving deeper into statistical analysis and data visualization.
In short, Python, R and SQL are the three pillars of modern data analysis. Mastering these languages will open the doors to a world of opportunities in this ever-growing field.
ORIGINS AND EVOLUTION OF PROGRAMMING LANGUAGES.
The R programming language has its roots in the S language, developed by Bell Laboratories in the United States in the 1970s. S stood out as a powerful tool for statistical analysis and data visualization, but its use was limited due to intellectual property restrictions.
Aware of S’s potential and looking for a more accessible alternative, a group of researchers at the University of Auckland in New Zealand undertook the task of creating a free and open source implementation. This ambitious project, initiated in the early 1990s, culminated in the birth of R in 1993.
R quickly became an attractive alternative for the scientific and statistical community. Being open source, it allowed developers around the world to contribute to its growth and improvement, giving rise to a wide range of packages and libraries that extend its capabilities.
Python was born in 1989 by Guido van Rossum, who was looking for a programming language that was easy to use and learn. The first public version was released in 1991 and quickly gained popularity.
Over the years, Python has evolved significantly. Versions such as 1.0 and 2.0 introduced major improvements in data handling and syntax. However, the transition to Python 3.0 was more complex due to substantial changes in the language.
Despite the changes, Python has remained one of the world’s most popular programming languages. Its active and constantly growing community has contributed to its development and success.
In short, Python is a versatile and easy-to-learn programming language that has come a long way since its inception. Its original creator, Guido van Rossum, although no longer at the helm of the project, left a legacy that continues to inspire millions of programmers around the world.
SQL was born in the 1970s as a way to interact with databases in a simpler and more structured way. Based on the relational data model, SQL allows querying, modifying, and managing information stored in tables.
Broadly speaking, its history can be divided into:
Beginnings: Created by IBM as SEQUEL, it evolved to become the SQL standard we know today.
Popularization: Its ease of use and versatility made it the most widely used language for relational databases.
Standardization: Organizations such as ANSI and ISO established standards to ensure compatibility between different database management systems.
Evolution: SQL has continued to evolve and adapt to new technologies, maintaining its relevance in the data world.
In short, SQL is the fundamental language for working with relational databases. Its history is a reflection of the evolution of data management and its importance in today’s digital world.
MASTERING DATA: USES OF R, Python and SQL IN DATA ANALYSIS.
In today’s world, data is the new oil. To extract value from this valuable raw material, data scientists turn to a set of powerful tools: programming languages. Among them, R, Python and SQL stand out for their versatility and ability to address a wide range of analytical challenges.
Below, we will explore in detail the most common uses of each of these languages.
R PROGRAMMING LANGUAGE:
The most common uses for this language are:
– Data analysis: Exploratory analysis, statistical calculations and hypothesis testing can be performed.
– Statistical modeling: In addition, it is used to fit statistical models to data, including linear regression, logistic regression and survival analysis, among others.
– Data Visualization: We have at our disposal libraries and packages specialized in graph generation and data visualization, which facilitates the creation of statistical graphs and interactive visualizations.
– Machine Learning: Presents a wide range of packages and algorithms for machine learning, which are used to train predictive models and perform classification tasks, regression and more.
– Bioinformatics: It is also used for genomic and bioinformatics data analysis; this is so because it has statistical tools and packages specialized in this field.
– Text analysis and natural language processing (NLP): Packages can be used to analyze and process text, perform text mining and other related analyses.
Thanks to its large community of users and developers, this programming language is being used in a wide variety of applications in different fields, from scientific research to industry, medicine and business.
Python PROGRAMMING LANGUAGE:
The main uses of Python are:
– Data analysis and Big Data: Its ease of use and the wide variety of data processing libraries make it an ideal tool for analyzing data in real time. It is currently the programming language most used by companies, either directly or indirectly, because behind the different data analytics software is this programming language. It specializes in analyzing a large volume of data to convert it into useful information.
– Data mining: is a procedure that analyzes large amounts of data in order to predict future trends. During this procedure, Python can help through data cleaning, data organization and the use of Machine Learning algorithms.
– Data Science: It is overtaking Matlab (language used by scientists to analyze large amounts of data) due to new numerical engines such as “Pandas” or “Numpy”. It stands out for its simplicity and its ability to work with a large amount of data.
– Artificial Intelligence: Its simplicity and robustness have made Python an ideal partner for developing Artificial Intelligence (AI). The possibility of programming complex ideas in a few lines, added to the existing high frameworks, has caused Python to be one of the programming languages that are driving innovation with Artificial Intelligence.
– Blockchain: This distributed database works very well with Python (the basis on which cryptocurrencies are based). As a versatile and fast language, it manages to create a simple blockchain in less than 50 lines of code, simplifying something very complex.
– Machine Learning: Machine learning is one of the new technologies that is changing the world as we know it. Robotics and AI with this technology are able to learn as they process data; in this way they are able to make appropriate decisions based on the relevant information they obtain.
– Web development: Thanks to Python, complex web development can be done with fewer lines of code, making them lighter and more optimized. Currently, Django is one of the most popular frameworks of this language, used to create more dynamic and secure websites. It can also be used for scraping, that is, extracting information from other websites, as Netflix does.
– Games and 3D graphics: It is ideal for handling 3D graphics due to the large number of existing frameworks and tools. One of the most popular games developed in Python is Battlefield 2, a 2005 war action game in which the graphics engine, animations and its various functionalities were developed with Python.
SQL PROGRAMMING LANGUAGE:
The most common industry applications are:
– Data analysis: analyzing large amounts of information from databases is one of SQL’s abilities, obtaining valuable insights and making good decisions.
– Application development: It is commonly used in business and industrial applications, such as inventory management systems, among others.
– Process automation: It is also used to automate repetitive tasks and simplify business processes, such as report generation and record updating.
– Database management: It is a fundamental language for the management of large corporate databases; it allows adding, updating and deleting information efficiently and securely.
– Systems integration: Different systems and applications can be integrated, with the will to achieve greater efficiency and a combined view of data.
Advantages of the different programming languages in data science.
R PROGRAMMING LANGUAGE:
Handling large data sets.
It is designed to handle large amounts of data sets. Using specific packages and techniques, R can process and analyze large amounts of data efficiently, which makes it the perfect candidate for Big Data applications.
Data visualization.
You have at your disposal a large variety of tools and libraries specially designed for charting and data visualization. It is ideal for generating interactive and captivating visualizations that help to better understand patterns and trends in the data.
Active community.
Behind R there is a large and very active community of users and developers who support this language. Thanks to this community, new packages and tools are developed, which benefits R and makes it really interesting for data analysis, providing innovative solutions.
Integration with other tools.
It easily integrates with other programming languages such as Python and with databases, making it a versatile tool for data analysis.
Python PROGRAMMING LANGUAGE:
It is undoubtedly the favorite programming language and the most used, let’s see its advantages:
Simplified and fast.
Python’s syntax is quite close to “natural” human languages, being friendly and clear. Its simplicity makes it one of the easiest programming languages to learn and is one of the best alternatives to get started in programming.
Elegant and flexible.
Python offers you many tools; using them it is not necessary to declare each data type if, for example, you want to program lists of several data types. You don’t have to worry so much about the details.
Tidy and clean.
The order you keep in Python is one of the most appreciated by its users, it is very easy to read, any other programmer can read it and work on the program written in Python. The modules are well organized, unlike other languages.
Compatibility with operating systems.
Python is very versatile, as it can run on Windows, macOS, Linux and Unix. For this reason, no matter which alternative you like the most, you can be sure that you will be able to count on this programming language on any platform.
Large community.
It has a large community that cares about the development of this language and almost all updates are done in a democratic way.
SQL PROGRAMMING LANGUAGE:
Faster query processing.
A large amount of data is retrieved quickly and efficiently. In addition, operations such as insertion, deletion and data manipulation are also performed in a short time.
No coding skills.
For data retrieval, we do not need a large amount of code. All the basic keywords such as SELECT, INSERT INTO, UPDATE, etc. are used, and moreover, the syntax rules are not complex in SQL, which makes it a simple language to use.
Standardized language.
SQL provides a uniform platform for all users around the world, due to documentation and long establishment over the years.
Portable.
It has versatility and can be used in programs on computer, server, laptops, regardless of any platform (operating system, etc.). It can also be integrated with other applications as needed.
Interactive language.
Easy to learn and understand, answers to more complex queries can be received in seconds.
Challenges of different programming languages in data science.
R PROGRAMMING LANGUAGE
When used for Big Data, we must take into account the following challenges:
*Memory consumption: it can require a large amount of memory to handle Big Data sets, which can limit its scalability in resource-constrained environments.
*Processing speed: It is efficient for statistical analysis and modeling tasks, but it is not as fast as other programming languages from a processing speed point of view in Big Data environments.
Python PROGRAMMING LANGUAGE
*Hosting. Most servers do not have Python support, and if they do, configuration is a bit difficult.
*Included libraries. Some libraries that come by default are not to the liking of most of the community and they choose to use third-party libraries.
*High memory consumption. The RAM memory available for the performance of a computer is limited, so we must know how much the programs we use consume. Unfortunately, Python has a very high consumption due to the flexibility of the data types handled.
*It is not optimal for mobile apps. Python has many limitations for the development of mobile applications. For this reason, it is difficult to see apps created with this programming language in Apple’s AppStore or Google’s PlayStore.
SQL PROGRAMMING LANGUAGE
*Complex interface. It has a complex interface, causing discomfort to users.
*Cost. Some versions are expensive and this makes it more difficult for programmers to access them.
*Partial control. Complete control is not provided to the database because of hidden business rules.
Popular library and packages.
R PROGRAMMING LANGUAGE
You have at your disposal a wide range of libraries and packages that allow users to perform specific tasks. The most popular ones are:
→ “ggplot2”: creation of high quality and visually appealing graphs.
→ “dplyr”: Efficient data processing and manipulation.
→ “caret”: Training and evaluation of machine learning models.
→ “tidyverse”: set of packages that facilitate data manipulation and visualization.
Python PROGRAMMING LANGUAGE
The most commonly used Python libraries are:
→ “Matplotlib”: high quality data processing and graph generator. In addition, you have facility to publish this data physically (under print) and also digitally.
→ “Seaborn”: Specialized in statistical data visualization, it stands out for its high-level interface, which is attractive and very interactive. In addition, it is compatible with other Python libraries such as Pandas and Matplotlib.
→ “Numpy”: It allows you to create a universal data structure to make your analysis and exchange of various algorithms easier.
→ “SciPy”: You can create numerical routines with data structure; it is specialized in numerical computation and data analysis.
→ “Pandas”: It is one of the most useful libraries and is focused on scientific data. Its most important functions are data series and data frame for two dimensions.
→ “Scikit-Learn”: It contains Machine Learning and data analysis functions. It is easy to use due to its multiple machine learning techniques.
→ “TensorFlow”: It was developed by Google, you can perform numerical calculations thanks to data flow diagram and it is used for various scientific calculation applications such as Deep Learning.
→ “PySpark”: The simplicity and flexibility of Python mixed with the speed and scalability of Spark make it an ideal choice for any company. It also facilitates real-time processing of streaming data.
→ “Shap: It is useful for understanding and explaining how Machine Learning models make decisions. It supports a wide range of Machine Learning models.
SQL PROGRAMMING LANGUAGE
SQL does not use libraries in the same sense as other languages. SQL functionalities are integrated into the language and database management systems (DBMS).
There are tools and extensions: Some DBMSs offer additional functions or stored procedures that can be thought of as “libraries” or “packages”. These are usually designed for specific tasks, such as advanced data analysis, geoprocessing or integration with other tools.
Example of a PostgreSQL extension:
PostGIS: This is an extension for PostgreSQL that allows storing and analyzing geographic data. It provides spatial data types, spatial indexes and functions to perform geometric operations.
Summary
In summary, Python, R and SQL are indispensable tools in any data scientist’s arsenal. Each language offers a unique set of features and capabilities that make it ideal for different tasks. By understanding the strengths and weaknesses of each, you can select the language best suited for your projects. The active community behind these languages ensures a bright future for data science. So where will you begin your journey into the world of data analytics?
Bibliography:
https://smowl.net/es/blog/lenguaje-r/
https://edgarjayo.wordpress.com/2020/05/02/historia-de-r-y-s/
https://keepcoding.io/blog/lenguajes-de-los-data-scientists/
https://bookdown.org/jboscomendoza/r-principiantes4/un-poco-de-historia.html
https://aws.amazon.com/es/what-is/python
https://aws.amazon.com/es/what-is/sql
https://www.grupoatrium.com/actualidad/los-8-principales-usos-de-python/
