All companies are going data-driven, and the technology is accessible to everyone, data science and data engineers who are ready to adopt the latest techniques and skills and start using data science tools like Python or R to analyze data and find insights.
Although there are many data science platforms, the most popular ones used by analysts include Jupyter Notebooks and Raspberry Pi.
Data Scientists is the future
There has been a rapid increase in the amount of data, and with the new tools, it is possible to analyze this vast amounts of information.
If companies were to look ahead in the future, they would see data science and data engineers as specialists.
Rather than using traditional methods, data scientists and data engineers will use their understanding of mathematics and statistics to examine the data, develop models, and then use data science tools such as Python and R to analyze and build the models.
In order to do that, it’s important to be up-to-date on the latest techniques in data science. In this article, we’ll take a look at a few of the most popular technologies in data science.
Data science and data engineering are highly popular today, and there is a significant number of programmers who are interested in using these technologies. In fact, the data-science space is so saturated that people are now using different platforms to learn data science and data engineering.
The most popular platform used by data scientists and data engineers is R. R is a very powerful programming language that was first designed in 1988. R’s main language features include mathematical operations (like multiplication, division, and addition), string manipulation, relational-data manipulation, and visualization.
R is distributed, and uses the Havok engine, which is capable of native compression. R is distributed because it allows data scientists and data engineers to work in different time zones.
If you’re interested in learning R, the Academy of R is a great place to start.
ActionScript & XHTLM
ActionScript is a scripting language that has been used for many high-profile video games.
XHTML (which stands for Extensible Markup Language) is a format used for representing documents in HTML (Hypertext Markup Language).
This format is widely used on the Web today, but was originally developed by Tim Berners-Lee, one of the inventors of the Web, and others.
Python - Pandas
If you’re interested in learning Python, a popular data-science language, consider trying out Pandas. Pandas is a free Python package for data exploration.
The Pandas package contains mathematical functions, and features a windowing tool. You can use Pandas to query data in different ways, or to import and export data.
If you’re looking to learn Hadoop, perhaps the most popular Hadoop platform, then consider trying out Hive.
Hive is a popular open source Hadoop data-planning and analysis platform that can be used in data-science environments.
Hive is designed for interactive data analysis and processing.
The languages below are the most popular ones used for data science. If you’re interested in learning these languages, you can do so by reading the following links:
There are more than 25 million Java developers in the world, and according to Stack Overflow, about 7 million of them use it for data analysis. Java is a multi-paradigm language that makes it ideal for processing large amounts of data, for example, by using parallel programming.
Python is a general-purpose, interpreted programming language that runs on all the major operating systems. It is easy to learn and easy to program in, and has many tools available to its users.
The name says it all! R is used mainly by researchers. It is an interpreted language that makes data analysis simpler and faster. Matlab: Matlab is a versatile scientific computing and graphics application. It provides a wide range of powerful tools such as standard statistical and plotting functions, as well as scientific notation and graphics. It runs on both Macintosh and Windows machines and requires a fully-licensed copy.
Mathematica is a scientific computing and mathematical modeling application. It provides powerful tools to build, simulate, analyze, visualize, and analyze various data sets.
Thank you for reading!