Popular Programming Languages for Data Analytics
More and more data means a greater demand for data analysts. With the ever-increasing volume of data that businesses produce and amass, the ability to extract useful information from this mountain of information is becoming increasingly in demand.
Getting these kinds of responses and insights is crucial for businesses of all types to make better decisions. Data analytics are used in various industries, including business-to-business and business-to-consumer trade, healthcare, manufacturing, and marketing.
Decision-making informed by data is at the heart of data analytics. Gathering, organizing, and evaluating data allows you to determine the best method for achieving a target.
Jobs in data analytics are here to stay because solving complicated problems always requires multiple people. Although businesses are making strides toward automating data analytics, only around 20% of the role has been digitized. Even that portion still needs to be automated.
Data science and data analytics are two different but related fields. Whether you’re just starting or have years of experience, a data analytics certification can help you take the next step in your career.
Work in data analytics is becoming increasingly vital and exciting. Data scientists typically have strong mathematics, statistics, and computer programming backgrounds. Numerous computer languages exist, C/C++ and Java, explicitly tailored to data analysis.
Programming language choice makes a difference in data analytics:
Any developer would be powerless without a programming language. A new programming language or an upgrade to an existing language is released periodically to provide faster and more optimal results. It’s not uncommon for developers to feel overwhelmed by the options available when picking which programming language to use for their next project.
In computer programming, no two languages are alike; what serves one project or set of requirements well may not fit another. The need for speed and high-performance outcomes is particularly acute in emerging fields like data science. Let’s discuss a few of the best programming languages in this post.
Most popular languages for data analytics:
Today, developers can choose from over 700 distinct languages. Different languages are better suited to various tasks, such as online games vs. software development or the analysis of large amounts of data. Therefore, data analysts must reevaluate their requirements and consider their context to select the most appropriate programming language.
- Python
Python is a free and open-source general-purpose programming language with many uses outside the data science business, including web and game design. Python can perform any data science job you can throw at it. It is primarily due to its extensive library selection. Python’s vast user base means that it has many application packages that you can use for everything from basic data manipulation to deploying complex machine learning and deep learning models.
Python is typically rated as one of the simplest programming languages to learn and use for newcomers because of its clear and concise syntax.
- R
With a firm grasp of this widely used open-source programming language of the computing environment, you can enjoy one of the best data visualization and analysis opportunities available. Companies with a global presence, such as Mozilla and ANZ, have found great success with R’s modern data processing capabilities.
- Scala
Using the Java Virtual Machine, Scala is a high-level programming language that can facilitate your work with Java. Scala is ideal for developing high-performance Data Science frameworks like Hadoop due to its inherent concurrency support. The combination of Scala with Spark is powerful enough to manage massive amounts of disparate data.
- SQL
Structured Query Language is the abbreviation for SQL. As far as data analytics languages go, this one is indispensable. The course prepares students for SQL. Data analytics requires a database, making familiarity with SQL essential. Developers working with big data should be proficient in SQL.
- SaS
SAS, the “Statistical Analytical System,” is an enterprise-level platform developed for statistical modeling, forecasting, and other sophisticated business analysis forms. In addition to its complex statistical analysis, you may use SAS for data mining, transformation, and management.
The software environment is separated into individual tools to achieve these capabilities. Among these are tools for managing presentations, data, and quality, a code editor, a project manager, a grid computing administrator, and a few others.
- STATA
Changing the command arguments or creating a do-file provides great customization for data analysis with these command-based statistical programs. Additionally, the programming language maintains a minimal structure, making it simple to pick up so that users can fully pay attention to statistical modeling.
- MATLAB
MATLAB is an environment for doing advanced numerical computations in mathematics. It includes many tools for manipulating matrices, displaying data and functions, and much more.
You can solve any complex mathematical or statistical problem with MATLAB’s help. Linear algebra and numerical analysis courses benefit significantly from their incorporation into the curriculum.
- Apache Spark
Best part? It allows programmers to communicate with an open-source community to analyze streaming data. You can share information on mobile, web, and app platforms.
Professional path
The post discussed top Data Science programming languages. Each language offers better and faster results than others. Data Science is a large field that often requires specialized tools. Multiple programming languages can assist you in tackling data difficulties. If you’re a budding Data Scientist, start with the languages above because they’re in demand.
Consider your long-term job ambitions when studying a programming language. Python is a more general-purpose language than R. R is ideal for statistical calculations and data visualization in data analysis. Python is preferable if you want to work with big data, AI, and deep learning methods. Same if you’re interested in programming, development, or other computer science fields.
Next steps
The Simplilearn online courses are an excellent approach to establishing your foundation through videos, exams, interactive laboratories, and portfolio-ready projects. If you are interested in beginning a career as a data analyst, you should enroll in these courses. Both can be finished in a time frame of less than half a year. Earning the professional certificate offered by Simplilearn provides a foundation for learning a statistical programming language within the larger context of data analysis.