Top 7 programming languages to learn as a budding data scientist
“The only way to learn a new programming language is by writing programs in it”, Dennis Ritchie.
For any developer, a programming language is their superpower. Now and then, a new programming language or an update in the existing language comes up. These new or updated programming languages endeavours more speed and optimised results. At times, the designers find them intertwined within various programming languages while deciding the language of their respective projects.
The fact is that not all programming languages are the same, and what may work for one project may not be suitable for the other. Data science, being the trendiest domain nowadays, demands efficiency and high-performance results. Let us have a look at the top programming languages to learn as a budding data scientist:
Python is one of the primary and top tools of data scientists. This programming language can be described as the go-to-choice for all the developers. It can cover a range of tasks for various domains, such as Machine Learning, Deep Learning, Artificial Intelligence, etc. Python is an object-oriented programming language that is easy to use and developer-friendly as it has high code readability.
The language possesses a vast ecosystem of rich libraries and is suitable for multiple purposes makes it versatile. Other features of Python are:
Java might look like an old programming language to most of you, but don’t get fooled. It is used by some of the top businesses, as it allows them to secure enterprise development as their preferred development stack. Tools like Hadoop, Spark, Hive, Scala, and Fink have been offered by Java to thunder data science.
It is one of the most popular languages among developers for writing code for distributed systems, catering data analysis, and efficient use in machine learning. Other features of Java are:
For handling the statistical and graphics side of things in Data Science, R is used as primary open-source software. This programming language handles statistical computing and analysis tasks, such as time series analysis, clustering, statistical tests, linear and non-linear modelling, etc.
RStudio and Jupyter, third party interfaces, make it easier to use R. Due to its strong object-oriented nature, R allows other programming languages to modify data objects without much hassle. Other features of R are:
One of the initial programming languages is C. Most of the new programming languages, such as r, use C/C++ as their codebase. To work with C/C++, an individual must possess a better understanding of programming fundamentals.
Due to its low level of nature, C/C++ is considered a complicated programming language among data science beginners. Thus, the language is being used to build data science tools. The core of TensorFlow is written in C++, and the rest of it is in Python. Other features of C/C++ are:
False conclusions because of unclean data can lead to poor decision making and lousy business strategy. Also, this may lead to a lot of embarrassment while communicating the outcome to the team, realising that the data is not anywhere near the analysis.
If you are a programmer, you must have indeed used SQL at some point in your life. SQL serves an essential purpose other than just connecting you to your data. The programming language helps gather the facts and statistics with a few queries from a large set of data.
SQL simplifies the tasks in data science, for example, data pre-processing, etc. Other features of AQL are:
Statistical Analytical System (SAS), an industrial-grade software environment, is built explicitly for predictive analysis, advanced analysis, and business intelligence. Using this programming language, the users can mine, alter, and manage data for advanced statistical analysis. Users can fetch data from multiple sources to process in SAS.
The software environment is then wrecked into sets of tools; these tools offer the data functionalities mentioned above. Some cover presentations, data management, quality control, etc. Code editor and project manager, grid computing manager, etc. are some of the other SAS features.
Each of the languages discussed above and all the other programming languages holds benefits for developers and offer better and faster results. Data science is the trending domain nowadays, exceptionally vast, and demands different tools to cater to different tasks.
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While dealing with data, being acquainted with multiple programming languages can help you to overcome challenges efficiently. If you are a budding data scientist, you can start learning the above mentioned in-demand programming languages.
With LEARNXT, students will get an opportunity to apply data science in real-world situations. LEARNXT will help students to learn the art of creative problem-solving. There are multiple solutions to a challenge, and through experiential learning, students will learn to apply data to seek unique solutions to the task.
Stay tuned to LEARNXT to know more about the top 7 programming languages to learn as a budding data scientist.
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