Many decades ago, data scientists were not on many radars. But they have gained immense popularity in today’s technology-driven era. This sudden popularity can be linked to the opinion businesses now hold about big data. It would be fair to say that many data scientists started their careers as data analysts or statisticians. But as the concept of big data started growing and evolving, their roles also evolved, because data is no longer an afterthought that is handled by the IT department. Data has now become key information owing to the large amounts of usable data that are generated by organizations that can be analyzed effectively to provide strategic business patterns.
In this article, I will be giving you a fair overview of the best programming languages for data scientists.
Understanding the Term ‘Data Science’
Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset.
Who is a Data Scientist?
So, what does a data scientist exactly do? A data scientist or data expert is one who gathers and analyzes both structured and unstructured data. He combines statistics, mathematics, and computer science to process, analyze, and model data. They then interpret the results to create actionable plans. Apart from having sound technical skills, data scientists must also be effective communicators, team members, leaders, and analytical thinkers, as they often exist in business settings across all industries and are charged with making data-driven decisions and communicating complex ideas.
Best Programming Languages Data Scientists Can Use
Let us now list down the top programming languages that will help data scientists to a great extent in their careers.
Python, as most of us are aware, is a high-level programming language that is versatile and easy to use. It is one of the most popular choices for data scientists owing to the vast array of useful libraries and a smooth learning curve. The code-readability offered by Python is one more feature that makes it a popular choice. As a data scientist tackles complex problems on a daily basis, it is essential that he uses a language that is easy to understand. Solutions can be implemented easily with Python as it has dedicated libraries such as Matplotlib, Numpy, Pandas, scikit-learn, etc. to help solve data science problems involving preprocessing, analysis, predictions, visualization, and data preservation.
This programming language is highly popular among statisticians. It is a language that is dedicated to statistical analysis. But, as compared to Python, data scientists may have to face a steep learning curve. If you wish to develop a profound knowledge of statistics and data analytics, R is the language of your choice. R is suitable for all statistical applications, as it has more than 10,000 packages in the open-source repository of CRAN. R can also handle complex linear algebra. So, it can be used for statistical analysis as well as neural networks. RStudio, an R-based environment, makes it easier to connect databases. Sparklyr and Tidyverse are other studio packages that provide Apache Spark interface to R. R also has a built-in package called ‘RMySQL’ which offers native connectivity of R with MySQL. These are some of the notable features that make R an ideal choice for hard-core data scientists.
Often known as the ‘meat and potatoes of Data Science,’ it is viewed as an essential skill that every data scientist must possess knowledge of. Structured Query Language (SQL) is the database language used for retrieving data from organized data sources named as relational databases. Data scientists use SQL for querying, updating, and manipulating databases. It is vital for a data scientist to know how to retrieve data. Though it offers limited capabilities, it is regarded as the left-arm of data scientists as it is highly crucial for specific roles. A data scientist must know how to extract and wrangle data from the database. For this purpose, data science experts must have a sound understanding of SQL.
Data science has transformed into one of the most prominent and popular technologies of the 21st century. There is a high demand for data scientists across all industries. If you are an aspiring data scientist who wants to know more about data science certifications online and aim to become a data science expert, check out Global Tech Council.