How Can You Choose The Right Programming Language For Data Science?
Data Science has made its mark among the most popular programming languages of this era. In a rapidly growing tech-heavy industry, the demand for data science professionals is only increasing. If you are looking for a data science career, programming expertise is a necessity apart from analytical and mathematical skills.
However, before you zero in on your choices for the programming language required for a job, you need to know about the various types of the programming language you can pursue a course in to become an expert in data science.
A highly popular and dynamic programming language, Python is extremely prevalent among data science enthusiasts. It is also among the easiest languages to master, and its capacity to sync with Fortran or C algorithms only increases its demand among your professionals. Additionally, as data science, machine learning, predictive analysis, and artificial intelligence make ints foray into regular jobs, demand for professionals skilled in Pyhton is constantly increasing. If your interests lie in data mining, scientific computing or w development, Python is what you need to learn.
If you have completed a basic course in data science and now want to excel in a particular language that helps you with statistically oriented jobs, R is your best option. This might be slightly difficult to master as compared to Python, however, if the statistical analysis is your calling, R is your key. However, R is less of a general-purpose language used for programming, hence, you should pursue R only if you are interested in statistics and data analysis. The additional advantage though is, R can help you deal with linear algebra, even complex ones.
A mandate for any skilled data scientist, SQL or more commonly known as Structured Query Language, retrieves data from organized data sources and is the most used database language. SQL manipulates, updates and researches into existing databases. Any expert data science would require to pull out and analyze data from the database; this is exactly where your knowledge of SQL will fit the bill. Also, owing to its simple syntax, SQL is among the most readable languages in data science.
If your interest lies more in learning a general-purpose language, Java is your answer. Supported by Oracle, Java is a unique computing system that makes migrating between platforms easier. Also, Java is widely used among organizations to create and launch mobile or web applications. If you are a skilled software engineer, developing engineer or software architect, Java will help you make the most of learning programming stack.
Next on the list is Scala, highly popular as a programming language with an immense user database. If you are interested or have to eventually wok with data sets that are really heavy and high on volume, Scala will help you nail the functional bit along with the strong static type bit as well. Scala is an open-source and general programming language, that can be operated within Java or JVM itself. Scala is your best option when it comes to working with processor clusters and Java codes.
Very similar to R in terms of usage, SAS is also used for statistical analysis, though unlike R it is not an open-source programming language. Noted to be among the oldest language used for statistics, SAS is highly reliable and often finds its use in predictive modeling, business intelligence, and complex analytics. Organizations keen on using a secure and stable platform for their analytical needs mostly use SAS since it offers a variety of packages that help in statistical analysis and machine learning.
While learning any of the above-mentioned programming languages will help you make the most of your data science career, if you are more enthusiastic and want to climb the career ladder faster, it is always advisable to go for more than one language. This not only gives you flexibility while changing jobs but also makes you a skilled professional.