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A data science job requires programming knowledge. Data science mostly uses Python programming language. These are some ideas data science job seekers usually come across. Most of the opinions on the internet revolve around these ideas, which are only partial truths. Search for 'most desirable data science skills' only to find Python as one of the top skills required for data science. Indeed, Python, as a programming language has ruled the data science world ever since it was developed. This doesn't mean learning Python alone would be sufficient to land a data science job. The reason might be on the part of the project's requirement with respect to Python's features or the aspirant's programming capability – depending on python would be like putting all the eggs in one basket. Python, the popular language which is presumed to be indispensable for a data scientist is losing its ground to other programming languages. A data science project goes through different stages from data extraction to data modelling to model deployment. Python though is a higher-level language with data-centric libraries and easy-to-read syntax, it cannot perform the tasks efficiently at all the stages. The newcomers include SQL, R, Scala, Julia, etc with benefits like better Cloud Native performances and the ability to run on modern hardware, etc.
SQL comes into the picture when we look at how much and where the companies store data. For a successful database analysis, the data should be retrieved simultaneously from servers, which Python lags way behind when compared to the query language, SQL. No wonder SQL though holds equal importance appears trailing Python in the list of required skills. SQL is used for data retrieval which is an essential step for even getting started with the project. Employers look for people who are multitaskers within the data science domain adept at basic skills because most part of data science project involves gathering and cleaning data. Perhaps this is the reason why SQL has ranked higher than Python in the Stack overflow survey. SQL syntax comes in different formats which companies use according to the demands of the project. MySQL, and SQL Server, are a few of them, you need to give a try.
R was the most popular language for data science application in 2015-16 overtaken by Python in the last 2 to 3 years. R is more for seasoned pros for it is coded heavy and has a steep learning curve. Given the emerging trends which suggest machine learning moving away from data, there is very much chance that R might become the must-learn language for beginners. Whether to use R or Python shouldn't be a question because the purpose or the data analysis goal differs. R is optimized for deep statistical analysis which data researchers employ for deep analytics and data visualisation features while Python is more suitable for data wrangling. When Burtch Works did a comprehensive survey of data scientists and analytics professionals, R was found more popular with experienced pros and Python with beginners.
Julia, an emerging language is still considered an add-on. It shares many features with Python, R, and other programming languages like Go and Ruby, it's worth learning right in the beginning because it has the potential to replace Python for its superior performance. With Julia, it is possible to achieve C-like performance, and hand-crafted profiling techniques without optimization, which in Python's case, is impossible. Why employ Python in the first place if Julia can make the job better? Besides, Julia is good at working with external libraries, and memory management by default, and otherwise.
As said in the beginning, programming knowledge is not the be-all and end-all solution to securing a data science job. It is pretty much an obscure fact that employers look for problem solvers rather than number crunchers. Learning coding without paying attention to why you are doing it will take you nowhere. Learning data structures will not teach how to apply them to a given database for a particular problem. Well, there are many contenders like Scala and Swift which are fast making their way into the list of viable if not popular programming languages. To survive as a data scientist, better to let Python let be a necessity rather than a sufficient requirement.
