Learn the Art of Data Science; Programming languages of the decade.
Rinu GourBlockedUnblockFollowFollowingJun 17Since our childhood, our brain is wired by our surroundings.
Our society, environment, culture, beliefs and background plays a major role in our upbringing.
But, there is no harm in that, because at least it helps us to distinguish good from evil, right from wrong, just from unjust, and vice from virtue.
I would agree to disagree here but maybe that is the reason I love machines more than humans.
Adding to that humans cannot be artificially intelligent and immortal like machine learning.
Talking about Machine learning, it made me think about Data Science because Machine Learning is a part of data science and Data Science is the master of all.
Data Science in simple terms is the art of storing, cleaning and organizing the data to turn into a valuable resource which helps to build business strategies.
It is more of an art than a science because it is all about finding patterns and insights from the Raw Data.
It is a need for people who possess the required skills in order to become proficient in this field.
Apart from mathematical skills, there is a requirement for programming expertise.
But before gaining expertise, an aspiring Data Scientist must be able to make the right decision about the type of programming language required for the job.
In this article, we will go through the Data science programming languages which is the need of the hour and how you can become a proficient Data Scientist.
So, let’s learn the language of the decade!1.
PythonIt is easy to use, an interpreter based, high-level programming language.
Python is a versatile language which has a vast array of libraries for multiple roles.
The easier learning curve and useful libraries have made it the most popular choices for Data Science.
Also, the code-readability feature has made Python the most popular choice for Data Science.
This is a great benefit because a data Scientist tackles complex problems, it is, therefore, ideal to have a language that is easier to understand.
It also makes it easier for the user to implement solutions while following the standards of required algorithms.
It supports a wide variety of libraries.
Also, various stages of problem-solving in Data Science use custom libraries.
Solving a Data Science problem involves data preprocessing, analysis, visualization, predictions, and data preservation.
In order to carry out these steps, Python has dedicated libraries such as — Pandas, Numpy, Matplotlib, SciPy, scikit-learn etc.
Furthermore, advanced Python libraries such as Tensorflow, Keras and Pytorch provide Deep Learning tools for Data Scientists.
RR is a substantial language because it helps to let work on statistically oriented tasks.
The aspiring Data Scientists may have to face a steep learning curve, as compared to Python.
R is specifically dedicated to statistical analysis.
It is, therefore, very popular among statisticians.
It helps to have an in-depth dive at data analytics and statistics.
The only drawback of R is that it is not a general purpose programming language which means that it is not used for tasks other than statistical programming.
With over 10,000 packages in the open-source repository of CRAN, R caters to all statistical applications.
It also has the ability to handle complex linear algebra.
Another important feature of R is its visualization library ‘ggplot2’.
There are also other studio packages like tidyverse and Sparklyr which provides Apache Spark interface to R.
R based environments like RStudio has made it easier to connect databases.
This has a built-in package called “RMySQL”, and provides native connectivity of R with MySQL.
All these features make R an ideal choice for hard-core data scientists.
SQLReferred to as the ‘meat and potatoes of Data Science’, SQL is the most important skill that a Data Scientist must possess.
SQL or ‘Structured Query Language’ is the database language for retrieving data from organized data sources called relational databases.
SQL plays a crucial role in Data Science as it helps in is for updating, querying and manipulating databases.
Apart from that knowing how to retrieve data is the most important part of the job as a Data Scientist.
It is a ‘sidearm’ of Data Scientists, which means that it provides limited capabilities but is crucial for specific roles.
It has a variety of implementations like MySQL, SQLite, PostgreSQL etc.
Knowledge of SQL is a must because the extraction and wrangling of the data from the database is done through SQL.
IT is also a highly readable language, owing to its declarative syntax.
For example SELECT name FROM users WHERE salary > 20000 is very intuitive.
ScalaScala is an extension of Java programming language operating on JVM.
It is a general-purpose programming language.
It has features of an object-oriented technology as well as that of a functional programming language.
It can be used in conjunction with Spark, a big data platform.
Scala provides full interoperability with Java while keeping a close affinity with Data.
It also helps to sculpt data in any form required and therefore it is an efficient language made specifically for this role.
A most important feature of Scala is its ability to facilitate parallel processing on a large scale.
However, it does suffer from a steep learning curve and we do not recommend it for beginners.
In the end, if your preference as a data scientist is dealing with a large volume of data, then Scala + Spark is your best option.
Thus, this makes it an ideal programming language when dealing with large volumes of data.
JuliaJulia is the best programming language for scientific computing.
It is a recently developed language.
It is popular for being simple like Python and has the lightning-fast performance of the C language.
These features make it an ideal language for areas requiring complex mathematical operations.
A Data Scientist, work on problems that requiring complex mathematics.
Julia is capable of solving such complex problems at a very high speed.
The language faced some problems in its stable release due to its recent development.
But all ‘s well that ends well, The language is now widely recognized as a language for Artificial Intelligence.
Flux, which is a machine learning architecture, is a part of Julia for advanced AI processes.
A large number of banks and consultancy services are using Julia for Risk Analytics.
SASLike R, you can use SAS for Statistical Analysis.
The only difference is that SAS is not open-source like R.
However, it is one of the oldest languages designed for statistics.
The developers of the SAS language developed their own software suite for advanced analytics, predictive modelling and business intelligence.
SAS is highly reliable and has been highly approved by professionals and analysts.
Companies looking for a stable and secure platform use SAS for their analytical requirements.
While SAS may be a closed source software, it offers a wide range of libraries and packages for statistical analysis and machine learning.
SAS has an excellent support system meaning that your organization can rely on this tool without any doubt.
However, it falls behind with the advent of advanced and open-source software.
The reason for it is that it’s a bit difficult and very expensive language to incorporate.
More advanced tools and features in SAS that modern programming languages provide.
Conclusion:These are some of the important programming languages for a data scientist.
Though every language has its own importance these 6 languages should be kept on priority because having a good command over it will take you higher in your respective fields.
In the end, you will have to finalize your Language and start learning.
You will need to develop your own intuition on this and get a hands-on experience to excel.
So, what are you waiting for?.Artificial Intelligence!.. More details