Top 10 Data Science & Machine Learning Programming Languages for 2020
Data Science Skills that you must know in 2020
In Today’s highly focused market, which is foreseen to strengthen further, the Data science aspirants are left with no solution yet to upskill and update themselves according to the industry demand. Winning circumstance tributes the mismatch among demand and supply proportion of Data scientists and other Data experts in the market, which makes up a great age to grab better and dynamic opportunities.
The knowledge and use of programming languages like R, Python, Julia, SQL, etc, that better intensify the data science industry, are must to have.
Learn Python to become a Data Scientist!
Software developers love contending about which programming language is the best. Be that as it may, the measure for what is “best” is confusing. At the point when we discuss software development for the data science and Machine learning field, this question is ageless and will never lose its significance.
The most useful programming language needs to have the simplicity of syntax and use, which is abstract, however, factors, for example, type security, speed, libraries, and network support are definitely not. Thus, let us take a look at the top ten programming languages for Data Science and Machine learning.
Along these lines, here I have assembled the list of top 10 Data Science & Machine Learning programming languages for 2020 that aspirants need to figure out how to improve their careers.
- Python:
Python holds a significant place among all other programming languages. It is an object-oriented, open-source, easy to learn, adaptable programming language and has a rich arrangement of libraries and tools created for data science. Likewise, Python has a colossal network base where developers and data scientists can ask their queries and answer inquiries from others. Data science has been utilizing Python for quite a while and it is relied upon to keep on being the top choice for Data Scientists and developers.
2. R:
R is a unique language and has some truly interesting highlights which are absent in different languages. These highlights are significant for data science applications.
Learn R to become a Data Science Star!
Being a vector language, R can do numerous things at once, functions can be added to a particular vector without placing it in a loop. As the intensity of R is being realized, it is finding use in a different place, starting from financial studies to genetics and medicine and biology.
3. SAS :
Like R, you can utilize SAS for Statistical Analysis. The main difference is that SAS isn’t open-source like R. Notwithstanding, it is perhaps the oldest language intended for measurements. The developers of the SAS language built up their very own software suite for cutting edge analytics, prescient displaying and business intelligence.
SAS is highly reliable and has been exceptionally affirmed by experts and analysts. Organizations looking for a steady and verified platform use SAS for their analytics necessities. While SAS might be a closed source software, it offers a wide scope of libraries and bundles for statistical analysis and machine learning.
4. SQL:
SQL (Structured Query Language) is a domain-specific language used in programming and designed for organizing data held in a relational database Management system. As the job of a Data Scientist is to transform raw data into significant bits of knowledge, hence they essentially use SQL for data recovery. To be a powerful data Scientist, they should know how to wrangle and concentrate data from the databases utilizing SQL language.
5. Scala:
Scala which is otherwise called Scalable language is an expansion of Java language. It runs on Java Virtual Machine (JVM) and is one of the accepted languages with regards to playing practically with Big Data. Scala serves as a significant tool for the Data Scientists because it supports both unidentified functions just as higher-request functions.
Master the concepts of Scala Tuples
Scala is preferred by numerous individuals as it is stable, adaptable, quick, and versatile. Coding in Scala is organized and accomplished much faster in contrast with Python. You can apply Scala to make profitable items that run with Big Data.
6. Java:
Java is perhaps the oldest language used for business development. The majority of the well known Big Data tools on the likes of Spark, Flink, Hive, Spark and Hadoop are written in Java.
It has an extraordinary number of libraries and tools for Machine Learning and Data Science. Some of them being, Weka, Java-ML, MLlib, and Deeplearning4j, to solve the greater part of your ML or data science issues. Additionally, Java 9 gets the much-missed REPL, that encourages iterative development.
7. Javascript:
Data Scientists ought to know about Javascript as it exceeds expectations at Data visualization. There are numerous libraries that streamline the use of js for visualization, and D3.js is one of them and very amazing at that also. With 2018 released Tensorflow.js, the language is presently equipped for bringing Machine learning to JavaScript developers — both in the browser and server-side.
8. Tableau:
Tableau is one of the most famous Data Visualization tools utilized by Data Science and Business Intelligence experts today. It empowers you to make insightful and effective representations in an intelligent and colorful way.
Its utilization isn’t only for making traditional graphs and charts. You can utilize it to mine significant experiences because of the plenty of highlights and customizations it offers.
9. Julia:
Julia is picking up prominence in the Data Science and Machine Learning world. A few specialists are now contrasting it with Python, which may be untimely, yet it doesn’t reduce Julia’s capability in the scarcest.
This programming language is modern, elite, significant and noteworthy, and was made by a group of MIT mathematicians and computer scientists. It is open-source and is generally applied for data manipulation and logical calculations. In the event that you have dealt with Matlab, R, or Python previously, you will get used to Julia rapidly. Julia’s speed makes it an astounding language for Machine Learning and Data Science.
10. MATLAB:
MATLAB has local support for the sensor, picture, video, telemetry, binary, and other formats. It offers a full arrangement of insights and machine learning usefulness, in addition to cutting edge techniques, for example, nonlinear optimization, system ID, and a great many prebuilt algorithms for picture and video processing, money related modeling, control framework structure. Its numeric schedules scale directly to parallel processing on clouds and clusters.
The End:
Data Science & Machine Learning are two of the hottest fields of the century & making a career in it is the biggest dream you would ever achieve. The field of data science and machine learning language is wide, and however R and Python are the most preferred languages for the same, but the goal was to give you how these different languages can be used.
If you want to learn Python and need resources, you can also check out this list of Top Python courses in 2020 :
