The demand for data scientists in every industry is growing substantially. For the development of every business, there is a need to assess the data you gather. As a data scientist, you require upgraded tools and skill sets to manage data for better productivity and performance. The aim is to produce better results with relevant and vital information. Data Mining plays an important role here.

What is Data Science?

Data science is an integration of mathematics, statistics, data analysis, and related strategies to understand and analyze real wonders with data. It engages theories and techniques drawn from various fields within the wide regions of statistics, mathematics, computer science, and information science.

With the advancement of technology, methodology, and software tools- data science applications are widely used and gaining high demand.  To become a data scientist, you must learn at least one programming language (although knowing more than one is advantageous to job seekers). You have many options to choose from.      

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

Types of Programming Languages in Data Science:

A low-level programming language is the understanding language used by a computer to perform its operations. Examples of this are assembly language and machine language. Assembly language is used for direct hardware manipulation, to access specialized processor instructions, or to address performance issues.

A machine language consists of binaries that can be directly read and executed by the computer. Assembly Languages require assembler software to be converted into machine code. Low-level languages are faster and more memory efficient than high-level languages.

A high-level programming language has a strong abstraction from the details of the computer, unlike low-level programming languages. This enables the programmer to create code that is independent of the type of computer.

These languages are much closer to human language than low-level programming language and are also converted into machine language behind the scenes by either the interpreter or compiler. These are more familiar to most of us. Some examples include Python, Java, Ruby, and many more.

These languages are typically portable and the programmer does not need to think as much about the execution of the program, keeping their focus on the problem at hand. Many programmers today use high-level programming languages, including data scientists

Which is the best programming language for Data Science?

Determining the “best” programming language for Data Science depends on various factors such as the application of Data Science, personal preferences, and data science skill sets required to meet specific targets or complete the task. 

However, Python is often considered the default language for data science due to several reasons:

1. Versatility: Python is a general-purpose programming language that can be used for a wide range of tasks beyond data science, including web development, automation, scripting, and more. 
2. Rich Ecosystem: Python has a vast and robust ecosystem of libraries and frameworks. These libraries provide powerful tools for data manipulation, analysis, visualization, and machine learning.
3. Ease of Learning and Use: Python’s syntax is clear, concise, and easy to learn, making it accessible to beginners and experienced programmers.
4. Community Support: Python has a large and active community of developers, data scientists, and enthusiasts.
5. Integration and Compatibility: Python seamlessly integrates with other programming languages and technologies, allowing data scientists to leverage existing tools and infrastructure. 

It concludes the best programming language for data science depends on the individual requirements and constraints of each project.

Exploring the Role of Programming Languages in Data Science

Programming languages are essential tools in data science, enabling data manipulation, analysis, and visualization. The choice of language depends on the task, team preferences, system compatibility, and performance needs. 

Python’s simplicity, readability, and libraries make it very popular. 

R excels at statistical computing and graphics. 

SQL retrieves and manipulates data in databases. 

Julia provides high performance for numerical computing. 

Scala combines object-oriented and functional paradigms for big data applications. 

MATLAB is widely used in academia and industry for analysis and visualization but is proprietary. 

Python and R are currently the most popular Data Science Languages

Top Programming Languages a Data Scientist Should Master 

Mastering Data Science skills is an art where you need to integrate your skills in mathematics, statistics, Information, and programming language for the best results.  With the upgradation in technology, you need to upgrade your data science skills to move forward in your work performance and career advancement. 

Simply, Master Top Programming languages and move ahead in the field of Data Science!

PYTHON: Best Programming Language in Data Science

Python holds a special place among all other programming languages. It is an object-oriented, open-source, flexible, and easy-to-learn programming language. t has a rich set of libraries and tools designed for data science.

Also, Python has a huge community base where developers and data scientists can ask their queries and answer the queries of others. Data science has been using Python for a long time and the language is expected to continue to be the top choice for data scientists and developers. Learn Python in detail by opting for a Machine Learning online course with Python.

R: Top Programming Language in Data Science

R is better for ad-hoc analysis and exploring datasets than Python. It is an open-source language and software for statistical computing and graphics. R is a difficult-to-learn programming language On the other hand, people find Python a much easier programming language. With loops that have more than 1000 iterations, R beats Python using the apply function.

This may leave some wondering if R is better for performing data science on big datasets, however, R was built by statisticians and reflects this in its operations. Data science Course applications feel more natural in Python.

SQL: Best Programming Language in Data Science to Handle Data

Referred to as the ‘meat and potatoes of Data Science’, SQL is the most important programming language that a Data Scientist must know. SQL or ‘Structured Query Language’ is the database language for retrieving data from organized data sources called relational databases.

In Data Science, SQL is for updating, querying, and manipulating databases. As a Data Scientist, knowing how to retrieve data is the most important part of the job. SQL is the ‘sidearm’ of Data Scientists means that it provides limited capabilities but is crucial for specific roles. It has a variety of implementations like MySQL, SQLite, PostgreSQL, etc.

To be a proficient Data Scientist, it is necessary to extract and wrangle data from the database. For this purpose, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax. For example SELECT name FROM users WHERE salary > 20000 is very intuitive.

JULIA: Advanced Programming language in Data Science

Julia is a recently developed programming language best suited for scientific computing. It is popular for being simple like Python and has the lightning-fast performance of C language. This has made Julia an ideal language for areas requiring complex mathematical operations.

As a Data Scientist, you will work on problems requiring complex mathematics. Julia is capable of solving such queries at a very high speed. While Julia faced some problems in its stable release due to its recent development, it has been now widely recognized as a language for Artificial Intelligence.

Flux, which is a machine learning architecture, is a part of Julia’s advanced AI processes. A large number of banks and consultancy services are using Julia for Risk Analytics.

Tensor Flow: Software Library for Numerical Computation

TensorFlow is an excellent open-source software library for numerical computation. It is a machine-learning framework suitable for large-scale data. It works on the basic concept. For instance, if you want to perform a graph of computations in Python, once you defined it, then TensorFlow will run it by utilizing a set of tuned C++ code.

One of the most significant advantages of TensorFlow is that the graph can be broken into many chunks that can keep running in parallel over various GPUs or CPUs. And also supports distributed computing; thus, you will be able to train huge neural networks on immense training sets in a short time.

TensorFlow is the second-generation system from Google Brain.  It powers a large number of Google’s large-scale services, like Google Search, Google Photos, and Google Cloud Speech.

SCALA: Top Programming Language for Data Analytics

This is a general programming language that provides support for functional programming, object-oriented programming, a strong static type system, and concurrent and synchronized processing. It was designed to address many issues that Java has.

Once again, this language has many different uses from web applications to machine learning, however, this language only covers front-end development.

The language is known for being scalable and good for handling big data as well as the name itself is an acronym for “scalable language”. Scala paired with Apache Spark allows the ability to perform parallel processing on a large scale. Furthermore, there are many popular and high-performance data science frameworks written on top of Hadoop to use in Scala or Java.

Top Scale Libraries for Data Scientists

• Breeze: Breeze is a library for numerical processing, like probability and statistic functions, optimization, linear algebra, etc.
• Vegas: Scala library for data visualization.
• Smile: Statistical Machine Intelligence and Learning Engine (Smile) is a modern machine learning library.
• DeepLearning.scala: It is a simple library for creating complex neural networks from object-oriented and functional programming constructs.
• SAS

Like 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 software suite for advanced analytics, predictive modeling, 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, SAS has fallen behind with the advent of advanced and open-source software. It is a bit difficult and very expensive to incorporate more advanced tools and features in SAS that modern programming languages provide.

Conclusion

The landscape of data science is evolving quickly, tools used for extracting value from data science have also increased in numbers. You must have strong hands-on expertise in any of the above-mentioned programming languages that will kick off your Data Science Career.

Though, there is no specific order to this list of popular languages for data science, Python and R fighting for the top spot. However, having more than one language skills give you versatility and competence as a data scientist.

Also, Python seems to be the most widely used programming language for data scientists today. This language allows the integration of SQL, TensorFlow, and many other useful functions and libraries for data science and machine learning. With over 70,000 Python libraries, the possibilities within this language seem endless.

Python also allows a programmer to create CSV output to easily read data in a spreadsheet. My recommendation to newly aspiring data scientists is to first learn and master Python and SQL data science implementations before looking at other programming languages. It also is apparent that a data scientist must have some knowledge of Hadoop.

Common Discussion:

 Before choosing a programming language, you need to consider several things:

• Kind of data science tasks will you need to perform
• Your organization uses data science
• Your company objectives
• What are your career interests?
• Programming languages do you already know
• Level of difficulty are you ready to tackle
• Your educational ambitions

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