Must-Know Programming Languages to Become a Successful Data Scientist

Importance of Data Science

With the improvements in data science technology, the outlook of various businesses towards their problems has completely changed. Business organizations are imbibing various data science techniques to cope with daily problems and have invented constructive solutions. Data science has allowed every industry to extract information from the data that is generated regularly due to the business involvements such as customer experience, buying/selling of products, etc. Data science is constantly contributing to and improvising the operations of various businesses wherein it allows a business to understand patterns from the business data and generate meaningful insights. These insights can later be used to enhance the services and products offered by a business. Hence, data scientists play a very crucial role in the proper functioning of a business. Click here to learn Data Science Certification in Hyderabad

Understanding a Programming Language

Let's first examine the definition of a programming language and its applications in both technical and non-technical sectors before analysing the influence that programming languages have on the careers of data scientists.

Computers are employed in practically every industry. Their significance and advantages have exceeded all expectations. Whether it's data storage or communication, computers are employed to tackle every everyday issue. Programmes are essential to the operation of a computer. A set of instructions supplied to a computer by programmers in order to do a certain job might be referred to as a computer programme.

Programming languages enable programmers to create computer code. So, in a technical sense, we may develop any kind of programme that can fulfil a task using a programming language. Using specialised software known as compilers and interpreters, programming languages are human-understandable languages that are later converted into computer-level language.

Characteristics of Programming Languages

• They are created in English and are easy to comprehend, apply, and learn.
• They can be used in any type of system.
• They provide programmers with a bunch of functionalities that can be used to create complex software

Varieties of Programming Languages

High-level languages

All programmers most frequently utilise these languages. They are effective because they include a range of symbols, operators, and basic syntax.

Examples of High-Level Languages are:

• Java.
• C/C++.
• Python.
• JavaScript.
• Ruby.
• Go.

Low-level languages

These are machine-level languages that are extremely challenging to understand and utilise. Machine or assembly languages are included in these languages. These languages are difficult because they employ a sophisticated system of rules and symbols.

Using Programming Languages for Data Science

In this section, we’ll be discussing the programming languages that are widely used by data scientists.

Python

In the technological sector, Python is regarded as one of the most potent, effective, and simple-to-understand high-level programming languages. It is a user-friendly language for beginners and offers a wealth of functionalities that may be applied across practically all industries. Because it has applications in every area, it is regarded as a flexible language. Python is regarded as the ideal option for a data scientist since it is simple to learn and facilitates code reuse. It also offers programmers a huge selection of libraries that may be utilised to quickly complete many different kinds of challenging jobs.

While tackling a data science topic, a data scientist must go through a number of steps. These stages each call for a distinct method of problem-solving. Python is used to create useful libraries that may be used to carry out various data science jobs, including data analysis, data visualisation, machine learning tasks, predictive analysis, etc. The usage of popular libraries like NumPy, Pandas, SciPy, Matplotlib, and others is common in task execution. Additionally, well-known frameworks like Keras, TensorFlow, and Pytorch give data scientists access to deep learning resources.

R

R is another programming language that is popular and widely used among data scientists. It comes as no surprise that data scientists are involved in a variety of tasks and for this, they require specific tools for every task. R is commonly used for statistical purposes by data scientists. But, its learning curve is very steep and therefore a lot of effort is required to apprehend this programming language. R is not a part of the general-purpose category under programming which means that apart from performing statistical tasks in data science, R doesn’t have any role in performing other tasks. But, considering its advantages and uses, R provides data scientists with many libraries which makes solving mathematical oriented tasks very easy. It also holds the ability to comprehend other advanced mathematical topics such as linear algebra. This allows R to be used to work on neural networks. Apart from this, R also provides packages for efficient connectivity with databases like SQL.

SQL

The fundamental tool of data science is SQL. This is a talent that data scientists really must have, unlike other abilities. SQL, which stands for "Structured Query Language," is a query language. Languages that are used to interact with databases are known as query languages. They facilitate the storage and retrieval of data from databases for data scientists. Data storage, retrieval, and updating for users is a crucial component of every organisation. Consequently, knowing SQL equips a data scientist with pertinent abilities that are crucial while working with databases. SQLite, PostgreSQL, MySQL, and other relational database types are some examples.

Julia

Julia is the latest programming language which is renowned for its high-performance. This programming language became popular because of its simplicity and fast performance. Data scientists have to carry out lots of mathematical operations like linear regression, neural networks, etc. Julia has the capability of solving such advanced mathematical problems with high-speed. Developers are constantly working on this language and the latest developments have made Julia an AI language.

Scala

It is capable of working with Java libraries. Scala and Spark work together most effectively. For handling Big Data, the Apache License-compliant Spark framework is employed. Data scientists work with a lot of data (Big Data) that is produced on a regular basis by company activities. In these circumstances, where Scala has appropriate features to handle massive volumes of data, Scala is great. Big Data processing may take a lot of time, which is time that could be better spent elsewhere. As a result, Scala supports parallel data processing, enabling simultaneous actions on a set of data. This saves a tonne of time and produces outcomes quickly. But learning Scala is challenging for beginners because of its high learning curve. However, Scala may be your greatest friend when working with Big Data.

Using Libraries and Frameworks for Data Science

So, what is a framework? A framework can be defined as software that can add certain functionalities to your application. So, instead of writing additional code, using a framework can allow you to use pre-written code in your application which makes the development of software easier.

In this section, we’ll be discussing a few frameworks which are extensively used by data scientists.

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TensorFlow

One of the most well-known data science frameworks was developed and is kept up to date by Google. It is widely employed for data science-related tasks including machine learning, deep learning, neural networks, etc.

To build deep learning models that can process data, utilise TensorFlow. Following data manipulation and analysis, they also produce computational graphs. Due to its adaptable design, this framework is used by large corporations like IBM, Twitter, etc.

Jupyter Notebooks

This is another great tool that can add more specialties to your skillset. It provides a platform for the development and presentation of data science projects. It provides functionalities such as combining the source code of an application and the output into a single place using different types of mathematical equations, rich text, and media, infographics, etc. as required. This tool is supported by big names such as Microsoft, GitHub, Google, etc.