5 Programming Languages to Excel in Data Science Career

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5 Programming Languages to Excel in Data Science Career

Data science has had a significant influence on practically every business industry. Both business domains, data engineers, and scientists greatly benefit from data science.

Most of these experts are keen to expand their knowledge by becoming proficient in programming languages and required skills. There are only several programming languages relevant to data science and associated technologies.

This explains why studies show that the data science field is rapidly becoming increasingly in demand. According to Statista, the percentage went up to 60% in 2021 from 30% in 2020.

Programming acts as the backbone of data science. So, it’s critical to learn these languages to have a successful career to meet corporate objectives.

It’s nearly impossible to have a successful data science career without applying programming language. If you are interested in this field and how to start, you can enroll some Data Science Course widely available on the internet.

So, what are the programming languages you’ll need to master to excel in this career?

Programming Languages That Data Scientists Need to Master

1. Python

Python is one of the most popular and best functional programming languages for data science. Most times, it’s used in artificial intelligence, machine learning, and deep learning fields.

Only Data Jobs, UK’s largest job board for data and analytics professionals, stated that most open data roles in their boards have Python programming language as a qualification requirement.

Python libraries are powerful enough to help in the automation of various operations. For instance, it can help with data collection, analytics, visualization, and data modeling.

You can leverage this language when creating tools and libraries from scratch. Moreover, you can use Python to develop computer applications, websites, finance, data analytics, video games, etc.

Most programmers consider Python a universal language. This enables developers to create various projects, from ML applications to straightforward apps.

If you’re starting, you can leverage it as it’s transparent and simple to learn. Moreover, its numerous add-on modules for libraries help solve most problems. Also, it’s suitable for projects ranging from larger data analytics, quantitative and qualitative calculations, and data mining.

Besides, it is adaptable and offers features like dynamic binding and typing and data structures that make it suitable for building complex applications.

2. R

R is a powerful scripting language for data science applications. Since it’s an open-source programming language, you can apply it to research and also use it in universities.

All thanks to its key features like simple learning, mathematical modeling, data processing, statistical graphics, computing, complex data, etc.

Data scientists find R a suitable programming language, because of its built-in statistical features, developers can comprehensively understand data visualization.

You can use it to conduct sentiment analysis to determine how an audience perceives a product or service. Most experts often use it including, marketers, data analysts, and statisticians.

R is one of the most imperative programming languages you need to learn if you’re looking for data jobs. It helps in various sectors to extract raw data, including, information analysis, processing, transformations, and visualization.

You can use R to create a wide range of prediction models, machine learning methods, and image processing software. Some of the main characteristics of R include:

  • A variety of support libraries
  • Computerized sharing
  • A comprehensive programming language with OOPS components
  • Powerful graphic abilities
  • Encourages programmers to create their packages and libraries
  • An extremely engaged community support
  • Add-on software such as ODBC, RODBC, etc.

3. Julia

Julia is rapidly gaining popularity in the programming industry. It is a multi-purpose programming language perfect for numerical analysis and scientific computing.

Julia focuses more on performance, and this has greatly influenced its popularity. Moreover, this has made it a top priority among high-profile companies in various industries. For instance, it’s widely used in companies specializing in risk analysis, time-series analysis, and space mission planning.

Besides, this is one of the most versatile programming languages. Julia can support both parallel and distributed computation. You can use Julia for low-level programming despite being a dynamically typed language.

Because of its built-in support package for a manager, it’s ideal for data visualization, deep learning, and operations on multi-dimensional datasets.

Aside from it being an easy-to-learn programming language, it has various pros and cons:

Julia ProsJulia Cons
The quickest programming language for interactive computing.It has smaller community support compared to others.
Python, MATLAB, and Ruby scripting languages inspired its syntax.Unlike other languages, it’s harder to solve problems and get answers by a quick Google search.
You can easily pick up the fundamentals and quickly start producing.This makes it time-consuming when solving problems.

4. SQL

If you’re looking for big data jobs, you need to strive and learn the SQL language. This is crucial if you want to impact your data scientist career greatly.

This is vital since, as a data scientist, you’ll need to have SQL knowledge to work with structured data. Moreover, SQL is a powerful data research tool that provides access to data and statistics.

Since data science depends on a database, it’s essential to use a database language tool like SQL. A solid understanding of SQL will help you query databases, especially when working with large data.

SQL is one of the most popular programming languages used in a relational database. It has a non-procedural nature hence eliminating the usage of traditional programming language. As a result, it’s simple to use SQL as you don’t need to have prior knowledge as a skilled coder.

On the other hand, SQL has an unfriendly user interface that can make it challenging for database users. Some of the versions of the SQL language can be quite costly. Moreover, it has hidden business rules making it hard to have complete control of the database.

5. Java/JavaScript

Java has been a top-notch programming language for over 20 years. Often people refer to it as a class-dependent, object-oriented, high-performance language.

Most programmers prefer using when creating algorithms in machine learning and data science. Most recent technologies like big data, and IoT devices integrate well with Java and hence have a wide range of applicability.

Besides, you’ll notice that the industry segment that develops web and mobile apps uses Java. Also, the Java language provides top-notch security measures to protect projects’ sensitive data.

Like CSS and HTML, JavaScript is one of the easy-to-learn, well-liked programming languages used today on the World Wide Web. Moreover, most website developers use JavaScript to develop interactive web pages and visualizations for the client’s side.

Additionally, it contains a wide selection of libraries. These libraries can provide various options for data science project solutions and can also solve Big Data-related problems.

Key Take Away

Data science is experiencing rapid growth, making it one of the most sought-after jobs. Companies understand that they need to hire data scientists to acquire a market advantage. This article has highlighted some of the programming languages you can leverage for your career.

Each language uses a framework that can integrate well with big data, machine learning, and linear regression, among other statistical tools. Therefore, this makes programming expertise to be a lucrative career. You need to learn the correct data science language to excel in your career.

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