Trending Now

Common Scrum Mistakes and How to Avoid
Future Of DevOps Engineering in 2024
Top MS Excel Functions For Data Analyst
Product Owner Responsibilities and Roles
Python for Machine Learning Developers
How to Transform IT Service Delivery with SIAM
What is SIAM - SIAM Need and SIAM Framework
Key Highlights of SIAM Survey 2023
Three Key Layers of Governance in SIAM
Business Analyst in Cybersecurity
ITIL 4: A Journey from Certification to Implementation
The Role of Big Data in Modern Business
What is AWS: A Beginner's Guide to Cloud Computing
Agile vs SAFe: Comparison Between Both
How to Get CSPO Certification in 2024?
SAFe Implementation Best Practices
CSM vs. SSM: Which Scrum Master Certification is Better?
Agile Scrum Best Practices for Efficient Workflow
Ethical Hackers Salary Structure Worldwide 2024!
Best Agile Estimation Techniques 2024
ITIL V4 Major Changes and Updates
COBIT 5 vs COBIT 2019: Differences and more
Scrum Framework and Its Advantages in 2024
PRINCE2 Certification Cost in 2024
CSM vs PMP: Differences Explained & Which One to Choose?
Agile vs. DevOps: Difference and Relation
Smart Manufacturing through IoT for Operational Excellence
Sport Analytics: How Data Analytics is Changing Games
PRINCE2 Certification Core Role and Process
PMP vs PRINCE2: Which Certification is Better
Your Guide to Agile Scrum Foundation
Guide to Scrum Master Career Path in 2024
Scrum Master Certification Detailed Curriculum
Business Analyst Career Path, Skills, Jobs, and Salaries
Advantages of Certified Scrum Master
Digital Marketing Trends and Evolution
Scaling Agile in Organizations and Large Teams
What are the Scaled Agile Framework Core Values?
Scaled Agile Framework (SAFe) Key Features and Components
Top 6 Most-in-Demand Data Science Skills
Power of Emotional AI in B2B Sales
Role of PRINCE2 in Project Management Trends
Power Skills: Key Drivers for Your Project Success
Benefits of Lean Six Sigma Black Belt Certification
Benefits of Scaled Agile Framework (SAFe) in Organizations
AZ-900 Study Guide: Microsoft Azure Fundamentals
Top Programming Languages for Data Science Professionals in 2024
What is One Key Purpose of DevOps?
How are the Business Analysts Ruling The Healthcare Industry?
Business Analytics Trends in 2024: Tools and Predictions
Scrum Master Certification Cost in 2024
Gemini Google's AI Model to Take Down GPT-4
Career Opportunities for PRINCE2 and PMP Professionals
Lean Six Sigma Certification Levels Complete Guide 2024
Kaizen Costing - Types, Objectives, Process
Lean Continuous Improvement: What is it and How Does it Work?
The Art of Lean by Leonardo Da Vinci
How AI is Transforming Robotic Surgery?
Project Management Strategies for Teamwork
What is Kaizen? With Principles, Advantages, and More
Achieving Predictive Maintenance & Predictable Outcomes through Root Cause Analysis
Agile vs Waterfall: Difference Between Methodologies
Agile at Scale: Strategies and Challenges
Combining Lean Principles and Agile Methodologies
Primary Benefits of Adopting Agile Methodology
Agile Requirements Gathering Techniques 2024
What are CI and CD in Agile?
Top 7 Ethical Hacking Tools in 2024
User-Generated Content and it's Importance
Top 10 Scrum Master Interview Questions and Answers for 2024
Project Closure Process in Project Management
Ways of Risk Management and Risk Mitigation
Best Leadership Qualities fo Project Managers
Difference Between Agile & Waterfall Methodologies
Agile Scrum Product Owner Roles & Responsibilities
Top 9 Advanced MS Excel Formulas in 2024
Project Management Budgeting Methods 2024
Digital Marketing Trends 2024 You Must Know!
5 Key Agile Project Management Metrics 2024
The Complete Ethical Hacking Guide 2024
The Ultimate Digital Advertisement Guide 2024
PRINCE2 Project Life Cycle and Major Processes Explained
Top 7 Power BI Projects for Practice 2024
How to Measure Progress of Agile Projects Using Agile Metrics?
Transformative Machine Learning Project Ideas by Brands
Top-Notch Entry-Level Digital Marketing Jobs
Top 5 Real-World Machine Learning and AI Examples in the Industry
Leading SAFe 6.0 Certification Exam Preparation (2024)
AI and Power BI: A Powerful Combination for Data Visualization
Tableau AI and ML Model: Making Data Experience Powerful
Database Management in Java Full Stack Development: JDBC and ORM Tools
How to do Video Marketing for Audience Engagement?
Top 7 Trends in Data Science in 2024 and beyond
Email Marketing Strategies for Campaign Success in 2024
ITIL 4 Certification: How to prepare and succeed in the latest exam
How to Become a Data Scientist in 2024?
Personalization in Digital Marketing for Improved Engagement
ITIL 4 and Artificial Intelligence: Leveraging AI for Service Improvement
Introducing PRINCE2® 7: The Future of Project Management
What is Agile: History, Definition, and Meaning
Home
Data Science Programming Languages

Top Programming Languages for Data Science Professionals in 2024

Stefan Joseph
Stefan Joseph
Stefan Joseph is a seasoned Development and Testing and Data & Analytics, expert with 15 years' experience. He is proficient in Development, Testing and Analytical excellence, dedicated to driving data-driven insights and innovation.

The first thing needed to become a Data Science professional is to know multiple programming languages and the faster you learn the better. Every Data Science enthusiast must be passionate about coding and coding experience is a must to become a Data Scientist. But before you jump on to writing piles of code you have to understand the specific domain you’ll be working on. 

Domains of Data Science

Domains of Data Science

Along with coding skills, a Data Scientist should also have analytical and problem-solving skills. A data scientist is a proficient specialist adept at employing mathematical and statistical methodologies to effectively process, scrutinize, and derive insights from data. This field encompasses various domains, including but not limited to machine learning, deep learning, network analysis, natural language processing, and geospatial analysis. The execution of data science tasks predominantly hinges on harnessing the computational capabilities of computers. Programming languages for Data Science stands as the pivotal methodology enabling data scientists to engage with and issue commands to computer systems.

Let’s talk about the most in-demand programming languages a Data Scientist must have in 2023 and beyond.

Data Science Course

Programming Languages for Data Science

These are the coding languages that are required for a Data Science professional:

  • Python
  • R
  • SQL
  • Java
  • Julia
  • Scala
  • C/C++
  • JavaScript
  • Swift
  • Go

1. Python

This is the maestro of all coding languages when it comes to Data Science and any Data Science task you can imagine can be executed using Python. Python possesses the capability to execute a broad spectrum of tasks, ranging from data preprocessing, visualization, and statistical analysis, to the deployment of machine learning and deep learning models. The syntax of Python is quite simple and easy to understand. Consequently, beginners in Data Science always start with learning Python, the best programming language for Data Science.

Best programming language for Data Science

2. R

Another programming language that is explicitly designed for Data Science is R. This is an open-source domain-specific language that has gained popularity after Python in the Data Science realm. Therefore, learning Python or R or both is the best scenario in Data Science and is going to be a plus for you. Working directly with R is not a big deal but people commonly use Rstudio, a powerful third-party interface that integrates various capabilities, such as data editor, data viewer, and debugger.

R Programing

3. SQL

Database management is one of the most fundamental jobs of a Data Scientist. SQL or Structured Query Language gives programmers the ability to communicate with, edit, and extract data from the database. The main funda is that by knowing SQL you can work with various databases where SQLite, MySQL, and PostgreSQL are the primarily used systems. SQL is a versatile language and the syntax is very easy to learn.

SQL

4. JAVA

In the past decade, JAVA has dropped its rank below Python but has become highly effective when it comes to website development. Java Virtual Machine provides a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala. However, the JAVA ecosystem is quite a reliable platform where endless technologies, software applications, and websites collaborate.

Java

5. Julia

Released in 2011, Julia is a star in the making in the Data Science profession and it has flabbergasted the entire Data Science community through its awesome numerical computing. It has a high speed, clear syntax, and versatility- the main reason behind its popularity. Though it has a smaller community without libraries as its main competitors, several organizations have shown trust in Julia.

Julia

6. Scala

Scala is a multi-paradigmatic language exclusively designed to be a clearer and less wordy alternative to Java. It was released in 2004 and now is one of the most in-demand programming languages for machine learning and big data. Scala, by its compatibility with the Java Virtual Machine, facilitates seamless integration with Java, rendering it an ideal language for complex, distributed big data initiatives. A notable illustration of this synergy is the utilization of Scala as the programming language of choice in the development of the Apache Spark cluster computing framework.

Scala

7. C/C++

C and C++ outpace many programming languages in terms of speed, rendering them highly suitable for the development of big data and machine learning applications. Notably, some pivotal components of widely used machine learning libraries, such as PyTorch and TensorFlow, are coded in C++.

C/C++

However, it’s important to acknowledge that C and C++ are inherently intricate due to their low-level nature. Consequently, while they may not be the initial choices for those venturing into the realm of data science, attaining proficiency in these languages can significantly enhance one’s skill set and career prospects, provided a solid grasp of fundamental programming concepts is established.

8. JavaScript

If you want to build rich and interactive web pages, shake hands with JavaScript. Generally, this programming language is mainly used in development sectors but its utility in Data Science is also reaching heights. The contribution of JavaScript in Data Visualization is a fantastic aspect and also supports popular libraries for machine learning and deep learning, such as TensorFlow and Keras. JavaScript is ideal for front-end and back-end developers who want to enter into the Data Science field.

JavaScript

9. Swift

With the advancement of mobile applications and IoT, the need for more mobile-friendly applications has increased over the last decade. Apple conceived Swift as a tool aimed at simplifying app development to expand its app ecosystem and bolster customer engagement. Following its introduction in 2014, a collaborative effort between Apple and Google swiftly ensued, marking the pivotal role of Swift in bridging the realms of mobile technology and machine learning.

The good news is that Swift is no longer restricted to the iOS ecosystem but can operate quite independently on Linux. It is now compatible with TensorFlow and is interoperable with Python. Therefore, a mobile developer who has grown a ferocious interest in Data Science should learn Swift swiftly. 

Data Science Course

10. Go

Google introduced Go in 2009 and ever since it has stolen the spotlight, especially for machine learning projects. Go is known to be the 21st-century C with C-like syntax and layouts. Data Science professionals are becoming fans of Go because it is a flexible and easy-to-understand language. Though the community is small Go is a good ally for machine learning tasks. 

Conclusion

Now that you have a clear understanding of the programming languages a Data Science professional requires, fasten your seatbelt and start learning coding. Each language has its traits and does exceptionally well in different domains. However, many coding languages are versatile and can be used in multiple genres. Therefore, choose your Data Science wisely based on your coding experience plus a new programming language learning capability. 

Leave a Reply

Your email address will not be published. Required fields are marked *

Popular Courses

Follow us

2000

Likes

400

Followers

600

Followers

800

Followers

Subscribe us