Data analysis enables firms to make better decisions and realize their full potential. Data analysts employ a variety of programming languages to extract information from organized or unstructured data, and there is high demand for this skill. According to the Bureau of Labor Statistics, jobs for data professionals are predicted to expand 36% by 2031.
The best programming languages for data analysis will help you start a profitable career in this industry. As a data analysis professional, you can choose from a variety of computer languages. This tutorial will discuss the top eleven programming languages for data analysis. In this article, you will also find tools to help you master data analysis abilities.
Most Used Programming Languages For Data Analysis
1. Python
Python is a favorite and widely used computer language for data science. It is quickly becoming a mainstream language in schools, universities, and corporations since it is both simple to use and extremely powerful.
If you want to be a data scientist, we recommend starting with Python, even if you’ve never coded before. Python is an easy-to-learn language with a clear syntax that emphasizes excellent performance and speed.
As a general-purpose language, it provides practically everything you require. Another advantage is that it has a large community. It’s nice to have thousands of individuals with whom to share ideas, discuss problems, and collaborate on solutions.
2. R
Ross Ihaka, a statistician, and Robert Gentleman, a bioinformaticist, established R in 1992. It is a major player in the realm of data science, with applications in finance, medicine, research, and academia.
Although it can be used for a variety of purposes, R is regarded as a more domain-specific language, with a concentration on statistics and graphics.
It includes numerous useful tools for data science, large data analysis, and machine learning. Dplyr, data.table, and readr packages are essential for data wrangling. R outperforms Python in data visualization. It includes a large collection of strong data visualization software such as ggplot2, Plotly, Lattice, and others.
SHINY is a superb R framework that allows you to create dynamic, stylish, and effective dashboards, far superior to Plotly.
If you have a background in statistics, you will find R quite easy to learn. Otherwise, it has a longer learning curve, but it is a user-friendly language with a crisp syntax.
3. Julia
Julia is a sophisticated general-purpose programming language designed specifically for data science and ML, with a heavy emphasis on scientific computing. It is a relatively new language that is rapidly rising in popularity, and many businesses are beginning to use it.
It provides a complete data science ecosystem that includes crucial libraries such as DataFrames, JuliaDB, Flux, and more. It is JIT (just-in-time) compiled, making it faster than Python and sometimes even faster than C.
The syntax is really simple, clear, and easy to understand. If you already know Python, Julia will be quite simple to comprehend. If you want to be at the top in the future, look into Julia and give it a try. You may learn it for free online and keep up with the newest developments by watching JuliaCON.
4. SQL
If you want to work in data, SQL is a must-have ability. 99% of firms worldwide utilize SQL and will include it in their job descriptions. SQL is a data-related system that allows direct connection with relational databases. It’s an easy language to learn and utilize. When working on a data science project, SQL is always the first step that brings you into close contact with the data you need to work with.
You may see the data right away, run queries and comprehend it quickly, handle massive amounts of data, and gain critical insights in just a few seconds. If you want to succeed, you should study SQL first before diving into Python or R.
5. Scala
Scala is a multiparadigm language that combines object-oriented and functional programming. It was invented in 2003 and has since gained popularity among data scientists. It has become a standard language for data science; it is suitable for big data analysis when used with the Apache Spark framework; therefore, it is ideal for dealing with large amounts of data.
It is utilized by a number of technology businesses, including Netflix, Twitter, LinkedIn, and Airbnb. Although not as simple to learn, if you already know Java or C, the transfer is simple.
There is a terrific course on Coursera created by the language’s creator that is free and takes you from the basics to Spark and Machine Learning.
6. C++
C is thought to be the most powerful language in the world. C++ appeared after C and is an improved version of C. It is one of the quickest languages ever invented, and this speed is extremely useful for data scientists.
C++ allows you to compile over a gigabit of data in less than a second. Many current languages are built on the foundation of C. Python was written in C. R has many C-based libraries, including Numpy, which includes C++.
Even if you don’t need it right away, it’s necessary to master the fundamentals of C++ programming. Where can you learn this? You can begin by reading “The C++ Programming Language” by Bjarne Stroustrup, the language’s developer.
Stanford, Yale, and MIT also provide free online lectures through YouTube.
7. MATLAB
MATLAB is an excellent data science system; nevertheless, it is not free. It’s rapid and ideal for statistical analysis and dealing with complex mathematical problems. MATLAB is well-developed and appropriate for deep learning, machine learning, and even graphing.
Pandas, ScikitLearn, and Matplotlib are libraries created by people in the MATLAB community that want to create an open-source approach to working with Matlab.
8. Java
Java is one of the oldest high-level programming languages, powering a wide range of enterprise-level applications (both web and mobile). While not the first choice when it comes to data science, Java is useful for creating machine learning algorithms because of its broad applicability and specific libraries for DS and ML: Weka, Java-ML, MLlib, and Deeplearning4j.
It is a difficult language to learn, and if you have to choose between Scala and Java, start with Scala because it is easier to learn.
9. JavaScript
Although Javascript is well-known as a superb web language, many people are unaware that it also excels at data visualization. TensorFlow.js now supports machine learning as well.
JS is useful for scraping the web or better understanding the reasoning behind dashboards made using R, Python, DASH, and SHINY.
10. SAS
SAS is a popular choice in the big pharma, government, health, and finance industries because it excels at statistical analysis and is one of the oldest analytics systems built. SAS, like MATLAB, isn’t free. Top companies use it because of its long-standing reliability and authority.
11. Go
Golang, a strong programming language similar to C, was developed by Google in 2007. It’s much faster than Python and a viable alternative to C if you want to accelerate your algorithms. It’s an intriguing pick, not only for its speed but also because it includes data science libraries such as Gota, GoLearn, Gonum, Qframe, Gorgonia, and others.
Data Science Languages and Tools
1. Programming Languages
Programming is important to data science, and the two most common languages used by data scientists are Python and R. Both of these languages are diverse and include many strong libraries for data manipulation, analysis, and machine learning.
2. Data Analysis and Visualisation Tools
After you’ve gathered your data, the next step is to evaluate and visualize it. Tableau and Power BI are powerful tools for creating meaningful reports and graphics. Examples are Tableau and Power BI.
3. Machine learning frameworks
Machine learning is an important aspect of data scientist work, and frameworks like TensorFlow, scikit-learn, and Keras are necessary for developing and training models. Examples are:
- scikit-learn
- TensorFlow
- Keras
4. Big Data Tools
When working with really large datasets, you must use specialized tools to manage the data efficiently. Apache Hadoop and Apache Spark are widely used big data tools. Examples are:
- Apache Hadoop
- Apache Spark
5. Cloud Computing Platforms
Cloud platforms such as Amazon Web Services (AWS) and Google Cloud are critical for performing data science projects that demand significant processing power or storage. Examples are:
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
6. Data Cleaning Tools
Cleaning data is typically required before it can be analyzed or modeled. OpenRefine and Trifacta are two tools that can assist with this vital work. They are
- OpenRefine
- Trifacta
Conclusion
Data science entails a significant amount of coding, and you will be forced to code extensively in order to solve problems. If you want to work with data, the best languages to learn are Python and R. We recommend that you start with either Python or R and work on mastering one before moving on to other languages.
SQL is a must-know data expertise that is essential in all businesses; therefore, you should surely master it from the start. After you’ve established a foundation in R/Python and SQL, depending on the size and purpose of your organization, you can consider alternative technologies such as Scala, Julia, C++, or others.