Best Programming Language for a Data Scientist to Learn

This is the most common question every data science learner has, “What is the Best Programming Language for a Data Scientist?”. So, if you have the same question, then give your few minutes to this article. In this article, I will discuss the Best Programming Language for a Data Scientist. After reading this article, you will find an answer to your question.

So without any further ado, let’s get started-

Best Programming Language for a Data Scientist

Data Science is one of the most popular fields. To learn Data Science, the most important skill is Programming Language. And at this step, most people have a question, “Which Programming Language?”. Because there are various programming languages are available for data science.

So, first, let’s start with some most popular programming languages used for Data Science. And after that, I will compare some most suitable languages with respect to data science.

1. Python

Python is one of the most popular programming languages for Data Science. A python is an object-oriented, interpreted, and high-level programming language. Python is easy to understand language. Its syntax is easily readable. Even beginners can easily understand its syntax without any complications. 

Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. Python also has a huge community where data scientist can ask their queries.

2. R

R is another mostly used open-source programming language for data science. There are some interesting features in R that are not present in other programming languages. These features are useful for data science-related tasks.

R also has a software environment for statistical computing and graphics. It can provide many statistical models. With the help of R programming, you can plot a graph and perform another visualization-related task easily.

3. Julia

Julia is a high-level dynamic programming language used for high-level dynamic programming language. This is the newer language. The reason behind its popularity is its speed and performance.

Julia has fast performance similar to the C language. Julia is also used for performing data science-related tasks.

4. Java

Java is one of the oldest and popular programming languages. Various popular Big Data tools like Spark, Flink, Hive, Spark, and Hadoop are written in java. Many organizations use Java to perform tasks. Java has a good amount of libraries and tools to perform Data Science and Machine Learning tasks.

Weka, Java-ML, MLlib, and Deeplearning4j are use used to solve various Data Science problems.

5. SQL

SQL stands for Structured Query Language. As a Data scientist or data analyst, you have to deal with data. That’s why knowledge of SQL is crucial for you. With the help of SQL, you can perform the querying and editing of the information stored in a relational database. SQL is used for managing a huge amount of databases.

6. MATLAB

MATLAB is a language for analyzing and visualizing data and performing numerical computation. You can import data in MATLAB, and explore and analyze it through built-in mathematical functions. In MATLAB, you can plot and visualize.

MATLAB language supports the vector and matrix operations. You can perform statistical analysis with the help of MATLAB.

So, these are the most popular languages that are used in Data Science. But you may be thinking, ” Do I need to learn all these Languages?“

So, the answer is, it’s not compulsory to learn all languages. But if you have knowledge of all these languages, then it’s good.

Now, the next question you may have, ” Which language should I learn for Data Science?”

So, to answer this question, I will compare the three most used programming languages for Data Science- Python, R, and Julia.

Python vs R vs Julia

Criteria	Python	R	Julia
Usage-	Python is general purpose programming language.	R is used for Data Analysis, Statistical Analysis, and Data Visualization.	Julia is used for Scientific computing.
Speed and Performance–	Python has average speed and performance.	R is a slow programming language.	Julia has high speed and performance similar to C language. Due to its high speed, there is approximately 13M downloads happened till May 2020.
Community-	Python has a huge community. That means, there is a huge Python community who can help you when you stuck at some point.	R has also huge community but not huge as Python.	Julia has small community because its a new language. It might take 3 to 4 years to build a huge community.
Libraries available	Python has more than 200k libraries. This is quite huge.	R has approximately 15000 libraries.	Julia has approximately 3000 libraries.

So, this is the basic comparison between Python, R, and Julia. But the final conclusion has still not drawn. For that, I will answer this question-

When to use Julia?

As I mentioned earlier, Julia has high speed and performance. So, If you have a huge data set, and you want a faster result, then only you should use Julia.

The next question that may come in your mind is, “Should I learn Julia or not?

The answer to this question depends upon your experience level. So I will explain for both cases-

For Experienced-

If you are an experienced person, that means you have enough knowledge of Python or R. And you have done various data science tasks in Python or R, then you can learn Julia in order to enhance your skills. Knowledge of Julia will give you more privilege as a data scientist.

For Freshers-

If you are fresher and planning to start your career as a Data Scientist, then you shouldn’t start with Julia. For freshers, it is better to start with Python. Once you are familiar with Python and perform some data science projects in Python, then you can learn Julia.

Now, you got a clear answer for both cases. The next question is, “Python or R, Which should I use for Data Science?”

Python or R

If you are a beginner then the answer is Python. Why?. because Python is easy to understand language. You can perform all data science tasks easily with the help of Python. That means, start your data science journey with Python.

As a fresher, you should have knowledge of only two programming languages- Python and SQL. That is enough for you. Once you cleared this level, then you can learn any other language.

If you are an experienced person, then knowledge of both (R and Python) is beneficial for you. Becoming an expert requires constant learning. The more knowledge you have, the more options you can create for yourself.

I hope, now you got an answer to all your questions. Only one question is left, “From where I can learn Programming Languages?

So, if you have the same question, then stay with me in the next section-

From where I can learn Programming Languages?

I have chosen some best online courses and books for you. So, let’s start with Python-

Resources for Python Learners-

Books for Python

1. Python Crash Course– Python Crash Course is an excellent book if you are a Beginner. This book will give you an in-depth knowledge of Python. Starting from Basic to the Advanced level, this book will give you a complete understanding of Python.
2. Head First Python: A Brain-Friendly Guide– If you are the one who doesn’t like to read the long heavy text. Then this book is just for you. This book is written in a more visual form.
3. Learn Python the Hard Way– Learn Python the hard way is a good book for practical exercises. This book consists of 52 brilliantly crafted exercises. This book is a guide on how to write good code and how to find and fix the errors.

Online Courses for Python-

1. Python for Everybody Specialization– This is one of the Best Online specialization programs available for Python offered by the University of Michigan. This specialization program has 5 Courses. This course will cover all concepts related to Python Programming. After completing this course, you will feel more confident in Python programming.
2. Python 3 Programming Specialization– This is another most popular specialization program for Python. At the end of this specialization program, you’ll be writing programs that query Internet APIs for data and extract useful information from them. Along with that, you’ll be able to learn to use new modules and APIs on your own by reading the documentation.
3. 2024 Complete Python Bootcamp: From Zero to Hero in Python– This course is offered by Udemy. This course is listed as Bestseller at Udemy. This course starts with the basics to more advanced concepts like building applications and games.

Resources for R Learners-

Books for R

1. The Book of R– This book is a beginner-friendly guide to R. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics. This book will provide you with a solid understanding of both statistics and the depth of R’s functionality.
2. R for Data Science–This book is suitable for readers with no previous programming experience. In this book, you will learn how to wrangle, program, explore, model, and communicate.

Online Courses for R-

1. Data Science: Foundations using R Specialization– This Specialization program is offered by Johns Hopkins University. This program will teach you foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.
2. R Programming– This is another best specialization program for the R language. In this course, you will learn how to program in R and how to use R for effective data analysis. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
3. R Programming A-Z™ R For Data Science With Real Exercises!– This course is the most popular course for R programming in Udemy. This course will cover all concepts related to R Programming.

Resources for Julia Learners-

Books for Julia-

1. Julia 1.0 Programming Complete Reference Guide- This book is a good reference book for Julia learners. If you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications, this Learning Path is for you
2. Julia Programming for Operations Research-if you want to learn the basics of Julia and apply it to optimization, then this is the best book for you. This is a great book, you can learn.

Online Courses for Julia-

1. Julia Scientific Programming– This course is offered by the University of Cape Town. In this course, you will learn how to program using the Julia language, write your own simple Julia programs from scratch, work in Jupyter notebooks, and use various Julia packages such as Plots, DataFrames, and Stats.
2. Hello Julia: Learn the New Julia Programming Language– This Julia online course will take you from complete beginner to intermediate. Anyone who has basic programming concepts can enroll in this course. This course is not for Intermediate learners of Julia. Only for Beginners in Julia.

Resources for SQL Learners-

Books for SQL-

1. SQL All-in-One For Dummies– This is the Bestseller book on Amazon. This single book will teach you almost all concepts related to SQL. If you are want to learn SQL from the book, then this is the best book for you.
2. SQL for Data Analytics– This is another book for SQL. This book not only covers concepts related to SQL but also teaches you to explore your data by identifying patterns and unlocking deeper insights. As a Data Analyst, you should read this book.
3. Practical SQL: A Beginner’s Guide to Storytelling with Data- Another great book for SQL!. This book is the alternate option for the “SQL for Data Analytics” book. This book also teaches you basic concepts of SQL as well as analyzes data from the U.S. Census and other federal and state government agencies.

Online Courses for SQL-

1. Learn SQL Basics for Data Science Specialization–This specialization program is offered by the University of California, Davis. In this program, you will learn, SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.
2. Excel to MySQL: Analytic Techniques for Business Specialization– This specialization program is offered by the Duke University. In this program, you will use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
3. Databases and SQL for Data Science- This course is offered by IBM. This course will teach you relational database concepts and perform SQL access in a data science environment. Anyone can enroll in this course.

So, these are some most popular resources I collected for you. I hope you will get benefited from these resources. Now, it’s time to wrap up.

Conclusion

As I mentioned earlier that programming languages are crucial for the Data Science field. I hope your all doubts have been cleared after reading this “Best Programming Language for Data Scientist” article.

If you have any doubts, feel free to ask me in the comment section. I will give my best to solve your queries.

All the Best!

Happy Learning!

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