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Find out which programming languages you should learn to become a data scientist.
Data scientists use data to create impact and guide companies in the right direction by providing vital insights, recommendations, and systems designed to solve ambiguous problems using data.
To be able to deeply understand the information and discover the best solutions, besides needing to have strong business acumen and a problem-solving mentality, data scientists need tools (programming languages) to be able to work with big data.
- Is coding required for data science?
- Why programming is required in Data Science?
- Which programming language is the most used in Data Science?
- How much time it will take to learn to code?
- Best Programming Languages for Data Science
- How much coding do you need for data science?
Is coding required for data science?
Yes. Coding is a big part of Data Science and it’s heavily used for data wrangling, cleaning, modeling, building scalable systems, user interfaces, data visualizations, analysis, and more.
Why programming is required in Data Science?
Coding is required to support the creation of data products, systems, and models. It’s also very important to understand the fundamentals of computer science to be able to transform ideas into actual solutions that deliver value to companies.
Spending time figuring out how to solve business problems with code is a bit part of the job.
Which programming language is the most used in Data Science?
Python and R are the most popular and favorite languages for data scientists. They appear in almost all job descriptions, and are most used in companies, and most taught in universities. But they’re not the only ones. Many companies use more than these 2 languages, each serving different purposes.
How much time it will take to learn to code?
To cover the basics and have a good understanding of Python, for example, it can take you around 6 months with everyday practicing and a good course on Python. If you want to master it, then expect 2-3 years of practicing it every day.
Prepare for a long run. Even professional coders never stop learning, every day they learn and re-learn something else.
Best Programming Languages for Data Science
Python is a favorite and the most used programming language for Data Science. It’s becoming a standard language used in schools, universities, businesses – because it’s a very simple language to use and at the same time, very powerful.
If you want to become a data scientist, we recommend you start with Python, even if you never coded before. Python is a beginner-friendly language, focused on high-performance and speed, with a clean syntax.
Being a general-purpose language, it provides you with almost everything you need. Another plus is that it has a big community. It’s refreshing to have thousands of people to share your ideas with, talk about your problems, and find solutions together.
Python is free and comes with a huge amount of packages, almost 137000 of libraries and rising.
- Pandas is a fundamental library for data science, perfect for data wrangling or munging.
- NumPy allows you to work with numeric information allowing you to manipulate data with advanced math functions.
- SciPy is perfect for statistics, scientific, and technical computing and can be used in various areas including machine learning. Plus, it comes different sub-libraries.
- TensorFlow and PyTorch are vital tools for machine learning and deep learning.
- Matplotlib, Seaborn, Plotly are flexible libraries you can use for plotting data.
R was created in 1992 by statistician Ross Ihaka and bioinformaticist Robert Gentleman. It is an important player in the Data Science world and widely used in financial, medical, research, or Academia fields.
Although it can be used with a general-purpose as well, R is considered to be a more domain-specific language, focused on statistic computing and graphics.
It comes with plenty of great packages for data science, big data analysis, and machine learning.
Packages like Dplyr, data.table, and readr packages are vital for data wrangling.
R outperforms Python at data visualization. It comes with an extensive list of powerful data visualization packages like ggplot2, Plotly, Lattice, and more.
SHINY is a fantastic R framework that allows you to create interactive, beautiful, and effective dashboards, much better than Plotly.
If you have a background in statistics then R will be very easy to learn. Otherwise, it has a steeper learning curve but still, overall it’s a friendly language with a clean syntax behind it.
Julia is a powerful general-purpose programming language, built entirely for Data Science and ML, with a strong focus on scientific computing. It’s a relatively new language, growing steadily and lots of companies are starting to using it.
It has a complete ecosystem dedicated to Data Science with essential libraries like DataFrames, JuliaDB, Flux, etc.
It is JIT – just in time – compiled, which makes it faster than Python and sometimes can even reach C-level speed.
The syntax is super simple, clean, and easy to learn. If you already know Python, it’ll be extremely easy to understand Julia.
If you want to be at the top in the future, take a look at Julia and give it a chance. You can learn it online for free, and make sure you keep up with the latest updates by watching JuliaCON.
If you want a career in data, then SQL is a mandatory skill to have. 99% of the companies around the globe use SQL and will require it in their job descriptions.
SQL is a data-related system used to enable direct communication with relational databases.
It is a simple language to learn and use. When tackling a data science project, SQL is always the first step that puts you in direct contact with data you need to work with.
You can immediately see the data, perform queries and understand it quickly, manage huge volumes of data and get vital insights in a few seconds.
If you want to succeed, you need to learn SQL preferably before starting with Python or R.
Scala is a multi-paradigm language combining object-oriented programming and functional programming. It was invented in 2003, and ever since it is gaining popularity among data scientists.
As the name says, it’s meant to be a “scalable language”.
Scala has become a staple language for Data Science, using the Apache Spark framework, it’s suitable for Big Data analysis, so if you’re dealing with a huge volume of data, Scala is perfect for it.
It’s used by numerous tech companies like Netflix, Twitter, LinkedIn, and Airbnb.
Not as easy to learn, but if you know Java or C already, it’s an easy transition.
There’s a great course on Coursera made by the actual creator of the language, and it’s free, taking you from the basics of the language to Spark and Machine Learning.
C is considered to be the most powerful language in the world. C++ came after C and it is an enhanced version of C. It’s one of the fastest languages ever created and high speed comes at great use for data scientists.
With C++ you can compile over a gigabyte of data in less than a second.
Behind many modern languages stands C. Python is written in C. R has plenty of libraries that come from C, Numpy has C++ in it.
Even though you might not need it immediately, it’s still important, at least understand the basics of coding in C++.
Where you can learn it? You can start with “The C++ Programming Language” book, written by the creator of the language, Bjarne Stroustrup.
Stanford, Yale, MIT also give online lectures on Youtube, for free.
MATLAB is a very good system for data science, but the only downside is that is not free. It’s quick, perfect for statistical analysis and dealing with complicated mathematical needs.
MATLAB is well developed and suitable for deep learning, machine learning, and even graphs.
Pandas, ScikitLearn, MAtplotlib – are libraries that come from people from the MATLAB world trying to make an open-source way of working with MATLAB.
Java is one of the oldest high-level programming languages, that stands behind a huge range of enterprise-level applications (web and mobile).
While this is not the first choice when it comes to Data Science, Java is great for writing machine learning algorithms due to its wide applicability and its dedicated libraries for DS and ML: Weka, Java-ML, MLlib, and Deeplearning4j.
It is a hard language to learn, and if you are to choose between Scala and Java, you should start with Scala as it’s easier to pick up.
JS comes in handy when you need to scrape the web or to better understand the logic behind the dashboards created with R and Python / DASH and SHINY.
Excellent for statistical analysis and one of the oldest systems built for analytics, SAS is a popular choice in the big pharma, government, health, and finance world.
Just like MATLAB, SAS is not free. Top companies are using it for its high reliability and authority it gained over the years.
Golang is a powerful programming language similar to C designed by Google in 2007.
It’s way faster than Python, and a good alternative to C if you want to speed up your algorithms. It’s an interesting choice, not only for its speed but also because it comes with data science libraries like Gota, GoLearn, Gonum, qframe, Gorgonia, and more.
Data science involves a lot of coding and you will be required to code a lot to be able to solve problems.
Python and R are the best languages you should learn if you’re interested in a career in data. We recommend you to choose first either Python, either R, and try to master one first before diving into other languages.
SQL is a must-know data skill that is required in all companies, so it’s safe to say that you should definitely learn it from the early beginning.
Next, after you’ll have a foundation in R/Python and SQL, depending on your company size and role, you can look into other technologies like Scala, Julia, C++, or others.
How much coding do you need for data science?
Do you need to be an expert at coding?
The level of your coding skills depends on the role and the company’s size.
Startups will usually have fewer data scientists, maybe even just one. This means that you, as a data scientist, will have more responsibility and could end up doing lots of tasks data engineers data analysts would normally do.
For example, you may have to create data infrastructures and build pipelines, code a lot, build programs for data collection, analyze data, perform A/B tests yourself, meaning that you’d do less or no AI/ML stuff.
Medium-size companies will have more resources to hire software engineers, data engineers, analysts, and data scientists. As a data scientist, you will have the freedom to focus more specifically on the analysis, strategy, aggregations, testing, building models, and deep learning.
Large companies with huge budgets will hire more people to focus on the tasks they’re best at. This adds up Research Scientists, ML Engineers, Machine Learning Scientists, Application Architects, and more.