Crucial Programming concepts for Data Scientists
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Crucial Programming concepts for Data Scientists
  • Introduction
  • Understand programming basics for a career as a data scientist
  • Examples of Programming Language
  • Know the Basics
  • Benefits of knowing programming for a data scientist
  • Programming concepts are crucial as a data scientist
  • Python
  • R language
  • Structured Query Language (SQL)
  • Scala
  • Takeaway


The demand for big data is increasing with every passing year. The increasing demand for data technology is leading to higher demand for data scientists.

Many people struggle with the programming aspects of the more lucrative data scientist jobs. If you plan to become a data scientist, then one of the first skills you should learn is basic programming.

Programming is a valuable skill that can be used in multiple industries, including Data Science.

Understand programming basics for a career as a data scientist

Modern culture recognises the importance of programming because technological development continues and advances with each day. Big data relies on programming to some extent.

Some people who want to develop their own apps and programs are looking for ways to learn how to program. In modern society, programmers need to know how to handle big data. When you are just starting out with programming languages, a few basics will come in handy.

The following will talk about three key concepts to guide your journey into coding.

Examples of Programming Language

Programmers have the freedom to pick and choose from one of multiple computer languages. C#, Java, and Python are all types of programming languages. They have different functions for different needs.

If you’re interested in becoming a data scientist, three of the most popular languages are Java, Scala, and Python. There are other programming languages that may be useful for adding to the size of your big data projects.

Instead of trying to learn many languages at once, try focusing on one language and build the others off that. Begin with one language and learn the basics before learning a second. As you become more capable and competent, you will be able to tackle other programming languages.

Know the Basics

Learning the concepts and algorithms that will turn your data into programs can be overwhelming, but jumping in is essential. It is unwise to create your own big data program at the onset, as you would be risking getting yourself confused and overwhelmed. You may think you need a global object. However, without understanding variables, an object is meaningless to you.

When coming up with an app idea for the first time, start with smaller ideas. You don’t need to focus on creating your final product immediately. Learning a new language is difficult. It also won’t turn out the way you want it if you tackle too many things at once. Instead of starting with everything all at once, focus on learning one small part and practicing until you get good enough before moving on to the next one.

Benefits of knowing programming for a data scientist

For instance, imagine you become a manager for a big data company. Your team is determined to create a program that will make things easier for your business, but they have no idea how to code. If you are not given enough time to create the program, there can be a division between the team and yourself.

Learning different programming languages allows you to become more marketable, work with others and build up your reputation in the business world. It doesn’t hurt to learn programming languages and they could help you in the long run.

Programming concepts are crucial as a data scientist

In order to be a qualified data scientist, it’s important to understand big data. The most difficult part about learning programming languages is getting past the initial barriers, but it’s worth it because you can start to handle them with some basic knowledge. It is enough to use your time learning the basics concepts of programming languages and app development so that you can create high-quality big data programs.

Let’s look at the top crucial programming languages that every Data Scientist must know.


Python is the best language to learn if your goal is to build up a skill in data science. It has a little more flexibility than most other programming languages, and you can use it for just about any task involving data science. You can use Python to write code for any kind of machine learning task, including building new tools and libraries.

Important facts about Python

  • 66% of data scientists apply Python on a regular basis.
  • 84% of the data scientists surveyed use Python as their primary programming language.
  • It’s safe to say that Python will maintain its top position in the coming times.

Pros of Python

Python is an established programming language that allows users to make any type of project- from machine learning applications to simple programs.

Python is an intuitive language that’s simpler and more clear than other languages. It’s best for beginners.

The public domain has all the essential and additional tools.

There are many libraries and add-on modules that will solve most of your problems.

Python’s usage in projects and tasks

Python is the best language for quantitative and analytical projects, such as those in finance. YouTube and Google use this language to work on their internal infrastructure. In addition, Forecast Watch, which is a data analytics service, uses it for weather data.

2.R language

R is one of the most important programming languages in data science, and it also tops statistical calculations compared to all other known tools. R, a programming language that is also a suite of software for statistical analysis. It permits you to work on operations that include mathematical modeling, data processing, and graphical display.

Important facts about R language

  • Individuals with expertise in the R programming language are paid the highest.
  • More than 70% of data miners are still using R.
  • There are over 2 million users of the R language worldwide.

Pros of R language

R is available as open-source software and can be used with several operating systems. The R tool runs on any platform.

One of R’s best features is its capability to visualise data.

R’s usage in projects and tasks

Credit card fraud detection systems can be built with the R language, which can also help to analyze the sentiments of consumers.

3.Structured Query Language (SQL)

SQL is important for processing large amounts of data. This is because it has a combination of transactional and analytical capabilities. SQL is the most crucial requirement for specialists in data science.

Important facts about SQL

  • 72% of companies consider SQL as the biggest driver of business intelligence.
  • 94% of data scientists label SQL as an important skill for data science professionals.
  • Pros of SQL

    • The most important advantage is standardisation.
    • SQL provides a fast search to your data because of its high speed.
    • The simplicity and flexibility of technology.
    • Compliance with data science workflows.

    SQL’s usage in projects and tasks

    The market for SQL is huge, and it’ll continue to grow due to the integration of data across all departments in businesses. The open-source community will make sure that users can easily access free tools.


    The most valuable feature of Scala is its ability to work on parallel processes when running big data arrays. In addition to this, Scala works with JWM and thus makes available the Java ecosystem. A benefit of Scala is that it was created with data scientists in mind. This can be accomplished with approaches that offer more flexibility, helpful during development.

    Important facts about Scala

    The Scala ecosystem is growing.

    It supports asynchronous programming and can be used in embedded systems. Its compatibility with Java also contributes to its growth rate.

    Scala offers a fast and flexible language, for the development of concurrent applications and services. Pros of Scala

    Scala’s most important advantage is its performance when dealing with large amounts of data.

    The ability to use multiple paradigms and functional programming raises Scala’s value in the field of data science.

    Pros of Scale

    Scala, a combination of functional and object-oriented programming languages, is the most suitable programming language for big data.

    Scala has many libraries that are widely used in data science tasks, such as Breeze, Smile, and Vegas.

    Scale’s usage in projects and tasks

    Scala is a great technology to use when your project features comparatively large quantities of data. However, with less data, R and Python are better languages.


    The landscape of data science evolves swiftly and new tools for extracting value from this field have also increased in numbers. Learning any one of the above-mentioned programming languages will help kick off your career in data science.

    Though Python and R are fighting for the top spot, gaining proficiency in more than one data science language is invaluable.

    Do you know of any programming concepts that data scientists should learn?

    Please share them with us. We look forward to it.


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