Is Data Science Hard to Learn?

This Data Scientist career guide will assist you in taking the first steps toward a successful career in the field of data science. Take a look to find out the challenges of data science and the most popular programming languages you need to master in order to be a Data Scientist. 

Due to the technical requirements that are often required in Data Science jobs, it is more difficult to master than in other technology fields. Being able to master this wide range of languages and software does require a long learning curve. Of course, this is one of the primary reasons for the current shortage of data science specialists and why they’re in huge demand. If you are not very keen to learn data science on your own, then consider taking Data Science Course.

Do Data Scientists Code?

In a nutshell yes. Data Scientists write code. This means that the majority of Data Scientists have to know how to program even if it’s not an everyday job. According to the adage, “A Data Scientist is an individual who’s more skilled in statistical analysis than every Software Engineer, and better in the art of software than almost anyone else. Statistician.”

How much programming (a.k.a. code) they actually use depends on the job they’re in as well as the software they’re employing. A few examples of aspects Data Scientists can expect to program:

  • Analysis scripts are usually written and created in R or Python to achieve the goal of providing actionable information.
  • Prototypes of digital goods. By using Python, the aim is usually to demonstrate the effectiveness of a new feature or product which allows developers to develop the feature.
  • Production code. In smaller firms, Data Scientists often have full responsibility for this, and might require the recourse to Ruby on Rails and Java (in combination with the most commonly used Data Science languages) in order to accomplish this.

Programming Languages to support Data Science

Data Scientists use a variety of programming languages in various ways during their daily tasks. However, there are some fundamental programming languages that each Data Scientist needs to master. The most commonly used programs for data science include:


With a nimble learning curve, and a wide array of libraries that permit almost endless possibilities, Python is the preferred programming language that is preferred by the multitude of Data Scientists who appreciate its accessibility, simplicity of use, and general-purpose versatility. In fact, Digital Skills Survey found that Python was the most commonly utilized tool for Data Scientists overall.

Since its origin at the time of its introduction in 1991, Python is now an ever-growing library designed to perform common tasks like data preprocessing analysis, analysis, forecasts visualization, and preservation. In addition, Python libraries like Tensorflow, Pandas, and Scikit-learn allow advanced machine learning and more profound learning-based applications. When asked about their preference to use Python compared to R, Data Scientists cited Python’s ability to speed up than R and more efficient in data manipulation.


Since it’s designed to be used for data analysis, R tends to be distinct from other software, earning its name to being more difficult to master as compared to other software for analytics. Even if you’ve had plenty of experience with other data science tools, you might be able to find R rather difficult to understand initially. It’s well worth the effort, but it has the majority of statistical and data visualization tools you as a Data Scientist might need, such as neural networks, non-linear regressions, advanced graphing, and others.

An open-source, free programming language, which was launched around 1995. It is a descendant of the S programming language. R provides a high-end collection of top-quality, domain-specific applications. The visualization library ggplot2 is an extremely powerful tool. R’s static graphics help you create graphs and mathematical symbols as well as formulas.

It’s true that Python is faster than R (and R does feature an arduous learning curve over the more accessible Python) However, for data and statistical analysis purposes, R’s extensive variety of custom-made programs provides it with a slight advantage. It’s important to note that R isn’t a general-purpose programming language like Python. It’s designed to be used exclusively for the analysis of statistics.


SQL or “Structured Query Language” has been the basis of data storage and retrieval for a long time. SQL is a specific language for a particular domain that manages the data stored in relational databases. It’s an essential skill for Data Scientists, who rely on SQL to modify, update, query, and manipulate databases and extract data. It’s a good thing that SQL is relatively simple to learn, very easy to comprehend, and a breeze to use. Since its commands are restricted to queries, SQL generally requires only 2 or 3 weeks to learn for newbies and much less for skilled programmers.

While SQL isn’t as efficient for analytical purposes, it is extremely efficient and vital in data retrieval. This means that SQL is an extremely useful tool to manage structured data, especially in huge databases.

Additional Data Science Languages

In addition to the primary language of data processing, Python, SQL, and R there are additional data science languages that could possibly have more niche applications:


While it is easier to master than its counterpart, C++, Java is a little more difficult than Python due to its long syntax. According to a few experts, it can take up to one month to master the basics of Java and a few weeks before you can begin applying these concepts in a practical manner. Java is a great tool to weave production code for data science directly into a database and a well-known software for statistical analysis, Hadoop operates in Java, which is the Java Virtual Machine. Java is well-known for its speed as well as type security and the ability to transfer between platforms.


Flexible and user-friendly, Scala is the perfect programming language for dealing with large amounts of data. Applications written in Scala can be run wherever Java is running, making it suitable for complex algorithms as well as large-scale machine learning. Scala does have a higher learning curve than many different programming languages generally it takes several weeks to master the hang of it, but the large number of users who use it is proof of its utility.


A language for programming that’s much newer than the other ones listed, Julia has quickly made an impact thanks to its lightning-fast performance as well as its simplicity and accessibility, especially for computing science and numerical analysis. It’s not that you’ll learn it quickly although it’s fairly easy to get started and start exploring right away, you should expect to take at least a few months to learn Julia. However, once you’ve got it down the language, it’s an excellent tool to perform complex mathematical tasks and is prominent in the world of finance. Since it’s a relatively young language, Julia lacks the variety of programs available in R or Python, at least for the moment.


A language for numerical computation, MATLAB is used for higher-level mathematical requirements such as Fourier transforms and image processing, signal processing, and matrix algebra. These contribute to its usage in industry and academia. If you’re a professional with a math background, you could be able to master MATLAB within as little as two weeks. Much like Julia, it’s not the same, however, MATLAB isn’t widely used by experts in data.