Does data science need coding?

There has been an increase in data scientists’ demand. This is one of the disciplines presenting some great opportunities for those already in the field and students. There are opportunities even for those with no experience or background in data science. People are curious about the field more than ever. You need to have the necessary skill sets to excel in data science. At some point, you may need coding.

Big data has become an important part of businesses today. Organizations can work on mining, customer retention, and product development by understanding data. The services of a data scientist are needed to grow and sustain businesses today. It is one of the ways you can stay a step ahead. However, coding and its importance in the field intimidates most people from pursuing a data science career. 

Data science and coding

You don’t need coding to land a job in data science. Some scientists handle their jobs without doing a lot of coding, if any. However, you must appreciate that most data scientists know how to code. Also, you would need coding language to be in data science long-term. 

In the traditional sense, roles in data science require one to have coding skills. The most experienced people in the field code. We note that the landscape keeps changing, and some technologies allow you to complete projects without typing a single code. 

Technology’s primary purpose is not to remove coding from data scientists’ skill sets. It aims to make basic data analysis levels accessible for those without technical skills. However, data scientists must still utilize code for bespoke and complex solutions when technology is used as it should. 

Programming languages popular in data science

Every programming language has its advantages and disadvantages. They have different popularity levels. They include:

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Python

Python is on top of our list. It is popular and easy to learn. It is used in data engineering to create visualization and handle machine learning algorithms. In addition, Python has many data science libraries that can be used to manipulate databases or generate data visualizations. 

R

This is different from Python. R was created for use in data science. It is the most commonly used language for statistical analysis and machine learning. It is also incredibly helpful in data visualization and manipulation. 

SQL

SQL stands for the structured query language. This domain-specific language is used for database interactions. Most people who learn this language first start with R or Python. SQL’s syntax is quite simple. It is one of the easiest languages you can choose to learn. 

JavaScript

This is another well-known scripting language. It is an easy language to learn. The language was not a data science language initially. However, people have started using it and applying it in the area. There aren’t many libraries like those of R and Python associated with Java, but it has great potential. 

Which language should a data scientist start with?

There are many ways that you can follow to learn code. You can do it through paid classes or independently using online resources. When choosing a coding language, most people often start with R or Python. If you have software engineering experience, one of the languages can come across as more intuitive. There is no valid reason for beginners to pick one over the other. 

Some people assume that choosing a programming language is the important part. However, Python should be your pick if you want the most popular language with the best support. 

You can learn code by going to school and doing a course where all aspects are covered. You may also choose to create a curriculum for yourself. You can learn through:

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Education websites offering dedicated coding

There are various coding websites that you can take advantage of. In many cases, accessing your lessons is very easy on such websites. Some of the dedicated coding websites include:

• W3Schools: This free coding website offers lessons and courses on all programming languages. You can access some of the most popular libraries on their website. For example, you can learn Java, SQL, Python, and R.
• Codeacademy: this is another free website that offers courses in Javascript, SQL, R, and Python. It has special sections that focus on machine learning and data science. 

Boot camps

These are intensive courses taken onsite on online. They are aimed at beginner-level learners to help them be job ready. This is made possible by having hands on projects, mentor programs, and online communities. The duration of time taken to complete a boot camp depends on where you are learning from. Boot camps are usually very intensive and cover topics in the shortest possible time. 

Online Courses

These are independent kinds of courses. They usually consist of quizzes and structured resources that help one progress in a topic. In addition, there are many programming courses on different online sites such as Coursera. In some cases, you get a certificate once you complete it. 

Online Communities

There are online communities that can help you learn to code. This includes communities like Slack, discord, and Reddit. There are independent forums as well that can help you in learning. 

Kaggle

Kaggle is one of the largest online communities where machine learning and data science professionals share their projects and data sets. They can interact and work together. Competition between programmers is also very common. 

Decathlons and coding challenges

With such challenges, you can socialize and test your skills. It is always a good idea to connect with same-minded individuals. There are some prevalent annual challenges you can take part in. There are segments for junior and senior programmers. 

Conclusion

Programming is a significant part of data science, with or without technology making it more accessible. You should decide how far you want to take your career. While there is more to data science than technical expertise, programming is a big deal, especially for those playing more prominent roles in this field. Getting background knowledge in coding gives you a better chance of excelling in this field.