Data science is one of the latest emerging fields with high potential growth. The adoption of cloud-based solutions and the usage of big data are driving growth for the data science industry. The market size of data science in 2022 was USD 122.94 billion, and it is estimated to surpass USD 942.76 billion by 2030.
Because of the market potential of data science, many people want to get into the data science industry and enhance their career options. There is a high demand for various data science jobs, including data engineer, data scientist, data analyst, data architect, etc. Beginners and even working professionals from non-programming backgrounds want to know if coding is required to get a job in the data science industry.
Does Data Science Require Coding?
From a technical point of view, coding is necessary for Data Science tasks. Since data science combines technology, mathematics, and business, you need to have technical knowledge and coding skills to land a high-demand data science job. However, it is important to remember that the data science field is still evolving, and the latest technologies are being developed every now and then, which is making it possible for people to work on data science projects without even writing the code. For people who work in less technical roles, these technologies are making their coding job much easier.
Lately, no-code or low-code solutions have become popular, which gives no-code or low-code experts a chance to try their hand in the data science field. Nevertheless, for highly complicated and custom data science projects, it is essential to have high-end coding skills. If you want to land a job in the data science field, it is better to learn basic coding skills, at the very least. If you wish to build your career in the data science field and upskill your career options, it is recommended to master coding skills.
What Are The Basic Requirements For A Non-Coder To Become A Data Scientist?
If you are a non-coder, here are some requirements or skills you need to possess to work as a data scientist:
- You need to have a good understanding and knowledge of mathematics, especially statistics and probability
- You should be able to identify business problems quickly and efficiently
- You should know how to extract valuable insights from specific data sets
- You must know how to visualize various model outputs
- You must have the ability to study data from multiple standpoints
- You should have basic technical and programming skills
- You also need to master analytical skills, communication skills, problem-solving skills, data modeling skills, critical thinking, etc.
What Programming Languages Are Vital For Data Science?
As the data science field is evolving rapidly, there are high chances for you to get a high-paying job if you master more than one programming language. Here are some of the best programming languages for data science.
1. Python
Python is a general-purpose programming language that is used for a wide variety of data science tasks. It is an open-source language and is great for automating multiple tasks quickly. Python includes a sizable library of modules that are primarily used for data analysis, data visualization, and ML. Python is relatively easy to learn, so you can master it within a short time. For data science jobs, you need to have advanced Python skills as this language is used for data analysis, data visualization, ML, etc.
To expand your skills and knowledge of Python, the below comprehensive articles can be a valuable resource:
2. Structured Query Language (SQL)
Structured Query Language (SQL) is a database language that people who want to get into the data science field should learn. You need to master SQL to get into a data science career as this language is mainly utilized for accessing and examining data from various databases. It is the ideal choice for data querying and data manipulation. With SQL, you can automate various tasks such as summing up averages, grouping data, discovering the minimum and maximum values in datasets, etc.
If you want to gain a better understanding of SQL, you can find plenty of information in the following informative articles:
3. R Programming
R is one of the best scripting languages and advanced programming languages that is widely supported. This open-source language has numerous libraries, R packages, and coding tools that are ideal for complicated and quantitative applications. R programming is generally the best choice for statistical analysis and statistical computing tasks with mathematics and graphics, so you must have advanced skills in R language for data science jobs.
If you want to learn more about R programming, you can go through the detailed articles listed here:
- Learn R programming
- Clustering in R programming
- AB Testing With R programming
- Data Handling in R programming
4. JavaScript
JavaScript is an easy-to-learn, object-oriented programming language which also comes with a good amount of libraries. This scripting language is utilized for a wide variety of tasks, such as data analysis, machine learning, data mining, etc. JavaScript is the best option for integrating applications into big data science development projects. Fundamental knowledge and skills of JavaScript is enough for a data science career.
Expand your knowledge of Javascript by checking out these extensive Javascript articles:
- Javascript complete guide
- Introduction to Javascript
- Debugging in JavaScript
- JavaScript variables and datatypes
5. C/C++
It’s recommended for data scientists to learn the C/C++ programming language. Having strong C/C++ skills makes it easy for you to collect data swiftly without any difficulty. This programming language is the ideal option for data science projects that require high stability or performance. . If you want to become a data scientist, it is vital to have a solid foundation of C/C++.
For entry-level data science jobs, it would be enough if you knew the fundamentals of these programming languages. However, it is vital to possess extensive knowledge of these programming languages for senior-level data science jobs.
If you are interested in learning more about C/C++, you can read the below comprehensive articles on C/C++ programming:
How Much Coding Is Required For Data Science?
Based on the career role, the amount of coding required for data science jobs will vary. Scroll down to get an overview of how much coding is necessary for the following data science positions.
1. Data Engineer:
If you want to work as a data engineer, you need to be familiar with the basics of R or Python language and master SQL or any data query language. You need to have thorough knowledge about distributed computing frameworks, stream processing frameworks, and ETL frameworks. As the popularity of Google Cloud, AWS, Azure are increasing, data engineers should master cloud technology.
2. Data Scientist:
Data scientists need to master SQL to handle various tasks like data collection, processing, and data storage. They should be skilled in Python and R languages which are essential for data visualization, statistical analysis, etc. Data scientists also need to be familiar with Apache Spark and Hadoop Platform to convey data swiftly and handle big data tasks.
3. Data Analyst:
If you want to work as a data analyst, you need to acquire basic programming skills. For instance, R and Python skills are essential to automate data tasks and work on advanced data analysis, statistical analysis, etc. For data extraction and database querying, you need to be familiar with SQL.
4. Machine Learning Engineer:
To become an ML engineer, you should master various computer science concepts. You need to excel in R and Python languages to handle statistics and machine learning concepts. You must have expertise in SQL for database management, data processing, etc.
What Are The Benefits Of Coding In Data Science?
If you have coding skills, it will be easy for you to collect the data, regardless of where it is stored. In addition to that, coding helps you manipulate the specific data. Having essential coding knowledge makes it simple to manipulate, fix, and convert the data from the data sets as needed. You can analyze even huge data sets with incredible accuracy and speed. In addition, you will be able to get better insights about your data. If you know coding, you can easily develop algorithms that will help you automate repetitive, tedious data science tasks.
With coding, you will be able to visualize the given data in numerous ways, which makes your job much easier. Data visualization allows company stakeholders to see the entire visual process of solving the issue and make better decisions that will drive business growth. Data in the datasets have specific patterns. If you master coding, it will be easy for you to interpret the data patterns. In addition to that, extensive coding knowledge helps you create data models and use ML on big data sets.
How Coding Is Helpful To Overcome The Difficulties Of No-code Approaches?
There are some limitations to no-code approaches, which you can easily solve with code.
Problem 1. Do you often face problems when tracking changes?
In that case, you can use code along with a version control system, which makes it easy to track changes. You can instantly know what changes are made, who made them, when they are made, and why they are made.
Problem 2. Are you encountering restrictions when it comes to data analysis methods and presentation formats?
With code, you will be able to easily analyze big sets of data and present the data that is required in the personalized dashboards and reports. In addition to that, you can analyze and present the data quickly and more precisely.
Problem 3. Is it hard to reproduce and expand work?
With code, you can reproduce the work because it is possible to record each and every step. In addition, you can copy, paste, and adjust the code in order to adapt to the issues that arise depending on the situation. Also, you can deploy the open-source code on a wide range of platforms and it does not depend on any proprietary tools.
Problem 4. Is there not a sole source of truth?
You can use code and many other centralized tools in order to build a single source of truth for your data, models, and dashboards as well. With version control, you can track numerous versions of code individually, so there will be no disputes.
Conclusion
Without any coding skills, you can still work in less technical and managerial roles in the data science industry. However, if you have incredible coding skills and technical knowledge, you can get a high-paying job with job security that enhances your career. To ensure a great career in the data science field, make sure to learn and master various programming languages, including Python, Structured Query Language (SQL), R programming, JavaScript, C, C++, and many more.
