Does Data Science require Coding or Not?

Does Data Science require Coding or Not?

Data Science is a field that is a combination of mathematics, business and technology. In a constantly evolving field, the mathematical understanding of Data Science remains consistent. Now, it is a question of the rest. Let’s understand more:

• Business: Data Science is a business-agnostic field. Whatever domain you come from, you can leverage your business knowledge to do better data science.
  For instance, if you are from a CA background, you can help Fintech companies. In addition, given your strong understanding of financial data, you can understand more than most. However, based on your interest, it is possible to work in any domain of Data Science.
• Technology : Technology is a field that keeps evolving every day. A lifelong learning mindset must be applied to keep up with the pace of technology.
  Once we understand the foundational elements of technology, it becomes vital that we keep upgrading ourselves based on the latest technology, like by doing the insight data science Bootcamp.

Why Is Coding Required in Data Science?

Data Science is a field where experiments are carried out on data to help improve the quality or bottom line of the enterprise. We just use project specific tools to analyze data. Large volumes of data are generally present on a cloud platform, and a Data Scientist must perform analytics.

To do this, a Data Scientist needs to have a robust toolkit where they are free to experiment. Any experimentation, data manipulation and visualization should be possible to strive to achieve the end result. It’s not engineering; it’s actual science that consists of performing experiments, where some succeed, and most fail. 

Coding is required in Data Science because:

• Sourcing Data: Regardless of the cloud platform or source, code can help get the data from wherever it is stored. Code enables us to manipulate data while pulling it right from the start.
• Data Transformation : Knowing how to code can help to manipulate, fix and transform the data as required — this can be done via multiple platforms. For instance, Python code can be applied on almost any cloud platform or tool.
• Exploratory Data Analysis : The patterns in data can be deciphered with the help of code; it is vital to explore large datasets to understand the visible and hidden patterns.
• Experimenting with Data : Working on different hypotheses to see if there is backing for a data-driven decision, can be done with the help of code.
• Machine Learning & Modelling : Having the freedom to make models and perform machine learning on data, can be done with the help of code.
• Visualization: Giving a Data Scientist the ability to visualize data in multiple ways is a powerful tool. It can transform how we go about solving a problem, as visualizing data can help business stakeholders make data-driven decisions better.

In this section, let’s go over some of the roles and the amount of coding that is required:

Data Engineer

A data engineer would need to be an expert in SQL or a data query language and understand the fundamentals of Python/R to manipulate data as required. A knack for attention to detail can help you become a better Data Engineer.

Machine Learning Engineer

A Machine Learning Engineer needs expertise in a coding language such as Python/R and understands the fundamentals of a querying language such as SQL. Value addition for this role is the fundamentals of Software Engineering, like basic Data Structures.

Business Analyst

Depending on the company you are applying for, this is a role that requires less coding. Understanding the fundamentals of SQL and a visualization tool such as Power BI and Tableau can help you become a better Business Analyst.

Data Scientist

A Data Scientist needs to know everything mentioned above. There must be a keen interest to learn, irrespective of the technology stack or problem. Data scientists must keep learning throughout their career, irrespective of platform, coding language, tools and technologies.

Python

Data scientists worldwide primarily use Python as their language of choice. It is a highly diverse language and fits nicely into multiple technology stacks companies use. Python also has excellent support from the developer community.

Without fail, it is asked in all Data Science technical interviews. The focus should be on mastering concepts and general logic rather than trying to become an expert in the syntax of Python. Language(s) simply enable you to implement logic.

SQL

Companies test SQL as a fundamental querying language skill. SQL enables us to query databases in a simple language. SQL is a reasonably intuitive language to learn and can be one of the first languages to pick up to give you the initial boost of confidence.

How Can You Start Learning Coding for Data Science

Sitting on the fence about if Data Science is for you? Have you been trying to understand how you can get a head start to get your foot into the door with Data Science? As your research might be pointing to gradually, learning to code is the best way to get into Data Science. There is an inherent fear of the unknown. Coding can seem daunting, like doing advanced algebra as a kid. However, it is simply a barrier you must overcome to be a good Data Scientist.

What Jobs in Data Science Require Coding?

All jobs in Data Science require some degree of coding and experience with technical tools and technologies. To summarize:

• Data Engineer: Moderate amount of Python, more knowledge of SQL and optional but preferrable is knowledge on a Cloud Platform.
• Machine Learning Engineer: More amount of Python, a moderate amount of SQL and a keen interest in experimenting with data.
• Business Analyst: Strong understanding of business, knowledge of a visualization tool, minimal coding (depending on company profile for Business Analyst).
• Data Scientist: End-to-end understanding of the data pipeline. Needs coding.

Conclusion

Now you understand whether coding is required for Data Science and the answer is a resounding yes! Many of these opinions have been formed, having spoken to over 2000+ people in Data Science. Depending on your nature and the role that you are going for, there are multiple ways you can and will pick up on coding!

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