Why coding holds prominence in data science and what coding habits require for data scientists?

Businesses these days are increasingly producing and leveraging a large amount of data that helps them garner much value for their business growth. This voluminous amount of data is not always enough to deliver actionable insights organizations seek. This is where the role of a Data Science expert comes into the rescue, providing all needed tools and techniques that can drive business value. Today, data science is becoming a most sought after career option across the world. As organizations across almost every industry have realized the importance of the data that they often overlooked, relevant skills and talent in data science is becoming critical.

A Data Scientist typically requires a combination of skills in mathematics, statistics, problem-solving, and programming. He/she also requires to have an understanding of how to use computers and technology to access information from large databases, manipulate data, and visualize numbers in a digital format. R and Python are the most popular programming languages used by Data Scientists today, and both are open-source programming languages with a large community.

As programming is undeniably an essential skill for a data scientist, it does not mean that candidates have to be proficient to pursue their careers in data science. Since data scientists classify a business problem, work on the given dataset, assess it from various perspectives and develop Machine Learning models to visualize and envisage outcomes, the programming skills they require rely on what area of analytics or data science they are likely to practice.

Getting Started into Coding Habits

To start learning coding, a Data Scientist must focus on one of the necessary programming languages to have the foundation he/she needs, including Python, Java, R, TensorFlow, Scala, Julia, MATLAB, and SQL. Python is the second most popular coding language in the world thanks to its flexibility and a wide range of available libraries and resources. This is frequently applied in various scenarios and supported by an active developer community.

Python programming language offers an incredible coding tool to data science programming, but it also brings challenges. As Python does not insist on strict rules, it can more easily influence coding that can harm entire projects at large. Not having abstractions, long functions that do multiple things and not having unit tests create more complexities to coding. To reduce these complexities, a data science practitioner needs to apply some habits:

Keep code clean: Keeping code clean creates room for understanding and modification of code. As unclean code adds to the complexity by making code difficult, changing code to respond to business needs becomes increasingly difficult and sometimes even impossible.

Use functions to abstract away complexity: Functions simplify the code by abstracting away complicated implementation details and replacing them with a simpler representation, its name.

Smuggle code out of Jupyter notebooks as soon as possible: Just like any flat surface within a home or office tends to collect clutter, Jupyter notebooks are the flat surface of the machine learning. They are great for quick prototyping, and amass glue code, print statements, glorified print statements, unused import statements, and even stack traces. Thus, as long as the notebooks are there, mess tends to accumulate.

Along with these habits, data scientists also must apply test-driven development and make small and frequent commits.

There are numerous institutes leading the way into offering coding programmes. For instance, Coding Dojo, a pioneer and top-leading coding bootcamp in the US, offers Java, Python and other top programming languages.