Do You Need To Learn OOP For A Job In Data Analytics?

Data analytics aspirants are often trying to find the answer to one of the most nagging questions in this field: Do I need to learn object-oriented programming for starting a career in analytics?

Object-oriented programming (OOP) can be intimidating to many aspiring data analysts as it can get a little tricky while implementing classes and their objects. But do they really need to grasp object-oriented programming skills?

An Analyst’s KPI

Aspiring data analysts do not create classes and define because as an analyst, their day-to-day activity revolves around data collection, cleaning, analysing, and visualising. These processes do not involve defining a class, but it does involve utilising Python and R functional programming.

Defining and calling functions are the most important skills they should thrive to achieve as an aspiring analyst. Proficiency in functional programming is adequate to get started in their career and efficiently perform data wrangling.

Besides, to visualise through dashboards such as PowerBI, Tableau, and others, they need to acquire the knowledge of the specific language of that tool, which again does not involve object-oriented programming.

Why Should An Analyst Learn OOP?

Although one can start one’s career in analytics without object-oriented programming skills, it is still highly recommended that analytics aspirants should understand at least the basic concept behind OOP. 

While you may not be asked to define classes, but throughout the data analysis process, you will need to engage with objects of various classes. Without knowing, one implements different methods on objects to obtain desired outputs.

For instance, lists are objects, on which you can call different methods such as append, insert, extend, and more, to handle data. Though a list will be taught as one of the many data structures in Python, it is a mutable object of a class, thereby, making it a necessary skill to understand at the minimum.

If not for implementing, one needs to be familiar with object-oriented programming to know what they are doing. This will empower them to use their skills in more efficient ways for deriving insights into data and making informed decisions.

In The Future

As an aspiring data analyst, one will in the future seek skills of data science or artificial intelligence. At this juncture, you will have to learn object-oriented programming as data scientists are expected for solving organisations specific challenges. Owing to this, data scientists implement object-oriented programming to make bespoke machine learning models and unriddle problems.

To implement such practices and assist companies in their business growth, you will have to learn object-oriented programming as off the shelve machine learning models do not resolve every challenge that a firm witness.

As a data scientist aspirant, you will have to master the skills of object-oriented programming and obtain the amenity of a proficient data scientist.

Outlook

For someone who is entering into analytics can give object-oriented programming a miss, but later in the career, you will have to learn it in order to prosper. However, the ideal option would be to master functional programming while gradually learn to write classes, create objects, and execute complex programs. Besides, embracing object-oriented programming will enable you to use other codes, thereby, increasing the productivity and efficiency of your performance.

Object-oriented programming can be overwhelming for some, but once it is mastered, you can carry out strenuous tasks and mitigate several challenges in organisations.

Naively, learning Python and R programming will not assist you in differentiating yourself from the competitors to strive in this competitive marketplace. Thus, it is important in the later stage of the career to be an expert in object-oriented programming for a better future in the data science landscape.