Why Coding is important in Data Science

Today, all leading organizations across the world are using the data generated by their business to their advantage to not only remain competitive but to make key decisions across all departments. Data Science is increasingly becoming a lucrative career option.

Over time, every business accumulates a lot of information from various touch-points. No matter how much care has been taken to collect the data, errors are investable. Also, the information collected can become outdated and may require updates or cleaning. The cleaning process involves either removing or updating of incomplete data, removing duplicates, imputing missing values, format improperly formatted data, and so on.

According to various studies, Data Cleaning constitutes 57% to 60% of the weight in a Data Science project. Knowledge of any one (or more) programming language(s) is very critical. The two most popular programming languages used by Data Scientists today are R and Python. Both R and Python are open source programming languages with large developer communities. There are specific libraries for data cleaning (also referred to as Data Wrangling) that can help you in coding programs for unifying mess and cleaning your data.

Machine Learning is primarily creating models that can extract and predict information from the data. This will require a combination of Mathematical, Statistical and Programming skills. You may not be required to write many lines of code as you would need in-system programming for operating system / compiler programs. But certainly, coding will be involved in analyzing data and making predictive models using pre-made algorithm / libraries.

Big Data platforms like Hadoop and Spark require strong programming skills in MapReduce (Java) and Scala / Python, respectively.

As Data Science Managers, will you be expected to code? Probably you may not need to code but are definitely required to structure business logic into decision logic for somebody to code and set up the environment.

Leading organizations have adopted the practice of conducting online coding challenges before selecting any candidates. It is critical that aspiring Data scientists are able to crack these challenges before moving further to subsequent rounds.

By AbhijitDasgupta, Director – Data Science Programs, S P Jain School of Global Management