Programming: Is it necessary for data science?

One question that every aspiring data science professional is confronted with is – Is programming essential for data science? The short answer is yes.

Programming is an essential skill for a data science professionals to work successfully. However, it’s not the ‘most important’ skill that decides the performance of a data scientist. There’s a lot more to learn to effectively work as a data scientist. If anything, programming is an ancillary skill that complements more important skills. More on that later.

Let’s learn what do data science professionals need.  

A data scientist requires a multitude of technical and non-technical skills. The trait that defines the prowess of a data scientist is his/her problem-solving skills. Data scientists are confronted with pressing business challenges. They have a multitude of tools and technologies at their disposal. They are expected to decide the best approach to find a fitting solution in the interest of the business.

Solving a business problem requires – gathering, cleaning, processing, and deriving insights from a large amount of data. Knowing the best statistical technique to analyze a dataset is far more critical to solve a problem and excel as a data scientist.

Your ability to dissect a problem in quantitative terms is more critical to becoming a data scientist. Problem-solving skills is the major focus area of any data analyst certification, training, or course.

What skills do data scientists need?

As a data scientist, you need the following major skills.

Statistics — Concepts such as probability mass function, correlation, central tendency, population sampling, are critical to analyze and understand patterns in data. Regression, dimensionality reduction, and Bayesian statistics are the foundation of data science that help professionals arrive at a befitting decision.

Big Data – Organizations have amassed a huge amount of data in their databases. These databases can be scavenged using SQL, NoSQL, and other modern database technologies. So familiarity with these technologies is essential to thriving in a data science role. Spark, Flume, Hadoop, Cassandra, are prominent tools that enterprises use to store and process data. Valuable data analyst certifications from DASCA, IBM, Microsoft, and other renowned

Data visualization — Data scientists are tasked with the responsibility to present findings to business stakeholders, requiring them to know tools like Tableau.

Machine learning – Building a predictive model is the culminating task that defines the success a data scientist. Models require extensive use of machine learning algorithms. K-nearest neighbor, Random forest, Support Vector Machines, and Decision Trees are frequently used algorithms. Understanding these algorithms and where and how to use to build an intended model is a key skill to thrive in a data science role.

Business acumen and soft skills – Before solving a problem, it’s important to understand what matters to a business. A comprehensive understanding of business operations allows data science professionals to think through and come up with a data-driven approach to find solutions that is in the interest of the business.

 Additionally, data scientists interact with C-suite executives and leaders in the highest rung of the hierarchy, which requires them to possess good communication skills. Data science projects are long–term and require frequent collaboration with other departments. Thus, interpersonal skills are necessary to grow in data science.

Neural networks and deep learning – The role of data scientists isn’t limited to suggesting solutions. They are expected to work with machine learning engineers, data engineers, and software engineers to build products that can effectively predict outcomes based on ongoing situations and/or suggest solutions to problems based on data. Building products that can predict accurately and keep getting better at it, requires the use of various neural networks, RBN, and autoencoders. Knowledge of deep learning libraries like Keras and tools like Tensor Flow is also required to excel as a data scientist.

Where does programming come in all of this?

Programming allows data science professionals to execute all the above functions. R and Python are the most widely used programming languages. As code for statistical techniques and other numerical operations remain nearly the same, there’s not much effort in writing code. However, how to change the code with a change in approach is a key skill for data science professionals.

In brief, programming may not be the critical skill to succeed as a data scientist, but it’s a skill data scientists can’t be data scientists.

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