5 Latest Data Science Skills That Are On A Rise In 2019

emerging data science skills

In the ever-changing data science landscape, skillset evolves as new tools and techniques surface. Based on the trends, data scientists should focus on emerging and most in-demand skills to stay abreast of the changing needs.

Although data scientists possess several skills, they utilise only a handful of them due to the current requirements. This makes them experts in some skills while being mediocre in others. However, with the shift in the market, they need to reposition themselves for being relevant and add value to the businesses. Moreover, data scientists also need to embrace the latest tools and technologies for automating their tasks by improving skills.

We have compiled a set of emerging skills that are pivotal for any data scientist. Here is the list:


Sign up for your weekly dose of what's up in emerging technology.
  1. AutoML
  2. Explainable Machine Learning models
  3. Custom Neural Networks
  4. Reusable Code
  5. Natural Language Processing


AutoML is not only helping professionals getting started with machine learning, but also data scientist to train and deploy ML models that are highly efficient. Utilising AutoML, data scientists can quickly evaluate among models, train, evaluate, and deploy them. AutoML frees up the time of data scientist by automatically managing the tasks involved in ML, thereby, enabling them to focus on solving problems than in selecting ML models.

Today, many vendors provide AutoML solutions with different functionality, thus it is paramount to understand their capabilities and leverage the one that aligns well with their organisations. This will streamline their workflow to become productive and efficient while unveiling insights into data to make business decisions.

Download our Mobile App

Custom Neural Networks

While off-the-shelf neural networks work for some cases, often businesses witness challenges that are unique to their domain. Consequently, data scientists should blaze their trail to build neural networks that can address the unique problems of their organisations. 

Almost all data-driven firms are striving to build in-house machine learning models that could effectively mitigate their problems. Such companies are able to make great strides in their landscape and gaining competitive advantages. Therefore, data scientists should ensure they are proficient in their object-oriented programming to make robust machine learning models and assist firms in achieving their objectives.

Explainable Machine Learning Models

Biased insights can lead to making wrong decisions, and in turn, afflict the organisations. In today’s highly competitive marketplace, one wrong decision by companies can veer them towards bankruptcy. Thus, data scientists need to be critical of the results that machine learning models return. 

Besides, data scientists should make sure that they explain the reason behind the results that both bespoke and off the shelf machine learning models provides. To do this, they must upskill and enable themselves to describe the factors behind the code.

Reusable Code

Unlike software developers who reuse their code for various projects, data scientists fail in adopting practices to improve reusability. This slackens their process of moulding and analysing data to find insights into it. 

Reusability of code is one of the longest standing predicaments for data scientists. They should make efforts in expediting their analysing activities and completing projects swiftly. Doing so will not only help them in shortening their time in delivering projects but also to other data analysts in their organisations.

Natural Language Processing

While this has always been a part of data science, it has gained more steam than ever due to the advancements in voice searches and text analysis for finding relevant information from documents. Besides, with the increase in the number of fake news, today, it is important to flag such information by automating the text analysis.

With voice search and voice assistants becoming the next paradigm shift in AI, organisations are now possessing a massive amount of audio data, which means those with NLP skills have an edge over others. 

Data Scientists form the backbone of any organisation’s success and employers have set the bar high while hiring these unicorns. It is highly important for this breed of professionals to enhance their knowledge in the aforementioned emerging skills to differentiate themselves from others. 

Support independent technology journalism

Get exclusive, premium content, ads-free experience & more

Rs. 299/month

Subscribe now for a 7-day free trial

More Great AIM Stories

Rohit Yadav
Rohit is a technology journalist and technophile who likes to communicate the latest trends around cutting-edge technologies in a way that is straightforward to assimilate. In a nutshell, he is deciphering technology. Email: rohit.yadav@analyticsindiamag.com

AIM Upcoming Events

Early Bird Passes expire on 3rd Feb

Conference, in-person (Bangalore)
Rising 2023 | Women in Tech Conference
16-17th Mar, 2023

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
27-28th Apr, 2023

3 Ways to Join our Community

Telegram group

Discover special offers, top stories, upcoming events, and more.

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Subscribe to our Daily newsletter

Get our daily awesome stories & videos in your inbox

All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges