Most in demand skills in Data Science
One of the most significant changes driven by the pandemic is digital acceleration. In this accelerated digital world, as businesses move to insights-driven decision making, data science and analytics will play a crucial role. This is giving rise to a high demand for Data Scientists. As data science candidates look to apply for these roles, what will set them apart are multi-disciplinary skills, especially strong technical expertise, and an analytical mindset.
Here are some most in-demand skills that experienced data science professionals can strengthen and freshers can hone over time.
Build strong technical expertise
a) Big Data and SQL
Knowledge of SQL is a must to be a data scientist or an analyst. This is because SQL is specifically designed to help data science professionals gain quick access to data, communicate and work on data. It provides faster insights when used to query / manipulate a database. Proficiency in Hadoop and Spark will help in parallel execution of models as sometimes implementation of models is a computationally intensive task.
b) Machine learning
Data scientists who are proficient at machine learning algorithms will help in driving impact in the accelerated digital future. Expertise in boosting, pruning and synthetic data creation techniques will help significantly in slicing the dataset and building prediction models or forecasting of various outcomes.
Statistical techniques form the base of machine learning algorithms, deep learning, and artificial intelligence. Both aspiring as well as experienced data scientists must be adequately adept in statistical concepts such as distribution curves, hypothesis testing, confidence interval and stats to derive machine learning algorithms.
d) Cloud computing
Businesses are increasingly moving towards Cloud platforms. Analysts and data scientists skilled in cloud computing can help organisations identify and easily migrate to the right cloud computing platform and help improve processing of large data sets.
e) R Programming / Python
Possessing an in-depth knowledge of at least one of the two programming languages – R and Python – is generally expected from analysts and data scientists. R is specifically designed for data science needs. On the other hand, Python is now increasingly becoming the language of choice among data science professionals as it handles a large amount of data better compared to R, and additionally, offers better visualization options.
e) Technical education
To build a strong foundation, the most common fields of study required to be a data scientist are Mathematics and Statistics, followed by Computer Science and Engineering.
Have an Analytical Mindset
a) Interest in mathematics
Mathematics forms the core of big data, analytics, and data science. A strong command over basic math-based concepts such as Rolling Averages, Data Distributions, and more can help in analyzing data better.
b) Curious nature
A curious and scientific mindset is a prerequisite to uncover customer insights, generate hypotheses, and seek answers to business questions. Curiosity plays a crucial role in analytics, specifically in data science where a significant amount of research is needed at every step of the way.
c) Exploratory attitude
Data science needs exploration of a variety of models to select one that suits best. This then leads to exploring ways to create a solution that is accurate as well as interpretable. That is why a data scientist must have an aptitude for research and exploration.
Besides hard skills and technical expertise, having a consultative and problem-solving approach can go a long way. Furthermore, to succeed as a data scientist, one should be able to learn quickly, and identify newer ways to leverage data. This may include having the ability to quickly understand the problem they are solving, learn more about the industry and function they serve, understand macroeconomic trends, and then proceed to solve the problem.
As businesses are accelerating digital faster than ever, there is a need to address business problems faster, better and in a scalable manner. Equipped with the skills listed above, data science professionals can take their career to newer heights.
The author is Vinay Mony, Vice President at Ugam, a Merkle Company.