Why a Data Scientist Should Learn Machine Learning Skills

 

We’ve all been hearing the buzz around data science and why it is the top skill to pick up currently. It is not an exaggeration when we say it is one of the most sought-after skills in the current market. Data Science enables businesses leaders to understand what their data is saying and how to implement changes that are in line with the data predictions. Almost every industry and sector right now are using analytics and data science to examine and interpret the data which until earlier was not tapped into.

Data Science and Machine Learning: The Connection

But like any other field, data science too is constantly evolving. Newer tools and techniques are being implemented to get far more specific insights. This evolution has led to machine learning being the next big thing for data scientists. Machine learning is the field of enabling machines/computers to learn without the need for explicit programming to perform a task or function. Machine learning falls under the umbrella of data science techniques. Machine learning currently find application in the areas of autonomous systems, recommender systems, credit scoring, virtual assistants, demand detection and prediction, fraud detection, stock trading etc.

Data science helps machines in understanding the data patterns and infer insights from it, which are then used to perform further actions based on these insights. Data science enables machine learning in the sense that it allows it to find the correct and meaningful information from huge data sets far more efficiently. It essentially helps machines in the co-relation of data to make sense out of it.

Staying Future Proof with Machine Learning

With data science and machine learning finding convergence, it has become a necessity for data scientists to pick up machine learning skills as the next step in their growth and scope. It wouldn’t be an exaggeration to say that we will increasingly look at systems being automated and machine learning enabling these systems to work without much human intervention. From the most trivial to the most complex of tasks, we are looking to automate everything. Self-driving cars, smart homes and smart cities, ad placements, facial and speech recognition, natural language processing are some of the applications of machine learning that we are already using, though we might not think about it.

Like every skill has a hype, every skill eventually becomes obsolete too. For anyone to stay relevant, upskilling must happen. Technology is growing at an unprecedented pace, with newer and more innovative approaches emerging every few years. The roles we have right now have to evolve, and machine learning is the way to go for data scientists.

 

 

 
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