As the need for machine learning increases, data scientists are on the spree to become experts in the technology
The concept of machine learning and the increasing demand for data scientists has been around for a while. But the ability to apply machine learning algorithms and mathematical calculation to big data is gathering momentum only recently. As the need for machine learning increases, data scientists are on the spree to become experts in the technology. It is anticipated that machine learning will have a lot to give for data scientists in future.
Before accessing the importance of machine learning for data scientists, let us have a look at the role of data scientists and the benefits of machine learning. The introduction of smart phones and digitization has turned human’s daily life into a mission to gather data. With or without knowledge, people click on thousands of things on their device every day, creating quintillion of data. Meanwhile, the continuation of Moore’s Law – the idea that computing would dramatically increase in power and decrease in relative cost over time has made cheap computing power widely available. Data scientists emerge from the gap between these two innovations.
The role of data scientist became more pivotal in recent years. Even traditional organizations who didn’t previously invest much of their budgets in technology positions are recruiting skilful data scientists to improve decision making and analytic processes. Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data niches. They generally have a foundation in computer science, modelling, statistics, analytics, and math-coupled with a strong business sense. Especially, companies that recruit data scientists get an opportunity to improve marketing based on customer behaviour to provide personalized campaigns and advertisements, enhance innovation by analyzing through a thorough understanding of customer needs and enrichment of lives by assisting consumers in their personal life.
On the other hand, machine learning enables computers to get into self-learning mode without explicit programming. Machine learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications we hear today. Typically, machine learning algorithms use statistics to find patterns in a massive amount of data. The data encompasses a lot of things like numbers, words, images, clicks, etc. These enormous amounts of data are digitally stored and fed into machine learning algorithms. Generally put, machine learning is everywhere starting from recommendation systems on Netflix, YouTube and Spotify to social media feed on Facebook and Twitter. Businesses enable machine learning to cut costs and make lucrative solutions to many problems. Machine learning is one of the many tools in the belt of data scientists. But remarkably, machine learning is becoming more and more essential for them as new techniques emerge from it.
Data scientists and machine learning are already interlinked with each other. While data science focuses on tackling big data tasks like data preparation, cleansing, and analysis done by data scientists, machine learning comprises of machines that use a set of algorithms to train on the set of data. Henceforth, to perform better as a data scientist, the profession holders should have an open arm towards machine learning.
The advantages of machine learning for data scientists
In the fast-evolving world, innovations emerge every day. Most of the new methods that unfold from technology are replacing humans form their routine hard-laboured jobs. Machine learning is at the core of accelerator technology to adopt human capabilities and intelligence. The increase in usage of machine learning in many industries will act as a catalyst to push data science to increase relevance. Since the role of data scientists involves making humans work easy by involving data analysis and insights, they should understand machine learning for quality prediction and estimation. This can help machines to take right decisions and smarter actions in real-time with zero human intervention.
Besides, machine learning is helping data scientists on small scales by transforming how data mining and interpretation works. It has also replaced traditional statistical techniques with more accurate automatic sets of generic methods. Going forward, basic levels of machine learning will become a standard requirement for data scientists. Here are four machine learning skills that every data scientist should be aware of.
• Data scientists should have knowledge and expertise in computer fundamentals like computer organization, system architecture and layers and application software.
• Since data scientists’ work involves a lot of estimation, knowledge of probability is very important for them. Besides, they should also focus on analyzing statistics to perform better.
• Data scientists should scheme through data modelling that is used to analyze various data objects and interact with each other.
• One of the essential talents of data scientists is to have programming skills and sound knowledge of programming languages like python and R.
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