As a result, it is crucial to understand the importance of machine learning and the reasons why data scientists must master it.
What is Machine Learning?
Currently, we engage with machine learning applications throughout our day through various interactions done on the internet. Regardless of you searching for a new toaster on a shopping or retail network, looking for blogs or even searching for acquaintances on social media, you are interacting with machine learning applications without even realizing it.
Machine learning technology enables these platforms to offer relevant recommendations and suggestions to the user.
The companies that run the platforms can monitor your day-to-day activity on their platform, your search behavior and preferences when it comes to shopping by leveraging machine learning technology.
Machine learning is also one of the core elements of artificial intelligence.
Who is a Data Scientist?
Before we stress the importance of machine learning to data scientists, let’s understand the role of data scientists and how to become one.
Data scientists can derive meaningful information from vast quantities of data. They are capable of detecting patterns and can help develop tools such as AI-powered chatbots, CRMs etc. to automate various processes within a company.
Using their knowledge in extensive machine learning techniques and contemporary technologies such as Python, SAS, R and SQL/NoSQL databases, Data scientists carry out in-depth statistical analysis on data.
The role of a data scientist might seem similar to a data analyst, but there are a few succinct differences.
Difference between a Data Scientist and a Data Analyst
- A data scientist predicts the future by analyzing past patterns. Whereas, a data analyst derives meaningful insights from data.
- A data scientist’s work deals with ‘estimation’ or prediction of unknown facts, while a data analyst investigates already known facts.
- A data analyst’s role leans more towards businesses. A data scientist’s role is more integral to developing innovations and technological advancements.
Why is Machine Learning So Important for a Data Scientist?
In the not too distant future, process automation will take over or manage most of the manufacturing work handled by humans. In order for devices to match human capabilities, they will be required to be highly intelligent and machine learning is at the core of AI.
This requires data scientists to have a thorough understanding of machine learning to formulate quality estimations and predictions. This will enable machines to make the right decisions and smarter actions in real-time without any human intervention.
Machine learning is revolutionizing the way data mining and data interpretation work. Its applications have replaced traditional statistical techniques to create highly accurate automatic sets of generic methods.
As a result, this makes it crucial for data scientists to possess expert skills and knowledge about machine learning.
4 Must-Have Skills Required to Become a Machine Learning Expert
A data scientist must possess the following skills to become an expert at machine learning.
- Extensive knowledge and expertise regarding computer fundamentals. For instance, computer organization, application software and, systems architecture and layers.
- In-depth knowledge of probability is crucial as a data scientist’s work often deals with creating estimations. Analyzing statistics is also another key aspect they need to focus on.
- Data modeling for analyzing different data objects and the manner in which they interact with one another.
- Being proficient in programming and having considerable knowledge about programming languages such as NoSQL, besides the traditional SQL and Oracle.
Data can be considered to be the new oil.
It is estimated that worldwide demand for data scientists will increase significantly in the next few years. The industries of finance, insurance and IT will form the bulk of the data science and analytics job demand.
A few years from now, we can expect machine learning to be realized and developed to be the best solution for analyzing vast quantities of data. As a result, data scientists should focus on gaining an in-depth knowledge of machine learning and artificial intelligence in order to boost their level of productivity and output.