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Difference between online and offline data science course

Data science courses have become very popular today. Data science is needed for all segments of the economy, making it one of the most intriguing areas to get involved in.

There are so many data science courses that you can pick from. Choosing the best is the only way to guarantee that you get the best outcome, and you get skills that will prove useful to you and the institution or enterprise you may end up working for. Earn yourself a promising career in data science by enrolling in the Data Science Training and Placement in Bangalore offered by 360DigiTMG.

Online and offline data science courses

Most people don’t know offline or batch learning by its name but may know exactly how it works. This approach is used in machine learning, and it is the standard in this field. In this case, it is all about sourcing datasets and then working on building a model that is based on the whole dataset all at once. The name batch learning comes from this concept.

So, where does the name offline learning come from? This is the other name that is used to refer to batch learning. Offline learning is the name used because it is a polar opposite of the other machine learning option that can be used that most people don’t know much about. This is online learning. It is important to know how online learning can help in data science. Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today

Online versus offline learning

Knowing what differentiates online learning from offline learning can help you distinguish the two quite easily. Simply put, the offline learning approach takes in the available data and then gives an observation at a time. In this case, the one-to-one analogy is used. Anyone who knows something about gradient descent can understand. Offline learning or batch learning is very similar to batch gradient descent.

Versions of online learning use stochastic gradient descent using different loss functions. Different algorithms are created, such as support vector machines and logistic regression. There is a kit involved in online learning. Also, check these Data Science Classes in Pune to start a career in Data Science.

Online learning is adaptable, and it is data-efficient. It is data-efficient because it is not needed anymore when data is consumed. In a technical sense, this is to say that the data that has been consumed does not have to be stored.

The reason why online learning is considered to be adaptable is that there are no assumptions made regarding data distribution. As data distribution changes because of a change in something like customer behavior, the model can adapt and stay in pace with the current trends as they come into play. If the same has to be achieved with offline learning, you need to develop a sliding data window and ensure it is retained every other time. This means that the methodology can be easily utilized to stream analytics. Become a Data Scientist with 360DigiTMG Data Science Training in Hyderabad. Get trained by the alumni from IIT, IIM, and ISB.

Bottom line

The above outlines a clear difference between online and offline learning. After this understanding, it is important to choose the method you want to employ and use and why you feel it is superior to the other.

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