2019: A leading path for becoming a data scientist

By ridhigrg |Email | Jan 2, 2019 | 24060 Views

Wanted to build yourself? Yes. These paths help you to eliminate that workload which you have to do otherwise.  There are a large number of resources which are overwhelming when you enter into data science. And for that, you have the learning path which gives you a success in the community. We have a complete path of learning to become a data scientist in 2019. This path is divided into certain categories that are:

Getting started
Begin your data science journey and this is the biggest step where understanding the main concept to what data science is. This is the step where programming language and tools should be chosen and as per our recommendation python is the best. Through this, you can enable the code easily through everything which you will learn in the upcoming course times. 

Basic maths and statistics learning
There are many core concepts which are must for a data scientist to be aware of. That is mathematics and statistics. From learning this tool you can easily perform the calculations which will help you to generate good results. And having a grab over the statistical method which is descriptive and inferential stats is must if you want to become a data scientist. In this year learning path, the main focus is on these two fields. 

Concepts of machine learning and applying them
Once you are done with the basic ideas of machine learning and it get started then you will actually start learning the machine learning concepts, Machine learning is not just the theoretical concept but learning by doing is must and hence some very awesome project are provided where you can experience the life of a data scientist, like what he does.

Other applications of machine learning
Having a good grasp over these basic techniques benefit you and there are many topics which are advanced like ensemble learning, random forest and methods of time series. Machine learning is not just about an algorithm, here you need to know the tricks where you can improve the model. For this, only the validation strategies and the featuring of engineering will play an effective role. Industry applications are also focused and have a project recommendation in the learning path.

Introduction to deep earning
These concepts of machine learning are now very clear to you and the nest is! Of course deep learning, in todayĆ¢??s time for data scientist it is becoming an essential part. And now data scientist path will lean towards understanding the neural networks. 

Deep learning architectures which are like RNN, CNN
You should really follow that up with a deep dive into the frameworks of advanced neural networks which are recurrent and convolution neural networks. These concepts are heavy and it might take a few weeks to go through these concepts from scratch.

Natural language processing
Without going through NLP, data scientist path of learning is not fully complete. Basics should be focused more and more which includes the text preprocessing and classification of the text. Exploring the workings of deep learning in NLP is really adventurous and the one who is willing to be in this field must go through it. 
These are the steps which will help you to follow the learning path and get enhanced with them. And hopefully, through this path you will get into the role of data scientist before the end of the year.

Source: HOB