Data Science Roadmap 2nd Part

Level-3

Already we have learned Coding, Math, ML Algorithms, ML Techniques, and Tools, and have completed some projects using them. In level-3, we will learn something new that has been used for the last 4-5 years. However, the use of machine learning concepts is not long-standing; it has been implemented in the industry for the last 10-15 years.

MLOps Principles:

An ML project has to follow a structure to build and deploy end-to-end; this method is called MLOps. MLOps is basically a set of principles and tools that help build and deploy ML projects. That’s why MLOps is so important. MLOps is based on coding principles and tools. MLOps basically requires mastering two things.

First, a set of principles to understand how to build an ML project end-to-end:

Version control

Experimentation tracking

CICD (Continuous Integration and Continuous Deployment)

Deployment

Monitoring These principles should be learned.

MLOPS Tools:

, learn some related tools like:

GitHub
TVC
ML Flow
ZenML
Cloud platforms like AWS

Project:

Once MLOps is done, some more projects should be created where you apply your existing knowledge of data science and use MLOps. The project should be designed in such a way that it can be used in any industry. After clearing level-3, you will be in the top 5%.

Level-4

Deep Learning:

In level-4, we will discuss deep learning. So far, you have learned data science, created projects by implementing ML algorithms, learned MLOps, and created projects using it. After mastering this knowledge, there is no reason not to learn deep learning. Now is the time to jump into the deep learning sector. This is a sector where, after learning, you will move to the top 1%.

Currently, the fastest-growing sector is deep learning. The world’s biggest tech giants are investing in this sector, and day by day, a huge demand for manpower is being created in the deep learning sector. In deep learning, you need to focus on three to four topics:

ANN (Artificial Neural Network)

CNN (Convolutional Neural Network)

RNN (Recurrent Neural Network) – Applied to text data

Techniques

Dropout

Regularization

Optimizers

Gradient Descent

Deep Learning Project:

Deep learning is a sector where it is not possible to work with only theoretical knowledge; real-world problems have to be solved practically. For this, 2-3 projects should be created using deep learning knowledge.

NLP/CV:

After completing a deep learning project, you have two options to choose from. The first one is NLP (Natural Language Processing), where you apply deep learning techniques to text data. The second path is CV (Computer Vision), where you apply deep learning techniques to image and video data. First, you have to select one of these two topics based on your interest and industry demand. Our recommendation would be to focus on NLP for future industry demand.