The role of a data scientist is becoming very popular these days. The “Harvard Business Review” mentions it as the “Sexiest job of the 21st century”. Usually, data scientists are avowed as big data wranglers. They gather a large number of clumsy data points and use their skills in statistics, math and programming to clean and organize them.

The roles and responsibilities of a data scientist are diverse and the skills required vary considerably. Here, you can have a view of various machine learning courses available for Data science enthusiasts.

Machine Learning Courses:

Machine Learning Crash Course

This course is by Google, which presents you with a basic course on Machine Learning for aspiring practitioners. Here, you will have access to video lectures, real-time case studies and hands-on practice sessions. This course has a benefit of learning from researchers of google.

Link to the website: https://developers.google.com/machine-learning/crash-course/

Coursera (University based course structure)

Coursera enables you to learn this course from a university-based curriculum taught by professors of the Stanford University. This course teaches you about: an introduction to machine learning, statistical patterns and data mining, there are many various topics included.

Link to the website: https://www.coursera.org/learn/machine-learning

Udemy

This online platform provides video-based courses on various courses, there are many courses related to machine learning. Some of them are, Machine learning A-Z, Machine learning, Data science and deep learning with python and complete machine learning course with python, etc.

Link to the website: https://www.udemy.com/topic/machine-learning/

Foreseeing the future of Data Scientists:

Over the next few years, data scientists will enhance their ability to implement all sorts of data in real-time.

This will boost the need for making more computations and intricate predictions at a scale which will give rise to the emergence of new paradigms of data science due to the need of future applications.

A lot of data will be utilized to drive important business decisions and will enable new innovations like Deep Learning that supports decision making and accurate predictions.