4 Best Online Courses for Aspiring Data Scientist in Beginner's Stage

There are plenty of resources online to learn about Data Science and Machine Learning for the starters. And there are plenty of platforms like Coursera, EdX, Udemy which are providing the courses. It's tough to find what will work the best for you.

Read through this article to understand about these courses. I can personally recommend them as I have completed all the courses that I have mentioned.

Python for Data Science and Machine Learning BootCamp by Josh Portilla - Udemy

This is by far the best course I would recommend all the beginners to take. The course contains 25 hrs of on-demand videos and many assignments for each section. The instructors explain everything with an example, which makes the course more practical based rather than theory-based. Everything will be explained in a simple and understandable way. Another positive of this course is its cost - it's just a mere 10$.

Topics Covered in the Course:

1. Basics of Python for Beginners

2. Python Libraries - Numpy, Pandas, Matplotlib, and Seaborn

3. Machine Learning - Basic Regression and Classification Models

4. Principal Component Analysis

5. Recommender System

6. Natural Language Processing

7. Neural Networks and Deep Learning

If you are planning to start a career in Data Science, I would recommend this to take as your first course.

Machine Learning by Andrew NG - Stanford University - Coursera

I know this is not what people usually say. Most of them would recommend you to take this course first before going on to the Udemy course. But in my opinion, this course at first would be more theoretical and can take you back a little.

If you take this course, after completing the first course, then it will help you understand some doubts about how everything worked in the first course.

This course costs around 35$, but you can always apply for financial aid through Coursera. And it will contain 54 hrs of on-demand video, so it will take you at least 1 month to fully complete and understand the course.

Irrespective of the order in which you take this course, this is a must for everyone as it explains the theory behind everything you are going to work on in the simplest and understandable manner.

Topics Covered in the Course:

1. Theory behind Linear and Logistic Regression

2. Theory behind the unsupervised learning algorithms

3. Practical exercises on Matlab or Octave

4. Special Applications of Machine Learning Models

5. Improving your Model Accuracy

Note: Don't just try to complete the videos, try to understand everything the instructor teaches. This is literally going to be the base for your future learning.

IBM Data Science Certification - Courses

This specialization by Coursera contains a list of 9 courses ranging from What is Data Science to Machine Learning in Python. The specialization also includes 2 hands-on projects at which you will work on real-life data science problems.

It will give you a solid conceptual understanding of a good portion of the modern-day Python data science stack. There are 7 courses at the beginning which will help you in completing the capstone projects at the end of the certification. And also as you get the review of the works of the others - you will be learning different ways to solve the problems.

Topics Covered in the Course:

1. What is Data Science?

2. Tools for Data Science

3. Data Science Methodology

4. Python for Data Science and AI

5. Databases and SQL for Data Science

6. Data Analysis with Python

7. Data Visualization with Python

8. Machine Learning with Python

9. Applied Data Science Capstone

This is also a beginner-level data science course which will give you an idea about how to solve a real-life data science problem.

Deep Learning Specialization by deeplearning.ai - Coursera

Unlike other courses that I have mentioned in this article which are separate from each other and can be taken in any order, this course should only be taken only after completing the Machine Learning by Andrew NG course.

The specialization contains 5 courses through which you will learn how to build neural networks, convolutional neural networks, artificial neural networks, work on TensorFlow, etc.

The content of the course is well structured and good to follow for everyone with at least a bit of an understanding of matrix algebra. Some experience in writing Python code is a requirement.

Topics Covered in the Course:

1. Neural Networks and Deep Learning

2. Improving Deep Neural Networks

3. Structuring Machine Learning Projects

4. Convolutional Neural Networks

5. Sequence Models

This is an intermediate level course, but I would recommend everyone to take it. This will help in taking you a step ahead of a beginner.

Thanks for reading through the article. Hope you have fun taking those courses. If you liked it, make sure you follow me on Instagram / Pinterest to get notified for future content like this.