Course structure for Data Science Freshers and Beginners.

If you are looking for a decent start in data science. Then this page will provide you the course structure and resource links. Learn the basic concepts to get into the data science world.

Prologue: Knowledge sharing should be free, there are more than enough resources available on internet to learn data science for Free, The below links and resources shared might require you to sign up for courses which are free.

Machine learning course by Andrew NG:

Note: This is the best course over the internet giving intuition about machine learning and linear algebra and also covers all the areas in depth and also might require your efforts in assignment submission.

Course Link or (https://www.coursera.org/learn/machine-learning)

Details: The above course link covers all the major concepts of Machine learning and hands on with assignments.

The course is hands on with Octave/Matlab because the understanding of mathematical notations would be easier in Octave/Matlab than Python/R at this point.

Topics covered:

1. Linear Algebra: Matrices & Vectors and operations.
2. Linear Regression: Model Representation, Cost function, Gradient Descent.
3. Linear Regression Multiple variables: Gradient Descent for multiple features, features for polynomial regression.
4. Logistic Regression: Classification, Hypothesis representation, Decision boundary, Cost function and Gradient Descent, Advanced optimization, Multi-Class classification.
5. Regularization: understanding over-fitting, Cost function, Regularized linear function, Regularized logistic function.
6. Intro Neural Networks: Neural Nets intuition and model representation.

Other readings:

Google also provides a self learning guide with videos and also the course material with examples in brief.

Link: https://developers.google.com/machine-learning/crash-course/ml-intro

Google resource will be really helpful for revision and clarification.

The above 2 resources will provide a very good foundation for Machine learning and Data science.

Myths about Data science:

1. Data science don’t require programming or coding skills. most of the code is already written into modules, just need to implement using APIs and understand the algorithm.
2. Need not be a pro in stats and math. Basic math and linear algebra which is thought in school will suffice.
3. Doesn’t require high configuration machines. Most of the datasets for practice are small enough and will be easy to understand the patterns.
4. Need not be computer science graduate. Data science is not domain specific, it is across all the domains.
5. Need not spend lot of money to learn Data science. Most of the best tutorials are free and open to internet.

The above are only myths and having them is only a add-on.

Useful resources links:

If you are new to programming, I would suggest to learn basic programming language like C/Java/Python and Object oriented concepts which will make your life easier to understand the already built modules.

Google provides a free python course contains videos and material which covers most of the python concepts like:

Data types, Strings, Lists, sortings, Dicts, Files, Regex, Utils.

Link: https://developers.google.com/edu/python/

This article contains only the resources links and intro to data science, Will publish another article which will contain all the list of concepts in Machine learning for a quick overview.

Next:

1. Real time analytics and Datasets.
2. Hands on with datasets on Kaggle.
3. Deeper dive to Machine learning.
4. Neural Networks and Deep learning.