This interactive career path teaches you everything you need to become a data science practitioner, with absolutely no background required.
You'll go from knowing no programming to analyzing real-world data problems in Python and delivering valuable insights.
What is Data Science? Why Data Science?
Data Scientists are in demand in virtually every company to drive strategic decisions and power their business. It was ranked the #1 Job by Glassdoor with an average salary of over $120,000.
Trillions of gigabytes of data are being produced yearly, and the number is still growing exponentially. It is estimated that for every person, 1.7 megabytes of data will be produced every second by 2020.
It's not surprise that our society is increasingly becoming data dependent. However, data is only a raw material and extra...
We'll start by describing what machine learning is, and introduce a simple learning algorithm: linear regression + gradient descent. Using this algorithm, we'll introduce the core concepts in machine learning: model parameters, cost function, optimization method, and overfitting and regularization. This section ends with a visual review of these concepts and a tutorial on the different types of machine learning problems.
This section introduces us to databases and SQL, used for storing and managing data used in computer systems. We'll also look at map reduce, a programming model that allows us to perform parallel processing on large data sets in a distributed environment. Again, our tutorials will be interleaved with quizzes and hands-on assignments.
The first tutorial is a detailed end-to-end example of a typical data science project and workflow. The next tutorial contains a list of 10 project ideas (including datasets and suggested algorithms). It is recommended that you do at-least one end-to-end project as part of the course.
Correlation analysis can help us understand whether, and how strongly, a pair of variables are related.
In data science and machine learning, this can help us understand relationships between features/predictor variables and outcomes. It can also help us understand dependencies between different feature variables.
How strong is the correlation between mental stress and cardiac issues?
Is there a correlation between literacy rate and frequency of criminal activities?
This tutorial will help you learn the different tech...
This course presents a quick, conceptual introduction to data science — what it is and examples of data science around us. It introduces key components of data science, namely programming, data, statistics, machine learning and big data.
Note: Unlike our other courses, this one does not have interactive coding blocks since it is more conceptual in nature.