Towards Data Science highlights this talk from the Toronto Machine Learning Summit, which introduces differential privacy and its use cases, discuss the new component of the TensorFlow Privacy library, and offer real-world scenarios for how to apply the tools.

In recent years, the world has become increasingly data-driven and individuals and organizations have developed a stronger awareness and concern for the privacy of their sensitive data. It has been shown that it is impossible to disclose statistical results about a private database without revealing some information. In fact, the entire database could be recovered from a few query results. Following research on the privacy of sensitive databases, a number of big players such as Google, Apple, and Uber have turned to differential privacy to help guarantee the privacy of sensitive data.

Demand for people with knowledge and skills in artificial intelligence (AI) and machine learning (ML) hugely outstrips the supply.

Fortunately, there are a lot of courses out there to help people up skill themselves. Many are even free.

Here’s a list of ten.

These courses are aimed at a range of different audiences – maybe you want to actually learn how to design and code AI algorithms, maybe you want to bolt together the increasing range of “DIY” AI tools and services that are available, or maybe you need to manage AI projects in your organization. Whatever your needs, you are likely to find something here that will expand your horizons. has provided this full 12 hours course on data visualization with D3.js

In this data visualization course, you’ll learn how to transform data into meaningful graphical forms using D3.js and web technologies. D3 is a JavaScript library for visualizing data with HTML, SVG, and CSS. Besides teaching all about D3, this beginner’s course also covers the basics of JavaScript, HTML, CSS, and SVG so you will have all the prerequisite knowledge to create stunning data visualizations.

Course index below video.

⭐️ Course Contents & Code ⭐️
⌨️ (0:00:00) Data Visualization Course Overview
⌨️ (0:02:50) Why Visualize Data?
⌨️ (0:14:28) Inputs for Data Visualization: Data & Tasks
⌨️ (0:29:31) Intro to Javascript
⌨️ (1:57:35) Intro to HTML, CSS & SVG
⌨️ (2:31:56) Intro to D3.js – Let’s Make a Face!
⌨️ (3:15:06) Making a Bar Chart with D3.js and SVG
⌨️ (3:44:02) Customizing Axes of a Bar Chart with D3.js
⌨️ (4:10:03) Making a Scatter Plot with D3.js
⌨️ (4:34:22) Making Line and Area Charts with D3.js
⌨️ (5:04:36) The General Update Pattern of D3.js
⌨️ (6:04:30) Marks & Channels in Data Visualization
⌨️ (6:28:43) Interaction with Unidirectional Data Flow using D3.js
⌨️ (6:45:13) Making a World Map with D3
⌨️ (7:02:43) Cheap Tricks for Interaction on a D3.js World Map
⌨️ (7:25:37) Blank Canvas
⌨️ (7:30:43) Building a Tree Visualization of World Countries with D3.js
⌨️ (8:04:48) Color and Size Legends with D3.js
⌨️ (8:33:27) Choropleth Map with D3.js
⌨️ (9:05:16) Interactive Filtering on a Choropleth Map
⌨️ (9:51:00) Using Color in Visualization
⌨️ (10:07:54) Scatter Plot with Menus
⌨️ (10:54:03) Circles on a Map
⌨️ (11:35:51) Line Chart with Multiple Lines
⌨️ (11:59:34) Melting and Munging Data with JavaScript
⌨️ (12:28:29) Selecting a Year on a Line Chart

Pandas 1.0.0 is the Python’s primary library for data analysis and manipulation. Pandas 1.0.0 is now officially released! ✅Get 20% OFF the data science training!

Although at first sight this latest version is not much different for the user than the previous release starting with a 0: 0.25.3, there are plenty of enhanced features that boost performance and lay a better foundation in the long run. They represent 1.0.0 as a stable version of pandas with a strengthened API, which has also been cleaned of many prior version deprecations.Here are the most notable improvements that come with 1.0.0. 

Great Learning has posted this 11 hour full course on Data Science with Python for Beginners course.


  • Statistics vs Machine Learning – 2:15
  • Types of Statistics – 8:55
  • Types of Data – 1:50:35
  • Correlation – 2:45:50
  • Covariance – 2:52:23
  • Basics of Python – 4:24:36
  • Python Data Structures – 4:43:58
  • Flow Control Statements in Python – 4:55:58
  • Numpy – 5:32:48
  • Pandas – 5:51:30
  • Matplolib – 6:14:28
  • Linear Regression – 6:38:14
  • Logistic Regression – 9:54:34