R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R's excellent data visualization feature.

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book "Python for Everybody".

Sourcing talent is an ongoing and important task for global organisations from startups to international governments. As technology continues to augment business processes, employing people with relevant expertise is a serious priority. Data scientists and engineers are in high demand – but supply is low. In fact, according to Element AI, there are only 10,000 people in the world with the necessary skills to handle complex Artificial Intelligence research. What can be done to close the AI talent gap, and who is responsible for making it happen?

About this course: Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Data sets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset.

About this course: Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback.

I used to think reading was just about getting smarter. But then, I started seeing a huge difference in my life that had nothing to do with the specifics of what I was reading, and I discovered that I couldn't be more wrong. Reading isn't just about getting smarter -- it's also about "altitude" as Donald Trump calls it. And I think this benefit of reading is even more important than what you'll learn from the books you read.

Structure: Good structure and went through all the basic principles of statistics in detail. Appreciated how it did not have to go through the methodology of each method, but taught us how to appreciate it and understand the data as it was presented in the literature. I liked how John went through the examples in the literature so it was good to see how it was utilised in practice. I wish there was a separate course to teach us how to use these methods with sample data, perhaps a taster of this would have been good to include? but I do understand that would be challenging for some. I think some in-video questions would have been good to check-up on the progress of learning.

For some us, AI is kind of an iffy proposition. To many, it is nebulous enough to seem like it might replace us or our jobs. And the harbingers of this sea change aren't exactly affirming: every other week in the news, self-driving smart cars keep crashing, with injuries and sometimes fatalities. AI generally doesn't seem to be that well-received in mass media, either, like in movies like Minority Report or TV shows like Westworld. Because of all this, the public perception of AI might be on the negative side. A good way to overcome uneasiness, anxiety or fear is simply be learning more about whatever seems to be the issue or problem.

Does your passion lie in Data Science / Analytics? Currently, data science and machine learning are changing the world. Here's your chance to live your passion. To become better at what you do, you no longer need to stick around your laptop for long hours. Take a break and switch to faster way of learning. Did you know you can run Python in your phone?