Data Science Specialization, 10 courses with verified certification and git hub code of each course.

Courses with certifications and Code uploaded to git hub

10 Courses specialization

The aim of this report is to describe the three files that will be used to build the corpus employed to model a predictive algorithm for Swiftkey

The aim of this report is to highlight a predictive algorithm (previously developed in a Milestone Report) and to provide a user interface that can be easily accessed using mobile web browsers.

The aim of this report is to highlight a predictive algorithm (previously developed in a Milestone Report) and to provide a user interface that can be easily accessed using mobile web browsers.

The aim of this report is to highlight a predictive algorithm (previously developed in a Milestone Report) and to provide a user interface that can be easily accessed using mobile web browsers.

The aim of this report is to highlight a predictive algorithm (previously developed in a Milestone Report) and to provide a user interface that can be easily accessed using mobile web browsers.

The aim of this report is to describe the three files that will be used to build the corpus employed to model a predictive algorithm for Swiftkey. The data is from a corpus called HC Corpora ([www.corpora.heliohost.org]) and it contains samples from news, blogs, and Twitter, in English, German, Russian, and Finnish.

Dos estudiantes buscan obtener apoyo para desarrollar sus emprendimientos en las áreas mas productivas de las ciencias naturales.

The Fuel-efficiency ratings of your Dream automobile

The number of car accidents in Great Britain, between 2005 and 2015

Developing Data Products, Week 3, Coursera Data Science Specialization

Number of road accidents in Great Britain from 2005 to 2015 Developing Data Products Week 3 Assignment -

The interactive plot on the next slide represents the number of road accidents in Great Britain from 2005 to 2015, grouped by severity (slight, serious, or fatal).

Data collection through self-monitoring and self-sensing combines wearable sensors (e.g.Electroencephalography, Electrocardiography) and [wearable computing](https://en.wikipedia.org/wiki/Wearable_computer) (smartwatchs, heart rate monitors, etc.) [See Quantified self](https://en.wikipedia.org/wiki/Quantified_Self). Using wereable computers like known fitness accessories is now possible to collect a large amount of data about fitness personal activity in an inexpensively way. This trackers devices are part of a “life logging” movement, and a general characteristic of the information collected is that is known how much a particular activity was did it, but rarely quantified how well they did it. In this project, the goal is to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).

Peer-graded Assignment corresponding to the Practical Machine Learning course offered by the Johns Hopkins Bloomberg School of Public Health via Coursera

prediction study design or how to minimize the problems that can be caused by in sample verses out of sample errors.

Notes from the class "In and out of sample errors", corresponding to Week 1 of the Practical Machine Learning, taught by Coursera Data Science Specialization.

Second peer-graded assignment of the Course Reproducible Research, part of the Data Science Specialization.