The Open Source Society University offers a complete education in computer science using online materials.
According to their GitHub page, the curriculum is suited for people with the discipline, will, and good habits to obtain this education largely on their own, but who’d still like support from a worldwide community of fellow learners.
Intro CS: for students to try out CS and see if it’s right for them
Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student’s interests
Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
Pro CS: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job
It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies. Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible, each class’s prerequisites are specified so that you can design a logical but non-linear progression based on the class schedules and your own life plans.
Now, as a gift to the European Union, Finland has opened up the course for the rest of Europe and the world to enjoy.
The course is even being translated into several local languages. At the time of writing, five Northern European languages are already supported, but additional translation efforts are still in progress.
Elements of AI takes six weeks and functions as a crash course and beginner introduction to the field of AI:
Google has announced to provide open access to its artificial intelligence and machine learning courses. On their overview page, you will find many educational resources from machine learning experts at Google. They announced to share AI and machine learning lessons, tutorials and hands-on exercises for people at all experience levels. Simply filter through the resources and start learning, building and problem-solving.
For instance, up your game straight away with this 15-hour Machine Learning crash course. Zuri Kemp – who leads Google’s machine learning education program – said that over 18,000 Googlers have already enrolled in the course. Designed by the engineering education team, the courses explores loss functions and gradient descent and teached you to build your own neural network in Tensorflow.
Public Service Motivation is a theorized attribute of government and non-governmental organization employment that explains why individuals have a desire to serve the public and link their personal actions with the overall public interest (Wikipedia, 2017). Academics are often said to score highly on this public service motivation and I can’t but admire those that share their knowledge freely with the public.
Colin Gillespie and Robin Lovelace are perfect examples of altruistic contributors to society. Their latest book – Efficient R Programming – is a definite recommendation for anybody who wants to power-up their R code, beginner or more advanced programmer. On top of this, the authors provide the digital version free-of-charge!
A time series can be considered an ordered sequence of values of a variable at equally spaced time intervals. To model such data, one can use time series analysis (TSA). TSA accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend, or seasonal variation) that should be accounted for.
TSA has several purposes:
Descriptive: Identify patterns in correlated data, such as trends and seasonal variations.
Explanation: These patterns may help in obtaining an understanding of the underlying forces and structure that produced the data.
Forecasting: In modelling the data, one may obtain accurate predictions of future (short-term) trends.
Intervention analysis: One can examine how (single) events have influenced the time series.
Quality control: Deviations on the time series may indicate problems in the process reflected by the data.
Analytics Vidhya hosts a more comprehensive tutorial on TSA in R. They elaborate on the concepts of a random walk and stationarity, and compare autoregressive and moving average models. They also provide some insight into the metrics one can use to assess TS models. This web-tutorial runs through TSA in R as well, showing how to perform seasonal adjustments on the data. Although the datasets they use have limited practical value (for businesses), the stepwise introduction of the different models and their modelling steps may come in handy for beginners. Finally, business-science.io has three amazing posts on how to implement time series in R following the tidyverse principles using the tidyquant package (Part 1; Part 2; Part 3; Part 4).