Tag: education

A free, self-taught education in Computer Science!

A free, self-taught education in Computer Science!

The Open Source Society University offers a complete education in computer science using online materials.

They offer a proper introduction to the fundamental concepts for all computing disciplines. Evyerthing form algorithms, logic, and machine learning, up to databases, full stack web development, and graphics is covered. Moreover, you will acquire skills in a variety of languages, including Python, Java, C, C++, Scala, JavaScript, and many more.

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.

Curriculum

  • 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.

Links to the contents

Links to the curriculum (v8.0.0)

Finland’s free online AI crash course

Finland’s free online AI crash course

Finland developed a crash course on AI to educate its citizens. The course was arguably a great local success, with over 50 thousand Fins taking the course (1% of the population).

Now, as a gift to the European Union, Finland has opened up the course for the rest of Europe and the world to enjoy.

All pictures are screenshots taken from the website

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:

Machine Learning and AI courses at Google

Machine Learning and AI courses at Google

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.

 

Online Resource: Efficient R Programming

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!

Time Series Analysis 101

Time Series Analysis 101

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:

  1. Descriptive: Identify patterns in correlated data, such as trends and seasonal variations.
  2. Explanation: These patterns may help in obtaining an understanding of the underlying forces and structure that produced the data.
  3. Forecasting: In modelling the data, one may obtain accurate predictions of future (short-term) trends.
  4. Intervention analysis: One can examine how (single) events have influenced the time series.
  5. Quality control: Deviations on the time series may indicate problems in the process reflected by the data.

TSA has many applications, including:

  • Economic Forecasting
  • Sales Forecasting
  • Budgetary Analysis
  • Stock Market Analysis
  • Yield Projections
  • Process and Quality Control
  • Inventory Studies
  • Workload Projections
  • Utility Studies
  • Census Analysis
  • Strategic Workforce Planning

AlgoBeans has a nice tutorial on implementing a simple TS model in Python. They explain and demonstrate how to deconstruct a time series into daily, weekly, monthly, and yearly trends, how to create a forecasting model, and how to validate such a model.

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).

 

TACIT: An open-source Text Analysis, Crawling, and Interpretation Tool

Click here for the original PDF: TACIT 2017


The first programs for (scientific) text mining are already over 50 years old. More recent efforts, such as the Linguistic Inquiry Word Count (LIWC; Tausczik & Pennebaker, 2010), have greatly improved our text analytical capabilities. Moreover, several single-purpose programs have been developed, which also consider syntactic text structures (e.g., Syntactic Complexity Analyzer [Lu, 2010], TAALES [Kyle & Crossley, 2015]).However, the widespread use of many of these programs has been hampered by two major barriers.

First, considerable technical expertise is required, which obstructs researchers without statistical backgrounds. For example, packages such as tm in R (Meyer et al., 2015) have been developed to conduct natural-language processing, but the steep learning curve forms a challenge. Additionally, the constant increase of computational processing power and the proliferation of new algorithms makes it difficult for researchers to maintain working knowledge of state-of-the-art methods.

Alternatively, most of the existing user-friendly NLP programs (and packages), such as RapidMiner (Akthar & Hahne, 2012), SAS Text Miner (Abell, 2014), or SPSS Modeler (IBM Corp., 2011), charge either a large software fee up front or a subscription fee. The cost of these programs can be prohibitively expensive for junior researchers and researchers looking to integrate new techniques into their research toolbox.

In the attached article, TACIT is introduced: Text Analysis, Crawling and Investigation Tool. TACIT is an open-source architecture that establishes a pipeline between the various stages of text-based research by integrating tools for text mining, data cleaning, and analysis under a single user-friendly architecture. In addition to being prepackaged with a range of easily applied, cutting-edge methods, TACIT’s design also allows other researchers to write their own plugins.

The authors’ hope is that TACIT can facilitate the integration and use of advancements in computational linguistics in psychological research, and by doing so can help researchers make use of the ever-growing documents of our social discourse in ways that have previously not been possible.

Statistics Visually Explained

Statistical literacy is essential to our data-driven society. Analytics has been and continues to be a game changer in many business fields, among other Human Resources. Yet, for all the increased importance and demand for statistical competence, the pedagogical approaches in statistics have barely changed.

Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University’s Royce Fellowship Program. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations.

Using JavaScript, the researchers have made statistics both intuitive and beautiful at the same time.

seeing theory