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Andrew Ritz
Katowice, Poland
I develop mobile and python web applications for business and education. I have also developed a LISP interpreter which runs on the Parallella 16/64 multi core computer: https://github.com/drewvid/parallella-lisp In addition to my business activities, I also teach communication skills at all levels using the android applications I have developed to help me do so: http://drewvid.pythonanywhere.com/features.html
CodersRank Score
What is this?
This represents your current experience. It calculates by analyzing your connected repositories. By measuring your skills by your code, we are creating the ranking, so you can know how good are you comparing to another developers and what you have to improve to be better
Information on how to increase score and ranking details you can find in this blog post.
16.9
CodersRank Rank
Top 8%
Highest experience points: 0 points,
0 activities in the last year
Python
8.2
exp.
Shell
1.2
exp.
List your work history, including any contracts or internships
E NET PRODUCTION
Jan 2012 - Jul 2017 (5 years 6 months)
English Language Training, Android Application Development, Python & C Programming, Web development
English Language Instruction and android app development
Langtech
Jun 2009 - Aug 2021 (12 years 2 months)
Owner
Telekomunikacja Polska
Jan 2003 - Jan 2005 (2 years)
Specialist
At TP I worked on assessing the user interfaces to their CRM software and company procedures associated with new services.
Add some compelling projects here to demonstrate your experience
ATI Tests
Jan 2017 - Present
Langtech
This online application consists of two parts. The first part, hosts Theravada texts taken from the ATI website which can be searched with the extra tools provided. The second part ecourages you to further your knowledge of the English language by creating open cloze tests from the very same texts.
Parallella LISP
Jul 2015 - Present
A small implementation of LISP for the Parallella-16 board with basic, mark and sweep, garbage collection
Sentiment and Semantics
Jan 2015 - Present
Sentiment and Semantics
The Sentiment and Semantics online app, allows you to view tweets containing the search phrase you enter when presented with a popup after pressing one of the update buttons (50 or 150). The tweets you view are classified according to their sentiment and can be analyzed with respect to their semantic content. Duplicates have been removed along with possible spam.
Sentiment
During the initial classification phase, the sentiment of each tweet is estimated by looking for the presence of words with known sentiment within each tweet. A score is then generated which allows a tweet to be classified as either vpos, pos, neutral, neg and vneg. Classified tweets are then passed through a learning algorithm which estimates the sentiment of all words occurring in each tweet independently of the words used during the initial classification phase. The POS, NEG and NEUT tabs contain the classified tweets. The LANG tab contains the words classified by the learning algorithm.
Semantic Content
Many tweets contain references to articles which can be analyzed further. By clicking on a link, the corresponding article will be opened in your browser and the semantic analysis displayed in the "SUM" tab.
The semantic analysis contains the following information:
Topics
Entities
Keywords
Tags
Categories
The Sentiment and Semantics online app, allows you to view tweets containing the search phrase you enter when presented with a popup after pressing one of the update buttons (50 or 150). The tweets you view are classified according to their sentiment and can be analyzed with respect to their semantic content. Duplicates have been removed along with possible spam.
Sentiment
During the initial classification phase, the sentiment of each tweet is estimated by looking for the presence of words with known sentiment within each tweet. A score is then generated which allows a tweet to be classified as either vpos, pos, neutral, neg and vneg. Classified tweets are then passed through a learning algorithm which estimates the sentiment of all words occurring in each tweet independently of the words used during the initial classification phase. The POS, NEG and NEUT tabs contain the classified tweets. The LANG tab contains the words classified by the learning algorithm.
Semantic Content
Many tweets contain references to articles which can be analyzed further. By clicking on a link, the corresponding article will be opened in your browser and the semantic analysis displayed in the "SUM" tab.
The semantic analysis contains the following information:
Topics
Entities
Keywords
Tags
Categories
This section lets you add any degrees or diplomas you have earned.
University of Surrey
MSc, Signal Processing and Machine Intelligence
Jan 1991 - Jan 1992
Birkbeck College, University of London
MSc, Ergonomics
Jan 1982 - Jan 1983
University of Sussex
BSc Hons, Biology
Jan 1979 - Jan 1982
Scrum Master (DSDM)
Sep 2016
Data Mining