Our relentless pursuit of improving our student’s learning experience has resulted in developing our learning applications. The internally built learning applications are backed by battle-tested pedagogical methods to support and accelerate our student’s learning ability.
PEDAGOGYA performance management tool for education professionals. Pedagogy delivers key performance indicators and assembly-wide analytics to its employees and training roster. It is open-sourced on GitHub as part of our company’s effort to (1) evangelize evidence-based teaching methods and to (2) instill a growth-oriented mindset in every educator that works with us. Feature sets include:
SectorsSectors is referred to as a financial data layer built from the ground up to bring data analysts and developers the most comprehensive financial data suite and tooling in their preferred work environment (Python, R, SQL, PowerBI, Tableau, Google Sheets etc.). The partnership with Sectors allow Algoritma to deeply embed into our curriculum key topics:
Corgi (courses on GitHub) is an automation tool for quiz authoring and publishing. It enables programming-course educators to create MCQ (multiple choice question) and assign scores to each question, all from the comfort of a Markdown file (.md). You can set passing grades on each course, maximum attempts, and see all students who have earned a badge on the course in one quick view.
Corgi (courses on GitHub) offers a free-for-all buffet-style learning environment by aggregating programming and data science courses from GitHub. You can participate in any course freely, and your attempts will be rewarded with badges. You can bookmark courses for each access, look at others who have completed the course, and you can freely participate in any of the programming courses you find.
ELANGElang is an acronym that combines the phrases Embedding (E) and Language (Lang) Models. Its goal is to help NLP (natural language processing) researchers, Word2Vec practitioners, and data scientists become more productive in training language models. The package provides a collection of utility functions and tools that interface with gensim, matplotlib and scikit-learn, it also curated negative lists for Bahasa Indonesia (kata kasar / vulgar words, stopwords, etc) and useful preprocessing functions. It abstracts away the mundane task so you can train your model faster and obtain visual feedback on your model more quickly.
PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
WhatsApp: 0816-692-471
Email: community@algorit.ma