1. Инструменты визуализации данных

    Инструментов для обработки данных сейчас полным полно, но особое место среди них занимают инструменты/сервисы визуализации — ведь грамотное, красивое, понятное представление данных играет важнейшую роль в принятии решений на основе проведённого анализа. Предлагаем вам список 30 простых (часто бесплатных) инструмента визуализации данных. Альбом можно посмотреть здесь. 1. iCharts iCharts is...
  2. Библиотеки визуализации в Питоне

    А  вот и 6 популярных библиотек для визуализации в Питоне с описанием на английском языке (по ссылкам есть примеры использования и красивые графики). ggplot ggplot is a plotting system for Python based on R’s ggplot2 and the Grammar of Graphics. It is built for making profressional looking, plots quickly with...
  3. A/B Testing Mastery

    A/B testing – for all the content out there about testing, huge amounts of people still mess it up. From testing the wrong things to running the tests incorrectly, there are lots of ways to get it wrong. Here’s what’s covered in the guide: What is A/B testing and How...
  4. Questions & Answers: Process & Miscellaneous

    1. How to optimize algorithms? (parallel processing and/or faster algorithms). Provide examples for both “Premature optimization is the root of all evil”; Donald Knuth Parallel processing: for instance in R with a single machine. — doParallel and foreach package — doParallel: parallel backend, will select n-cores of the machine —...
  5. Questions & Answers: Statistics

    1. How do you assess the statistical significance of an insight? Is this insight just observed by chance or is it a real insight? Statistical significance can be accessed using hypothesis testing: — Stating a null hypothesis which is usually the opposite of what we wish to test (classifiers A...
  6. Questions & Answers: Machine Learning & Math

    Machine Learning & Mathematics 1. What is cross-validation? How to do it right? It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately...
  7. Four Types of Data Scientists

    The information here comes from the O’Reilly paper “Analyzing the Analyzers” by Harris, Murphy, and Vaisman, 2013. There are 40 pages of good analysis here so this will be only the highest level summary. In short, they conclude there are four types of Data Scientists differentiated not so much by...
  8. What exactly is data science?

    There is a lot of talk about the power of data science and how organisations today needs data scientists to extract insight from data to meet customer needs and achieve significant competitive advantage. But, many are left wondering what exactly data science is, what the components of it are and...

Data Scientist # 1

Машинное обучение, большие данные, наука о данных, анализ данных, цифровой маркетинг, искусственный интеллект, нейронные сети, глубокое обучение, data science, data scientist, machine learning, artificial intelligence, big data, deep learning

Данные — новый актив!

Эффективно управлять можно только тем, что можно измерить.
Copyright © 2016-2021 Data Scientist. Все права защищены.