Англ | Data Science - Part 2

Два
Раз

Главная / Англ

Разница между предиктивной аналитикой и дата майнингом

10.11.2016

What is Predictive Analytics? According to Wikipedia, “Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.” What’s behind Predictive Analytics? Prerequisite for Predictive Analytics is the collection of...
Подробнее...
Простой способ изучить машинное обучение

23.08.2016

Ниже приводится один из подходов к изучению основ машинного обучения. My goal is to create a plan where you get to the level of average industry practitioner. Skills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the results. Recommended steps: 1. Download...
Подробнее...
Структурирование больших данных через автоматизированную индексацию

12.08.2016

You have gathered gigabytes or terabytes of unstructured text, for instance scraping the Internet, or pieces of email from your employees or users, or tweets, or millions of products that you want to categorize (only product description and product name is available — sometimes with typos). Now you want to...
Подробнее...
Типы регрессий

22.07.2016

Какой тип регрессии выбрать и использовать? Зависит ли это от контекста? Это не секрет, что существует множество видов регрессии и важно знать их недостатки и широту использования. Давайте рассмотрим некоторые из них. Linear regression: Oldest type of regression, designed 250 years ago; computations (on small data) could easily be carried...
Подробнее...
The worst predictive modeling techniques

21.07.2016

Typically, these bad techniques are still widely used. Linear regression. Relies on the normal, heteroscedasticity and other assumptions, does not capture highly non-linear, chaotic patterns. Prone to over-fitting. Parameters difficult to interpret. Very unstable when independent variables are highly correlated. Fixes: variable reduction, apply a transformation to your variables, use...
Подробнее...
Class of metrics: bumpiness

19.07.2016

All statistical textbooks focus on centrality (median, average or mean) and volatility (variance). None mention the third fundamental class of metrics: bumpiness. Here we introduce the concept of bumpiness and show how it can be used. Two different datasets can have same mean and variance, but a different bumpiness. Bumpiness...
Подробнее...
Ошибки на собеседовании data science

17.07.2016

При собеседовании на должность в сфере data science соискатели часто допускают банальные ошибки, которую снижают их шансы быть принятым на работу. Вот некоторые из них: Not doing any research on the company prior to the interview. Not understanding whether they want to hire a scientist or a developer. In many...
Подробнее...
Жизнь исследователя данных

09.07.2016

Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions...
Подробнее...
Ошибки при подготовке данных

08.07.2016

Рассмотрим топ-5 ошибок, которые совершают при подготовке данных к анализу. 1. Including ID Fields as Predictors Because lots of IDs look like continuous integers (and older IDs are typically smaller), it is possible that they may make their way into the model as a predictive variables. Be sure to exclude...
Подробнее...
6 советов как стать крутым Data Scientist

06.07.2016

In 2012, Harvard Business Review cited data scientist as the sexiest job of the 21st century. We all know the usual list: strong background in statistics and other maths, programming skills, analytical skills, and so on. But what are the things that make an analyst great? What’s a Data Scientist...
Подробнее...