background preloader

Learn R, Python & Data Science Online

Related:  djedi66

جلب الحبيب | بالسحر وبدونة | علوي وسفلي Python Interview Questions and Answers for Coding |Python Data Science Interview Questions for Advance Developers 1. Compare Java & Python 2. What is Python? Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable. 3. PYTHONPATH − It has a role similar to PATH. 4. PYTHONSTARTUP − It contains the path of an initialization file containing Python source code. PYTHONCASEOK − It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. PYTHONHOME − It is an alternative module search path. 5. Python has five standard data types − NumbersStringListTupleDictionary 6. Become Python Certified in 24 hrs. 7. Python memory is managed by Python private heap space. 8. Inheritance allows One class to gain all the members(say attributes and methods) of another class. They are different types of inheritance supported by Python: 9. 10. The built-in datatypes in Python is called dictionary. Let’s take an example: The following example contains some keys. print dict[Country] 11. 12. list.sort() print (list)

Home Page R: The R Project for Statistical Computing Some datasets for teaching data science · Simply Statistics In this post I describe the dslabs package, which contains some datasets that I use in my data science courses. A much discussed topic in stats education is that computing should play a more prominent role in the curriculum. I strongly agree, but I think the main improvement will come from bringing applications to the forefront and mimicking, as best as possible, the challenges applied statisticians face in real life. I therefore try to avoid using widely used toy examples, such as the mtcars dataset, when I teach data science. However, my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy. After a few years of teaching I have collected a few datasets that I think fit this criteria. install.packages("dslabs") Below I show some example of how you can use these datasets. library("dslabs") data(package="dslabs") Note that the package also includes some of the scripts used to wrangle the data from their original source:

Supports des cours informatique gratuit en PDF Отбираем валидные мобильные номера друзей VK на Python В процессе изучения Python стало интересно попробовать его в связке с API VK. В ВК есть телефонная книга, она показывает телефоны ваших друзей в более-менее удобном формате. Так как далеко не всегда люди охотно оставляют там полые(валидные) номера своих телефонов, мне показалась интересной идея написать скрипт, который отбирал бы только валидные номера моб.телефонов и выдавал бы их отдельной таблицей. Наша телефонная книга будет генерировать csv-файл, который затем можно будет открыть, например, в excel. Для использования API VK на Python я нагуглил отличную, на мой взгляд, библиотеку с оригинальный названием vk. Итак, импортируем необходимые модули: import vk from time import sleep from re import sub, findall from getpass import getpass from csv import writer, QUOTE_ALL Создадим класс User с необходимыми методами: Долго не мог решить проблему, и в гугле как-то не попадалось на глаза, как взять id текущего пользователя. def norm_mob(str): if len(str) ! Сохраняем полученный результат.

Profiling and benchmarking “Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered.”— Donald Knuth. Optimising code to make it run faster is an iterative process: Find the biggest bottleneck (the slowest part of your code).Try to eliminate it (you may not succeed but that’s ok).Repeat until your code is “fast enough.” This sounds easy, but it’s not. Even experienced programmers have a hard time identifying bottlenecks in their code. It’s easy to get caught up in trying to remove all bottlenecks. Outline Prerequisites In this chapter we’ll be using the lineprof package to understand the performance of R code. devtools::install_github("hadley/lineprof") Measuring performance To understand performance, you use a profiler. If we profiled the execution of f(), stopping the execution of code every 0.1 s, we’d see a profile like below.

Supports de cours -- Data Mining, Data Science et Big Data Analytics Cette page recense les supports utilisés pour mes enseignements de Machine Learning, Data Mining et de Data Science au sein du Département Informatique et Statistique (DIS) de l'Université Lyon 2, principalement en Master 2 Statistique et Informatique pour la Science des donnéEs (SISE), formation en data science, dans le cadre du traitement statistique des données et de la valorisation des big data. Je suis très attentif à la synergie forte entre l'informatique et les statistiques dans ce diplôme, ce sont là les piliers essentiels du métier de data scientist. Attention, pour la majorité, il s'agit de « slides » imprimés en PDF, donc très peu formalisés, ils mettent avant tout l'accent sur le fil directeur du domaine étudié et recensent les points importants. Cette page est bien entendu ouverte à tous les statisticiens, data miner et data scientist, étudiants ou pas, de l'Université Lyon 2 ou d'ailleurs. Nous vous remercions par avance. Ricco Rakotomalala – Université Lyon 2

Introduction to Data Science

Related:  PythonCodeData Sciencegigi65LifehacksPythoncurosos online