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Samouczki data science

Rozwijaj karierę w danych dzięki naszym samouczkom data science. Przeprowadzamy przez wymagające funkcje i modele krok po kroku.

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AI for BusinessAnaliza danychBig DataData GovernanceDeep LearningInżynieria danychMLOpsUczenie maszynoweWizualizacja danych
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Python

PEP-8 Tutorial: Code Standards in Python

With this beginner tutorial, you'll start to explore PEP-8, Python's style guide, so that you can start formatting your code correctly to maximize its readability!

Chitrank Dixit

Python

Python String Tutorial

In this tutorial, you'll learn all about Python Strings: slicing and striding, manipulating and formatting them with the Formatter class, f-strings, templates and more!

Sejal Jaiswal

Python

Time Series Analysis Tutorial with Python

Get Google Trends data of keywords such as 'diet' and 'gym' and see how they vary over time while learning about trends and seasonality in time series data.

Hugo Bowne-Anderson

Python

Kaggle Tutorial: EDA & Machine Learning

In this Kaggle tutorial, you'll learn how to approach and build supervised learning models with the help of exploratory data analysis (EDA) on the Titanic data.

Hugo Bowne-Anderson

R

Five Tips to Improve your R Code

Five useful tips that you can use to effectively improve your R code, from using seq() to create sequences to ditching which() and much more!

Simon Jackson

AWS

AWS EC2 Tutorial For Beginners

Discover why you should use Amazon Web Services Elastic Compute Cloud (EC2) and how you can set up a basic data science environment on a Windows instance.

DataCamp Team

R

R Formula Tutorial

Discover the R formula and how you can use it in modeling- and graphical functions of well-known packages such as stats, and ggplot2.

Karlijn Willems

R

Pipes in R Tutorial For Beginners

Learn more about the famous pipe operator %>% and other pipes in R, why and how you should use them and what alternatives you can consider!

Karlijn Willems

Python

Web Scraping & NLP in Python

Learn to scrape novels from the web and plot word frequency distributions; You will gain experience with Python packages requests, BeautifulSoup and nltk.

Hugo Bowne-Anderson