Samouczki data science

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

Inne tematy:

AI for BusinessAnaliza danychBig DataData GovernanceDeep LearningInżynieria danychMLOpsUczenie maszynoweWizualizacja danych

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Python

Python Exploratory Data Analysis Tutorial

Learn the basics of Exploratory Data Analysis (EDA) in Python with Pandas, Matplotlib and NumPy, such as sampling, feature engineering, correlation, etc.

Karlijn Willems

15 marca 2017

Python

Python Dictionary Tutorial

In this Python tutorial, you'll learn how to create a dictionary, load data in it, filter, get and sort the values, and perform other dictionary operations.

DataCamp Team

16 lutego 2017

Algorithmic Trading in R Tutorial

In this R tutorial, you'll do web scraping, hit a finance API and use an htmlwidget to make an interactive time series chart to perform a simple algorithmic trading strategy

Ted Kwartler

9 lutego 2017

Python

Scipy Tutorial: Vectors and Arrays (Linear Algebra)

A SciPy tutorial in which you'll learn the basics of linear algebra that you need for machine learning in Python, with a focus how to with NumPy.

Karlijn Willems

8 lutego 2017

Web Scraping and Parsing Data in R | Exploring H-1b Data Pt. 1

Learn how to scrape data from the web, preprocess it and perform a basic exploratory data analysis with R

Ted Kwartler

12 stycznia 2017

15 Easy Solutions To Your Data Frame Problems In R

Discover how to create a data frame in R, change column and row names, access values, attach data frames, apply functions and much more.

Karlijn Willems

10 stycznia 2017

Data Science

Learn Data Science - Infographic

Start your data science journey now and follow these 8 steps to learn data science.

Karlijn Willems

18 października 2016

Python

Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN

This article will explain the importance of preprocessing in the machine learning pipeline by examining how centering and scaling can improve model performance.