Displaying 1-10 of 10 results
Clean data: tips, tricks and techniques
- Website
- Udemy
Data cleaning is the hardest part of big data and machine learning. This course will equip you with all the skills you need to clean your data in Python, using tried and tested techniques. You will find a plethora of tips and tricks that will help you get the job done in a smart, easy and efficient way.
Cleaning data in Python
- Website
- DataCamp
A vital component of data science involves acquiring raw data and getting it into a format ready for analysis. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose problems, to dealing with missing values and outliers. At the end of the course, you will apply all of the techniques you have learned to a case study to clean a real-world Gapminder dataset.
Cleaning data in R
- Website
- DataCamp
Data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analysing it. For this reason, it is critical to become familiar with the data cleaning process and the tools available to you. This course provides a basic introduction to cleaning data in R. After taking the course, you will be able to go from raw data to insights quickly and painlessly.
Cleaning data in R with Tidyverse and Data.table
- Website
- Udemy
Get your data ready for analysis with R packages tidyverse, dplyr, data.table, tidyr and more. You will learn about the tidyverse package system - a collection of packages that work together to produce clean data. This system helps you with the data cleaning process, from data import to the data query process.
Cleaning data in R: case studies
- Website
- DataCamp
Running analysis on interesting datasets is the dream of every data scientist. However, first, importing and cleaning must be done. In this series of four case studies, you will revisit key concepts from our courses on importing and cleaning data in R.
Data processing with Python
- Website
- Udemy
If you use a programming language such as Python, you can drastically reduce the time it takes to process your data and make it ready for use in your project. This course will show you how Python can be used to manage, clean and organise huge amounts of data.
Getting and cleaning data
- Website
- Coursera
This course will cover the basic ways that data can be obtained: from the web, from APIs, from databases and from colleagues in various formats. It will look at the basics of data cleaning and how to make data tidy. The course will also cover the components of a complete data set, including raw data, processing instructions, codebooks and processed data, and the basics needed for collecting, cleaning and sharing data.
Importing data in Python (Part 1)
- Website
- DataCamp
In this course, you will learn the many ways to import data into Python: from flat files such as .txt and .csv; files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; and from relational databases such as SQLite and PostgreSQL.
Importing data in Python (Part 2)
- Website
- DataCamp
In this course, you will extend your knowledge base from Importing data in Python (part 1). You will learn to import data from the web and by pulling data from Application Programming Interfaces (APIs), such as the Twitter streaming API, which allows us to stream real-time tweets.
Practical data cleaning: 19 essential tips to scrub your dirty data
- Website
- Udemy
Practical data cleaning is a thorough introduction to the basics of data cleaning. It is perfect for beginners and takes you through data collection, data cleaning, data classification and data integrity.
Displaying 1-10 of 10 results