Tag: data cleaning

Getting and Cleaning Data

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This is course 3 of 10 in the Coursera Data Science Specialization. From Coursera: “Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. … Continue Reading

New York University – Bachelor of Science in Applied Data Analytics and Visualization

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From NYU: “The BS in Applied Data Analytics and Visualization will prepare you to transform data into valued insight for a variety of decision makers. You will learn techniques to set-up systems to retrieve, aggregate, and process large data sets; separate big data sets into manageable and logical components; and eliminate ‘noise’ by cleaning data. You also will learn different methods of data analysis and visualization, aided by statistical and graphics … Continue Reading

Data Science Essentials

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from edX: “Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft. In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or … Continue Reading

Pandas Cheat Sheet – Python for Data Science

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“It’s common when first learning pandas to have trouble remembering all the functions and methods that you need, and while we at Dataquest advocate getting used to consulting the pandas documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out!” This cheat sheet covers the following topics: Key and Imports Importing Data Exporting Data Create Test Objects Veiwing/Inspecting Data Selection … Continue Reading

Learning Path: Your mentor to become a machine learning expert

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From Analytics Vidhya: “Through this learning path, we hope to provide you an answer to this problem. We have deliberately loaded this learning path with a lot of practical projects. You can not master machine learning with the hard work! But once you do, you are one of the highly sought after people around. Since this is a complex topic, we recommend you to strictly follow the steps in sequential … Continue Reading

DataCamp – Exploratory Data Analysis in R: Case Study

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From DataCamp: “Once you’ve started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You’ll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you’ll gain more practice with the dplyr and ggplot2 packages, learn about the … Continue Reading

DataCamp – Importing and Cleaning Data in R: Case Studies

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From DataCamp: “Running exciting analyses on interesting datasets is the dream of every scientist. But first, some importing and cleaning must be done. In this series of four case studies, you’ll revisit key concepts from our courses on importing and cleaning data in R.” This course consists of four chapters (case studies): Ticket Sales Data MBTA Ridership Data World Food Facts School Attendance Data

DataCamp – Cleaning Data In R

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From DataCamp: “It’s commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R, so that you can get from raw data … Continue Reading

DataQuest – Python Programming: Intermediate Course

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This course is step 2 of 6 in the DataQuest Data Analyst Path and step 2 of 11 in the DataQuest Data Scientist Path. From DataQuest: “Learn some more aspects of Python, including modules, enumeration, indexing, and scopes. What you’ll learn: Learn how to create modules and classes to organize your code Learn how to handle errors, use list comprehensions, and use regular expressions Learn about variable scoping and the date … Continue Reading

Introduction to Data Science in Python

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This course is #1 of 5 in the Applied Data Science with Python Specialization from the University of Michigan. From Coursera: “This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas … Continue Reading