Explore Raw Data

This course is part of Google Data Analysis with Python Specialization

Instructor: Google Career Certificates

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Access provided by Workforce Innovation Network (WIN) LLC

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

7 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Identify ethical issues that may come up during the data “discovering” practice of EDA
Using the PACE workflow to understand whether given data is adequate and applicable to a data science project
Recognize when and how to communicate status updates and questions to key stakeholders

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Google Data Analysis with Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

Finding stories in data using exploratory data analysis (EDA) is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. In this course, you’ll learn how to use Python to perform the EDA practices of discovering and structuring.

By the end of this course, you will be able to: • Identify ethical issues that may come up during the data “discovering” practice of EDA • Use Python to merge or join data based on defined criteria • Use Python to sort and/or filter data • Use relevant Python libraries for cleaning raw data • Recognize opportunities for creating hypotheses based on raw data • Recognize when and how to communicate status updates and questions to key stakeholders • Apply Python tools to examine raw data structure and format. • Use the PACE workflow to understand whether given data is adequate and applicable to a data science project • Differentiate between the common formats of raw data sources (json, tabular, etc.) and data types

Data professionals must understand data sources, file formats, and responsible parties during exploratory analysis. In this module, you will learn when to contact data owners for questions or issues, how to import data using Python and perform EDA using basic functions in Python.

What's included

5 videos3 readings1 assignment3 ungraded labs

5 videosTotal 33 minutes

Introduction to data exploration3 minutes
Yaser: Understand data to drive value2 minutes
Where the data comes from9 minutes
Find stories using the six exploratory data analysis practices 9 minutes
EDA using basic data functions with Python10 minutes

3 readingsTotal 24 minutes

Reference guide: The EDA process8 minutes
Reference guide: Import datasets with Python8 minutes
Reference guide: Pandas methods for the discovery of a dataset8 minutes

1 assignmentTotal 8 minutes

Test your knowledge: Discovering is the beginning of an investigation8 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along resource: EDA using basic functions with Python20 minutes
Activity: Discover what is in your dataset60 minutes
Exemplar: Discover what is in your dataset20 minutes

EDA discovery uses targeted questioning to identify data gaps and missing information. In this module, you will learn how to formulate hypotheses, manipulate datetime strings and create bar graph visualizations.

What's included

2 videos1 reading1 assignment1 ungraded lab

2 videosTotal 19 minutes

Discover what is missing from your dataset5 minutes
Date string manipulations with Python13 minutes

1 readingTotal 8 minutes

Reference guide: Datetime manipulation8 minutes

1 assignmentTotal 6 minutes

Test your knowledge: Understand data format6 minutes

1 ungraded labTotal 20 minutes

Annotated follow-along guide: Date string manipulations with Python20 minutes

Structuring is an EDA practice for organizing data to learn more about it. In this module, you will learn different types of structuring methods, pandas tools for structuring datasets, and interpret histograms to understand data distributions.