Explore Raw Data

This course is part of Google Data Analysis with Python Specialization

Instructor: Google Career Certificates

Access provided by DAVIbank y BNP Paribas Cardif

1,649 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

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.

10 reviews

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

Tools you'll learn

JSON

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

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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 videos Total 34 minutes

Introduction to data exploration 3 minutes
Yaser: Understand data to drive value 2 minutes
Where the data comes from 9 minutes
Find stories using the six exploratory data analysis practices 10 minutes
EDA using basic data functions with Python 10 minutes

3 readings Total 24 minutes

Reference guide: The EDA process 8 minutes
Reference guide: Import datasets with Python 8 minutes
Reference guide: Pandas methods for the discovery of a dataset 8 minutes

1 assignment Total 8 minutes

Test your knowledge: Discovering is the beginning of an investigation 8 minutes

3 ungraded labs Total 100 minutes

Annotated follow-along resource: EDA using basic functions with Python 20 minutes
Activity: Discover what is in your dataset 60 minutes
Exemplar: Discover what is in your dataset 20 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 videos Total 20 minutes

Discover what is missing from your dataset 6 minutes
Date string manipulations with Python 14 minutes

1 reading Total 8 minutes

Reference guide: Datetime manipulation 8 minutes

1 assignment Total 6 minutes

Test your knowledge: Understand data format 6 minutes

1 ungraded lab Total 20 minutes

Annotated follow-along guide: Date string manipulations with Python 20 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.