This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
Basic Data Processing and Visualization
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Basic Data Processing and Visualization
This course is part of Python Data Products for Predictive Analytics Specialization
Instructors: Julian McAuley
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Gain insight into a topic and learn the fundamentals.
4.3
(192 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 10 hours
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Develop data strategy and process for how data will be generated, collected, and consumed
Load and process formatted datasets such as CSV and JSON.
Deal with data in various formats (e.g. timestamps, strings) and filter and “clean” datasets by removing outliers etc.
Basic experience with data processing libraries such as numpy and data ingestion with urllib, requests
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
12 assignments
Taught in English
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This course is part of the Python Data Products for Predictive Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
This week, we will go over the syllabus and set you up with the course materials and software. We will introduce you to data products and refresh your memory on Python and Jupyter notebooks.
What's included
6 videos6 readings2 assignments2 discussion prompts
This week, we will learn how to load in datasets from CSV and JSON files. We will also practice manipulating data from these datasets with basic Python commands.
What's included
6 videos3 assignments1 discussion prompt
This week, our goal is to understand how to clean up a dataset before analyzing it. We will go over how to work with different types of data, such as strings and dates.
What's included
4 videos3 assignments1 discussion prompt
In this last week, we will get a sense of common libraries in Python and how they can be useful. We will cover data visualization with numpy and MatPlotLib, and also introduce you to the basics of webscraping with urllib and BeautifulSoup.
What's included
5 videos4 assignments1 peer review2 discussion prompts
Create your own Jupyter notebook with a dataset of your own choosing and practice data manipulation. Show off the skills you've learned and the libraries you know about in this project. We hope you enjoyed the course, and best of luck in your future learning!
What's included
1 video2 readings1 peer review1 discussion prompt
Instructors
Instructor ratings
4.6 (33 ratings)Offered by
Recommended if you're interested in Data Analysis
University of Colorado Boulder
University of California San Diego
Microsoft
University of Colorado Boulder
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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