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This course is part of Python Data Products for Predictive Analytics Specialization
Instructors: Julian McAuley
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(193 reviews)
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Intermediate level
Basic knowledge of programming (any language)
(193 reviews)
Recommended experience
Intermediate level
Basic knowledge of programming (any language)
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
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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.
This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
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.
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.
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.
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.
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!
1 video2 readings1 peer review1 discussion prompt
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
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Reviewed on Jun 29, 2022
Great content. When you apply yourself to this course , there's no "dirty" data you can't handle.
Reviewed on Mar 4, 2021
Really nice. Learnt a lot ! Thanks to the faculties and UC San Diego.
Reviewed on Jun 29, 2019
Excellent to start your career in machine learning!!!
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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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