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October 7, 2024
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Launch your career in Data Science. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.
Instructor: Di Wu
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(26 reviews)
Recommended experience
Beginner level
Students are expected to know basic Python and statistics to be most comfortable.
(26 reviews)
Recommended experience
Beginner level
Students are expected to know basic Python and statistics to be most comfortable.
Define techniques and methods for collecting data from various sources including files, web, databases, etc.
Identify statistical analysis and visualization techniques that can be used to gain insights into the data.
Calculate and apply techniques for data preprocessing such as dealing with missing values, outliers, sampling, normalization, and discretization.
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This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
Applied Learning Project
The final project provides students with an opportunity to apply the knowledge gained throughout the specialization in a real-life data wrangling project of their interest. Participants will follow the data wrangling pipeline step by step, from identifying data sources to processing and integrating data, to achieve a fine dataset ready for analysis. This course enables students to gain hands-on experience in the data wrangling process and prepares them to handle complex data challenges in real-world scenarios.
You will be able to describe the fundamentals of programming in Python.
You will be able to identify data structures for efficient organization and manipulation of data.
You will practice using NumPy and Pandas for numerical computing, data manipulation, and analysis.
How to utilize Python and Python packages to collect data from various sources
How to integrate data collected from various sources to a unified dataset for further processing and analysis
Understand and communicate the various statistical aspects of datasets, including measures of central tendency, variation, location, and correlation.
Utilize Pandas for data manipulation and preparation to set the foundation for data visualization.
Utilize Matplotlib and Seaborn to create accurate and meaningful data visualizations.
Understand the importance of data processing and manipulation in the data analysis pipeline.
Learn techniques to handle missing values and outliers, data reduction, and data scaling and discretization.
Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.
Initiate and conduct a data wrangling project from raw data to a refined dataset for analysis.
Apply data wrangling techniques learned in the specialization to handle real-life data scenarios.
Utilize Python libraries and tools effectively for data wrangling tasks. Communicate and present data wrangling results effectively to stakeholders.
CU Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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It will take about 2 months to complete this Specialization.
Learners are expected to have a mastery of the fundamentals of Python programming and statistics.
It is recommended to complete the courses in this Specialization sequentially.
Data Wrangling with Python cannot be taken for academic credit.
By completing this Specialization, you will have a foundation of data wrangling in Python for data science.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
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