Data Collection and Integration
Master Python data collection techniques from files, web, databases, and APIs. Learn to integrate diverse data sources efficiently.
Master Python data collection techniques from files, web, databases, and APIs. Learn to integrate diverse data sources efficiently.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Wrangling with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
Powered by
What you'll learn
Learn to collect and process data from various file formats including CSV, JSON, and XML
Master web scraping techniques using Python libraries like Beautiful Soup
Develop skills in database interaction and SQL query execution
Understand API integration and data retrieval methods
Gain expertise in combining data from multiple sources effectively
Skills you'll gain
This course includes:
0.9 Hours PreRecorded video
5 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
The Data Collection and Integration course provides comprehensive training in gathering and combining data from various sources. Students learn to work with different file formats, web scraping techniques, database interactions, and API integrations using Python. The curriculum covers essential tools like Pandas, Beautiful Soup, and SQL, preparing students to handle real-world data collection challenges and create unified datasets for analysis.
Collect Data From Files
Module 1 · 4 Hours to complete
Collect Data From Web
Module 2 · 5 Hours to complete
Collect Data From Database
Module 3 · 5 Hours to complete
Collect Data From APIs
Module 4 · 3 Hours to complete
Data Integration
Module 5 · 5 Hours to complete
Case Study
Module 6 · 3 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.