Course Summary
Learn the basics of data processing and visualization using Python. This course covers how to import, clean, and visualize data using Python libraries such as Pandas and Matplotlib.Key Learning Points
- Learn how to import and clean data using Python libraries such as Pandas
- Explore data visualization techniques using Matplotlib
- Apply your skills to real-world datasets and projects
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹4,65,000
- Spain: €28,000
- USA: $62,453
- India: ₹4,65,000
- Spain: €28,000
- USA: $73,000
- India: ₹6,00,000
- Spain: €30,000
- USA: $62,453
- India: ₹4,65,000
- Spain: €28,000
- USA: $73,000
- India: ₹6,00,000
- Spain: €30,000
- USA: $113,000
- India: ₹9,00,000
- Spain: €45,000
Related Topics for further study
Learning Outcomes
- Ability to import and clean data using Python libraries
- Understanding of data visualization techniques using Matplotlib
- Experience applying skills to real-world datasets and projects
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Access to a computer with Python and required libraries installed
Course Difficulty Level
BeginnerCourse Format
- Online
- Self-paced
- Video Lectures
Similar Courses
- Data Science Essentials
- Python Data Structures
Related Education Paths
Related Books
Description
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.
Knowledge
- 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
Outline
- Week 1: Introduction to Data Products
- What is a Data Product?
- Data Product Examples in Enterprise
- Developing a Data Product Strategy
- Python and Jupyter Basics
- Python Recap
- Livecoding: Getting Started With Jupyter
- Syllabus
- Course Materials
- Set Up Your System
- Our Case Study: Recommender Systems
- (Optional) Python: How to Run
- (Optional) Python: Additional Resources and Recommended Readings
- Review: Data Products
- Review: Python and Jupyter
- Week 2: Reading Data in Python
- CSV & JSON Files
- Reading CSV & JSON Files
- Processing Structured Data in Python
- Live-Coding: JSON
- Extracting Simple Statistics From Datasets
- Simple Statistics: Live-Coding
- Review: CSV and JSON Files
- Review: Simple Statistics
- Python: Reading Data and Simple Statistics
- Week 3: Data Processing in Python
- Data Filtering and Cleaning
- Processing Text and Strings in Python
- Processing Times and Dates in Python
- Livecoding: Time and Date Data
- Review: Data Filtering and Cleaning
- Review: Processing Different Data Types
- Data Processing in Python
- Week 4: Python Libraries and Toolkits
- Matrix Processing and Numpy
- Introduction to Data Visualization
- Introduction to Matplotlib
- Live-coding: MatPlotLib
- urllib and BeautifulSoup
- Review: NumPy
- Review: MatPlotLib
- Review: urllib and BeautifulSoup
- Python Libraries and Toolkits
- Final Project
- Course Summary
- Project Description
- Where to Find Datasets
Summary of User Reviews
Key Aspect Users Liked About This Course
Many users appreciated the practical nature of the course, with emphasis on real-world applications.Pros from User Reviews
- Informative
- Engaging
- Practical applications
- Good pacing
- Clear explanations
Cons from User Reviews
- Lack of depth in some areas
- Not enough exercises
- Limited interaction with instructors
- Some technical issues with platform
- Lack of variety in examples
Julian McAuley
4.3 Raiting
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