1 option
Exploratory data analysis with Pandas and Python 3.x / Mohammed Kashif.
- Format:
- Video
- Author/Creator:
- Kashif, Mohammed, author, speaker.
- Series:
- Academic Video Online
- Language:
- English
- Subjects (All):
- Python (Computer program language).
- Information visualization.
- Electronic data processing.
- Genre:
- Instructional films.
- Physical Description:
- 1 online resource (301 minutes)
- Place of Publication:
- Birmingham, England : PACKT Publishing, 2019.
- Language Note:
- In English.
- System Details:
- video file
- Summary:
- Improve your understanding of descriptive statistics and apply them over a dataset. Learn how to deal with missing data and outliers to resolve data inconsistencies. Explore various visualization techniques for bivariate and multivariate analysis. Enhance your programming skills and master data exploration and visualization in Python. Learn multidimensional analysis and reduction techniques. Master advanced visualization techniques (such as heatmaps) for better analysis, and rapidly broaden your understanding. About: How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on course shows non-programmers how to process information that's initially too messy or difficult to access. Through various step-by-step exercises, you'll learn how to acquire, clean, analyze, and present data efficiently. This course will take you from Python basics to explore many different types of data. Throughout the course, you will be working with real-world datasets to retrieve insights from data. You'll be exposed to different kinds of data structure and data-related problems. You'll learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! All the code files and related files are placed on the GitHub.
- Participant:
- Presenter, Mohammed Kashif.
- Notes:
- Title from resource description page (viewed March 25, 2020).
- OCLC:
- 1125343530
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.