Description
Introduction to Data Science in Python course. This course introduces the student to the basics of the Python programming environment, including basic Python programming techniques such as Lambda, reading and manipulating csv files, and the numpy library. This course introduces data manipulation and cleansing techniques using Python’s famous Panda data science library, and introduces series abstraction and DataFrames as central data structures for data analysis, as well as training in the use of features such as grouping, merging, and pivot tables. In fact, by the end of this course, students will be able to take tabular data, clean it, manipulate it, and perform basic statistical analysis. This course must be taken before other applied data science courses using Python: Applied Mapping, Charting, and Data Visualization in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.”
What you’ll learn in the Introduction to Data Science in Python tutorial series
- Learn techniques like Lambda and working with csv files.
- Describe common Python features and capabilities used for data science.
- Finding DataFrame structures to clean and process.
- Explain distribution, sampling and t-test
Course details
- Publisher: Coursera
- English language
- Duration: 4 hours
- Number of courses: 4
- Teacher: Christopher Brooks
- File format: mp4
- Course level: from introductory to advanced
- Presenting Institution/University: University of Michigan
Courses available in the Introduction to Data Science in Python training series.
Training Set Prerequisites
Images
Example video course
installation instructions
Once extracted, watch using your favorite player.
English subtitles
Quality: 540p
Download link
Password for file(s): www.downloadly.ir
size
383 MB