Introduction to Data Science in Python
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as...
By Christopher Brooks on Coursera
About This Course
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Topics Covered
Frequently Asked Questions
How much does Introduction to Data Science in Python cost?
Visit the Introduction to Data Science in Python course page for current pricing and available discounts.
Who teaches Introduction to Data Science in Python?
Introduction to Data Science in Python is taught by Christopher Brooks, University of Michigan.
What skill level is Introduction to Data Science in Python for?
This course is designed for beginner learners.
Similar Courses
TensorFlow: Advanced Techniques
DeepLearning.AI
Microsoft Azure AI Fundamentals AI-900 Exam Prep
Microsoft
Data Analysts' Toolbox - Excel, Power BI, Python, & Tableau
Packt
Data Literacy: Exploring and Visualizing Data
SAS