Introduction to Data Science in Python
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 lambdas, reading and manipulating csv files, and the numpy library.
This course will introduce the basics of learning 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 take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
You should take this course before any 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.
This course is part of “Applied Data Science with Python“ and is intended for learners with a basic python or programming background and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. This is an excellent path in learning data science.
Course 1 of 5 in the Applied Data Science with Python Specialization.
Institution: University of Michigan, via Coursera
Level: Intermediate
