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.
Basic Statistical Testing
Skills You'll Learn
Python Programming, Numpy, Pandas, Data Cleansing
Reviews
4.5 (27,108 ratings)
- 5 stars66.34%
- 4 stars24.29%
- 3 stars5.36%
- 2 stars1.91%
- 1 star2.09%
CB
Feb 6, 2023
The assessments, quizzes, and course coverage are quite good. The main points are covered, although it does not cover everything. Additionally, it provides opportunities to learn and conduct research.
HC
May 3, 2018
It's very useful specially for new learner because it only dives into the part of python that data science need. I strongly recommend to anyone even if you don't have experience in programming before.
From the lesson
Answering Questions with Messy Data
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
Taught By
Christopher Brooks
Associate Professor