About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the 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.
Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have 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. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Taught by: Christopher Brooks
| Basic Info |
Course 1 of 5 in the
Applied Data Science with Python Specialization.
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| Level | Intermediate |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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|---|---|---|
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No words. Smashing one
Python is huge. Course helps you focus and apply Python to data science.
Me? was new to Python; was novice at programming; had strong background in math and business (both helpful, but not prerequisite); read "Python for Data Analysis" by Wes Mckinney to supplement lack of programming experience -- focused on numpy and pandas chapters; frequented stack overflow
Overall, I liked the course but there were some flaws (not too big ones but still worth mentioning) - The way things were explained seemed more like just giving information about something rather than explaining it well (of course at times!). Exercises were really very good as they promoted individual learning more than just depending on what was taught. Recommended for beginners/
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It's a comprehensive course for the learners who wants to learn the skills of data science using python.