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.
|
| Level | Intermediate |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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|---|---|---|
| Access to course materials | Available | Available |
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Very well constructed course. Admittedly, there is a pretty big gap between the content in the lecture and what is needed to finish the programming assignment. However, I think this is by design to help learners understand the content better and to seek solutions outside classroom. Highly recommended for anyone interested in data science.
Hi Professor,
Thank you so much for taking the time to structure this program. Looking forward to another great learning experience in course 2.
Thanks,
Neel Roshania
Good course
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SA
This is a very nice, structured organized course to start on empirical machine learning.