Introduction to Data Science in Python
4.4 /5- Offered byCoursera
Introduction to Data Science in Python at Coursera Overview
Duration | 16 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Data Science in Python at Coursera Highlights
- Earn a Certificate on successful course completion from University of Michigan
- Get unlimited access to the course content
- A great course for learning Python for data science
- 35% got a tangible career benefit from this course
- 10 % got a pay increase or promotion
Introduction to Data Science in Python at Coursera Course details
- Offered by the University of Michigan, the Introduction to Data Science in Python course focuses on the basics of the python programming environment. In this course, you will dive into data science using Python and learn how to effectively analyze data. It covers topics such as Lambdas, Numpy library, and Query DataFrame structures
- In this 31-hour intermediate-level course, the learners will be introduced to python programming techniques and data manipulation and cleaning techniques with Python pandas data science library. On completion of this course, you will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses
- The course is delivered by Christopher Brooks who is an assistant professor at the University of Michigan
Introduction to Data Science in Python at Coursera Curriculum
Week 1
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
Week 2
In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question.
Week 3
In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
Week 4
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
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