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Learn to Code for Data Analysis

Michel Wermelinger, Tom Mudd, Paul Streeter, George Matthew, Matthew Jacques, Tony Hirst, Rob Griffiths, Suraj Pandey, and Andreas Wernecke

Topics Covered

Python: variables, assignments, expressions, basic data types, if-statement, functions
Programming: using Jupyter Notebooks, writing readable and documented code, testing code
Data analysis: using pandas to read CSV and Excel files, to clean, filter, partition, aggregate and summarise data, and to produce simple charts

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Reviews summary

Intermediate python proficiency course

This course assumes prior proficiency in Python. The course covers a variety of topics including Jupyter Notebooks, writing readable and documented code, testing code, using pandas, using CSV and Excel files, and properly cleaning, filtering, partitioning, aggregating, and summarizing data. Reviews are mixed but overall students who had some experience with Python found the course useful. The pace of the course is slow, and some students think that it can be completed in less than the four weeks estimated by FutureLearn.

The pace is slow.

"I am still doing this course after three months , future learn rate it as four weeks, crazy..."

Course assumes some Python experience.

"This is a course for getting your feet wet in data analysis with Python."

"...Some basic knowledge of Python may make this course a bit more useful and easier to understand..."

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Time

20 hours

Via

FutureLearn

Institution

The Open University

Instructors

Michel Wermelinger

Tom Mudd

Paul Streeter

George Matthew

Matthew Jacques

Tony Hirst

Rob Griffiths

Suraj Pandey

Andreas Wernecke

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