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About this Course
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.
WHAT YOU WILL LEARN
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Describe common Python functionality and features used for data science
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Explain distributions, sampling, and t-tests
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Query DataFrame structures for cleaning and processing
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Understand techniques such as lambdas and manipulating csv files
SKILLS YOU WILL GAIN
Python ProgrammingNumpyPandasData Cleansing
Syllabus – What you will learn from this course
3 hours to complete
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 Cou
functionality and features which data scientists use, and be introduced to the Cou
3 hours to complete
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 .
Python has for data cleaning and processing — pandas. You’ll learn how to read in .
3 hours to complete
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,
merge DataFrames, generate summary tables, group data into logical pieces,
6 hours to complete
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
distributions, sampling and t-tests. The majority of the week will be dedicated
- Introduction1m
- Distributions4m
- More Distributions8m
- Hypothesis Testing in Python10m
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