Applied Python Pandas, Data Analysis with Pandas Library in Python Programming Language..
Course Description
Applied Python Pandas is a course on pandas library, which is used for data analysis using python programming language.
The intent of this course is to learn data analysis with pandas by solving problems that can be encountered in real world.
This course has following lectures.
- Overview of Pandas
- Data Structures
- Data Selection & Data Filtering
- Groupby, Aggregation & Visualisation.
- Time Series
We will be answering below questions with scenario based examples.
– What is Series and DataFrame in Pandas? When to use them?
– How would we inspect the structure of a DataFrame?
– What methods would we use to summarize the statistical properties of the columns?
– What are the differences between .loc[], .iloc[], .at[] and .iat[] in Pandas?
– What are different ways of adding new column to a DataFrame?
– What are differently ways of filter data in a DataFrame?
– How do we group data and perform aggregations in Pandas?
– How do we do times series analysis in Pandas?
Each section is presented with a scenario and set of tasks to solve. As we solve each task we shall learn new concepts in pandas library. All the code/data presented in the course is available in GitHub. When you clone the git repository you would get both exercise and solution notebooks. Exercise notebooks can be used for coding along as you watch exercises being solved in the course videos.
The focus of this course is to apply our learnings to solve real world issues, whether you are a Student, Professional or Business person this course shall give you enough knowledge in short time to work with data you encounter in your field to derive insights and solve real problems with a data driven approach.
If you are new to Data Analysis and Python Pandas you will find this course useful and can provide you a jump start.
The course is easy to follow even if you are new to python.
Disclaimer: The insights we draw as we solve different tasks in the course are to demonstrate data analysis with pandas, it is not a business or financial advise.
