Data Analysis with Python and Pandas
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Python programmers are some of the most sought-after employees in the tech world, and Python itself is fast becoming one of the most popular programming languages. One of the best applications of Python however is data analysis which also happens to be something that employers ca not get enough of. Gaining skills in one or the other is a guaranteed way to boost your employability but put the two together and you will be unstoppable.
Python data analytics made Simple
This course contains 51 lectures and 6 hours of content, specially created for those with an interest in data analysis, programming, or the Python programming language. Once you have Python installed and are familiar with the language, you will be all set to go.
The course begins with covering the fundamentals of Pandas (the library of data structures you will be using) before delving into the most important functions you will need for data analysis; creating and navigating data frames, indexing, visualising, and so on. Next, you will get into the more intricate operations run in conjunction with Pandas including data manipulation, logical categorising, statistical functions and applications, and more. Missing data, combining data, working with databases, and advanced operations like resampling, correlation, mapping and buffering will also be covered.
By the end of this course, you will have not only have grasped the fundamental concepts of data analysis, but through using Python to analyse and manipulate your data, you will have gained a highly specific and much in demand skill set that you can put to a variety of practical used for just about any business in the world.
Students should be familiar with the Python programming language, specifically Python 3+
- People looking for methods to normalize the handling of multiple data types and databases
- Those interested in data analysis with Python
- Those interested in efficient data manipulation
Self-Paced Learning Outline
- Section 1: Introduction to the Course
- Section 2: Introduction to Pandas
- Section 3: IO Tools
- Section 4: Pandas Operations
- Section 5: Handling for Missing Data / Outliers
- Section 6: Combining Dataframes
- Section 7: Advanced Operations
- Section 8: Working with Databases
|Learning Style||Self-Paced Learning|
|Course Duration||6 Hours|