I have compiled a list of suggested courses for advanced users below. These are specifically targeted and geared toward a career in data engineering and data science. These are the product of my training experiences and I have found these have provided significant learning and coding value for time (and money) spent.
As mentioned, I myself am a product of composite Udemy courses. It follows that these are from Udemy as well. On with the list..
[1] Data Analysis with Python and Pandas.
Building a training track for data science or data engineering, Python Pandas library is a must. Pandas, otherwise known as Python Data Analysis Library, is a library designed to manipulate and transform datasets with ease. It is designed to read from a variety of input formats, from structured to unstructured, as well as write it back to various formats.
Pandas strength lies in the flexibility to add columns, transform a dataframe to turn it as an input for visualizations or machine learning training and test sets. To make the story short, mastery of the pandas library is a data scientist's pride.
[2] The Complete Python 3 Course: Go from Beginner to Advanced!.
Data visualization is part of Python. Libraries are available to provide the functionality. That is discussed in depth in this course.
Pandas is built on the Numpy library. To compliment your mastery of Pandas, this course is a good tandem. Another skill to master is Regular Expressions, or regex. That is the last module to be discussed in this course.
[3] The Complete Python 3 Course: Beginner to Advanced!.
Not to be confused with #2 above. They are distinct courses.
What I liked most about this course is its practical approach. It dives into "projects" involving real life applications of Python. Django is also part of the curriculum. And the last project involving speech recognition and artificial intelligence is what I enjoyed most. Talk about saving the best for last!
These courses are what I found to provide the valuable modules that provide long term benefits and coding examples that you could use and re-use. Not only is Pandas used for extract, transform and load (ETL), but also for visualizations and projections to a map. These techniques will be applicable to day to day data analytics.
Some of the lessons in the beginners courses are reviewed in these courses. That just goes to show how important it is to go back to the basics. After taking the courses listed here and you find it a bit difficult to absorb the lessons discussed, take time to go back to the beginner courses. Remember, you may access the course again and again.
Also, having real world examples really invoke one's interest in pursuing learning Python. Some of these courses don't only contain practical approaches to data science and data engineering, they also tackle applications outside of that field.
RELATED: Top Python Courses for Beginners
You can always go back to these courses, including the basic courses, for reference. Udemy provides lifetime access to the courses enrolled. Next list will contain courses specifically directed to a data science and data engineering career -- for hardcore Python coders. Stay tuned.
