Dates and Times in Python Course

Hi!

My name is Ivan Manov and I'm happy to announce the brand-new addition to our program: Dates and Times in Python!

For me, being the co-author of this course together with Martin Ganchev was an exciting journey and I can't wait to share more details about it with you.

In this post, I’ll introduce you to its topics and the invaluable skills it will help you develop. I’ll also tell you a bit more about myself, Martin, and our work at 365 Data Science.

The 365 Data Science Dates and Times in Python Course

Why Dates and Times in Python?

Time is not just one of the most important factors for any business activity but also – for our everyday life. It helps us keep track of past events and to make plans for future occasions.

In business, knowing when a certain event occurred or is going to occur, is crucial. Such an event could be a transaction, a sale, a meeting, or even a job interview. We can’t imagine keeping track of such occasions without having information about the exact date and a time, can we?

But working with dates and times can be tricky. Their values depend on too many factors such as geographical regions, political restrictions, or regulation systems. Integrating these considerations into the programming world could be another challenge. That’s why anyone who works in a data science-related field must know how to handle date- and time-related information.

Who Is This Course for?

This course is for anyone who wants to become familiar with the fundamental techniques of working with Dates and Times in Python and integrate these techniques into the world of data science.

What Will You Learn in This Course?

Under our guidance, by the end of the course, you will be able to:

understand time standards, time zones, and time regulations
work with time measurement systems
apply different programming techniques for manipulating date and time values
use an essential Python module for working with dates and times
use different methods and classes from the Python arsenal
apply different conversion techniques
use pandas and NumPy for date- and time-related operations
make practical applications on an actual data set
visualize date- and time-related data in the form of different graphs

About the Authors

As promised, let me tell you a bit more about myself. I have a solid background in systems and sound engineering, along with information technologies and communications. I'm passionate about data analysis, data collection, Python programming, artificial intelligence, and consider data as a key aspect of our future because it is one of the biggest commodities in the world and is growing in value every single day.

Martin Ganchev, with whom I had the pleasure to co-write this course, is also the author of the Python, SQL, and Integration courses in the 365 Data Science Program. He has an MSc in Economic and Social Sciences from Bocconi University in Milan, Italy, where he gained advanced knowledge of Mathematics, Statistics, Econometrics, Time-Series, and Behavioral Economics & Finance.

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Ivan Manov

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New Course! Dates and Times in Python