Online Course top-rated
Dates and Times in Python

Name: Dates and Times in Python Course
Price: 36 USD

with Ivan Manov and Martin Ganchev

Take your data analyst skillset to the next level: master working with dates and times in Python

4.8

862 reviews on

Start for free

5,065 students already enrolled

Skill level:

Intermediate

Duration:

3 hours

Lessons (2 hours)

Practice exams (30 minutes)

CPE credits:

3.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Master working with dates, times, time zones, and time standards.
Boost your Python skills for handling date- and time-based data.
Apply date/time logic to analyze a real-world sales dataset.
Translate date-time insights into compelling visualizations.
Advance toward full Python proficiency through practical date-time tasks.

Topics & tools

PythonProgrammingData VisualizationJupyterDates And TimesTime ZonesData Preprocessing

Your instructor

Ivan Manov

AI & Data Science Course Creator | Sound Engineer

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Sound Engineering

Media and recognition

Author of Speech Recognition with Python on 365DS & Udemy
Speaker at Power Platform Bootcamp 2025 on Audio AI
Sound Engineer for TV/Film incl. The Bachelor & award-winners

Ask Ivan a question

Martin Ganchev

Content Creator & Content Manager at 365 Data Science | Data Science Instructor

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Economic and Social Sciences

Ask Martin a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 3.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Dates and Times in Python is the course that will take your data analyst skillset to the next level! The ability to adjust dates and time according to a specific territory is crucial across numerous businesses and industries. In this course, you will master handling both date values that depend on the structure of the different calendars used across the globe, and time values that reflect time-saving regulations, clock changes, and time conversions. You will also discover how to keep track of historical data, use specific date- and time-related libraries, classes, methods, and conversion techniques in Python.

Prerequisites

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Completion of an introductory Python course is required.

Advanced preparation

Introduction to Python

Curriculum

23 lessons 13 exercises 3 exams

1. Dates and Times in Python

76 min

Here, we’ll cover the concepts of time standards, time zones, and time regulations. We will discuss the main principles of working with dates, times, and time zones in computing and will learn how to apply these principles in Python.

76 min

Here, we’ll cover the concepts of time standards, time zones, and time regulations. We will discuss the main principles of working with dates, times, and time zones in computing and will learn how to apply these principles in Python.

What Does the Course Cover Free

Introduction to Working with Dates and Times Free

Time Standards and Regulations: GMT and UTC Free

Exercise

Principles of Working with Dates and Times in Computing – Unix Time Free

Exercise

Principles of Working with Dates and Times in Computing – Dates in Python Free

Coding exercise

The datetime Module: working with Dates Free

The datetime Module: working with Time Free

The datetime Module: working with the datetime class Free

Coding exercise

.strftime(): Converting datetime Objects into Strings Free

.strptime(): Converting Strings into datetime Objects Free

Coding exercise

pd.to_datetime(): Converting Data into datetime Objects

Coding exercise

Focus on Working with Timestamps

Exercise

Practical Application of Working with Timestamps in Python

Coding exercise

Converting between Timezones in Python: the pytz Module

Converting between Timezones with pandas

Coding exercise

Practice exam
2. A Practical Example: Working with Dates, Times, and Time Zones

47 min

This section consists of a practical example covering all the basic, as well as the advanced techniques you’ll need to become a data analyst who can handle dates and times with ease. We will work on an actual data set and will operate on tasks including processes such as data preparation, data manipulation, data analysis, and data visualisation.

47 min

This section consists of a practical example covering all the basic, as well as the advanced techniques you’ll need to become a data analyst who can handle dates and times with ease. We will work on an actual data set and will operate on tasks including processes such as data preparation, data manipulation, data analysis, and data visualisation.

Practical Example Exercise - Dates and Times

Introduction

Part I - Data Preparation part 1

Part I - Data Preparation part 2

Coding exercise

Part II - Data Manipulation part 1

Part II - Data Manipulation part 2

Coding exercise

Part III - Data Analysis

Coding exercise

Part IV - Data Visualization

Practice exam
3. Course exam

50 min

50 min

Course exam

Free lessons

1.1 What Does the Course Cover

4 min

1.2 Introduction to Working with Dates and Times

3 min

1.3 Time Standards and Regulations: GMT and UTC

6 min

1.5 Principles of Working with Dates and Times in Computing – Unix Time

3 min

1.7 Principles of Working with Dates and Times in Computing – Dates in Python

5 min

1.9 The datetime Module: working with Dates

7 min

Start for free

Very well designed, well narrated course. Starts from scratch and ends up at the advanced levels. The icing on the cake is the integration of My SQL with Tableau at the end of the course which is a very important aspect of today's Data Analytics field. I have personally saved the last 10 practice exercises to dive in some days later, thus it will enable me to revise the course in the process.

Nilotpal C.

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4.8

Based on 862 reviews

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When I began my master’s program, I felt lost—statistical jargon was overwhelming, and Python and R seemed impossible. 365 Data Science changed that. The clear explanations, structured courses, and practical projects helped me build a solid foundation in statistics, programming, and SQL. I not only completed my degree but also advanced to a better role. 365 made complex topics approachable and gave me skills that boosted both my academic and professional growth.

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After 365: Accounting Manager | JFE Engineering Corporation

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9 in 10

of our graduates landed a new AI & data job

after enrollment

One of the biggest takeaways from the bootcamp was the ability to tackle real-world data problems. During my job interview, I was given a practical task by the hiring managers, and thanks to the skills I gained from 365 Data Science, I was the only candidate who solved it! After this, I was hired as a BI developer and have now moved up to a data engineer.