Tareq S.
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Online Course
Intermediate Python Programming
with
Martin Ganchev
Boost your Python programming skills: Learn advanced text manipulation and efficient coding techniques
7,149 students already enrolled
Skill level:
Intermediate
Duration:
1 hour
-
Lessons (1 hour)
-
Practice exams (10 minutes)
- Lessons (1 hour)
- Practice exams (10 minutes)
CPE credits:
1.5
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
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
- Work with text data and argument specifiers effectively.
- Use list comprehensions to write concise, efficient code.
- Apply anonymous (lambda) functions to streamline your code.
- Solve complex problems using multi-level iteration and nested loops.
- Become a proficient Python programmer.
Topics & tools
JupyterProgrammingList ComprehensionsNested LoopsWorking with Text DataPythonAnonymous (Lambda) Functions
Your instructor
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
Course OVERVIEW
Description
CPE Credits: 1.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
This course is short yet intense. We assume you’re no stranger to Jupyter and Python fundamentals, such as conditionals, functions, sequences, and iterations. Nevertheless, a lack of experience working with text data, list comprehensions, and anonymous (Lambda) functions would immediately reveal to your data science colleagues that coding in Python hasn’t become second nature. In the following sections, we explain these intermediate programming tools in detail and provide an extensive set of exercises to ensure your ability to comply with today’s best practices in the field.
Prerequisites
- Python (any recent version, such as Python 3.8 or later) and a code editor or IDE (e.g., Spyder, VS Code, or Jupyter Notebook)
- Completion of an introductory Python course is recommended.
Advanced preparation
Curriculum
13 lessons
11 exercises
2 exams
- 2. Working with Text Data in Python41 minWhether you use Python for general programming or analytics, you’ll not only manage quantitative data; you’ll also need to clean and preprocess vast amounts of text data. This section covers various tools—such as argument specifiers and string accessors—to help you manipulate text data. We’ll also work with widely-applicable Python string methods, such as .split(), .strip(), and .format().41 minWhether you use Python for general programming or analytics, you’ll not only manage quantitative data; you’ll also need to clean and preprocess vast amounts of text data. This section covers various tools—such as argument specifiers and string accessors—to help you manipulate text data. We’ll also work with widely-applicable Python string methods, such as .split(), .strip(), and .format().Dealing with Text Data and Argument Specifiers FreeCoding exerciseWorking with Python Strings at the Next Level FreeCoding exerciseExploring Python String Methods - Part ICoding exerciseExploring Python String Methods - Part IICoding exerciseLearning How to Use String AccessorsCoding exerciseWorking with the .format() MethodCoding exercise
- 3. Nested for Loops, List Comprehensions, and Anonymous Functions32 minOnce accustomed to manipulating text data, doors will open for you to employ several tools that will optimize the use of iterations and functions to the highest level. You’ll learn and apply iteration over range objects, nested for loops, list comprehensions, and anonymous (Lambda) functions.32 minOnce accustomed to manipulating text data, doors will open for you to employ several tools that will optimize the use of iterations and functions to the highest level. You’ll learn and apply iteration over range objects, nested for loops, list comprehensions, and anonymous (Lambda) functions.The Concept of Iterating Over Range Objects in Python FreeIntroduction to Nested For LoopsCoding exerciseWorking with Triple Nested For LoopsCoding exerciseUsing List ComprehensionsCoding exerciseCoding exerciseWorking with Anonymous (Lambda) FunctionsCoding exercisePractice exam
- 4. Course exam13 min13 minCourse exam
Free lessons
1.1 Course Introduction
3 min
2.1 Dealing with Text Data and Argument Specifiers
9 min
2.3 Working with Python Strings at the Next Level
4 min
2.5 Exploring Python String Methods - Part I
7 min
2.7 Exploring Python String Methods - Part II
7 min
3.1 The Concept of Iterating Over Range Objects in Python
4 min
96%
of our students recommend
365 Data Science.
9 in 10
people walk away career-ready
with practical data and AI skills.
94%
of AI and data science graduates
successfully change
or advance their careers.
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-study learning plan.
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