Design principles for software development with object-oriented programming
With the training Python - Focus Object-Oriented Programming you deepen your Python knowledge and bring it to the next level.
You will learn the most important design principles and best practices of object-oriented programming, so that you can implement your software projects even faster and more efficiently.
The advanced training is designed for those who already have experience with Python and want to use the programming language effectively in a professional context.
In the first chapter you will take a deeper look at function definition and learn about default values, type hints and assert statements. Afterwards you can use functions even better as tools for your projects. You will work with the terms list comprehension and dictionary comprehension for the efficient creation of lists and dictionaries. At the end of the chapter, you will learn how to adapt your code to the industry standard PEP8 by means of layout and structure.
In the second chapter, you'll learn what OOP is, what programming principles are based on it, and what conclusions you can draw from it, using simple examples. In the main part of the chapter you will explore how classes and attributes are defined and used. Using examples, you will examine instance methods and their use and definition with method chaining. You will learn what the self keyword is and how to distinguish debugging from class definitions. Finally, you will test your knowledge in an interactive intermediate project and repeat the exercises from the chapter.
In the third chapter, you'll learn what inheritance and composition are and how to use these concepts in use cases. In addition to simple inheritance, you'll also learn more advanced methods such as inheritance hierarchy and multiple inheritance. You'll practice deep inheritance methods that are used when reusing data from parent to child classes, compensating for data loss. Finally, you'll learn the most important unit testing best practices to help you find bugs in your code before your users find them.
In the fourth chapter you will deal with more advanced concepts of object-oriented programming, which will accompany you in your everyday work. You will study how programs and modules differ and what role object __main__ plays a role in this. You will learn what decorators are and how to use property decorators optimally. You will look at static and class methods and which special methods and class representations are possible with __ str__ () and __repr__ () can be used. Based on this, you will learn about the representation options of operator overloading and other important methods from the Python Standard Library and then apply the learned content in a company-relevant interim project.
In the fifth chapter, you will demonstrate your knowledge in two larger projects that represent classic use cases of object-oriented programming. In the first project, you will build your own interface to a popular data science library that enables further uses for machine learning or data or text analysis. The second project deals with programming your own blockchain, where you will learn the underlying concepts in more detail.
The demand for data experts is high. Around 4 million data experts will be needed in Europe by 2025. And in 2021 alone, more than 80,000 jobs for IT specialists were advertised in Germany. The demand for data and AI experts in particular continues to grow enormously.
But a decision for a data career is so much more than just a safe decision for the future! As a data expert, you deal with powerful, socially relevant topics, are a tech professional, and are communicative and creative at the same time. The profession is varied, can be combined with most other professions and offers an attractive salary. And most importantly, with us it can be learned unerringly!
Yes, after successful completion of the training, you will receive a certificate of completion from us that you can show in your job applications. Data Analysts and Data Scientists are desperately sought after in many business sectors. Even without relevant work experience, your chances of finding an entry-level job are good. In addition, there are analysts in almost every industry who have different job titles, but the skills you need are the same as those of a data analyst or data scientist.
No, the training is designed to be flexible and part-time. You can pursue your profession without restrictions and can schedule your learning times as they fit best for you. If you suddenly have more time available, you can contact us by e-mail and we will activate the learning content for the part-time version in your account. In this variant you can complete the training within only four weeks. If you realize that you need more time, you can still complete the content in the regular time.
Yes, our online training courses are designed to offer you the greatest possible flexibility. In general, we recommend that you plan six to eight hours per week for learning. When you want to schedule this time is up to you and is not prescribed by us. In our career paths, the Data Analyst and Data Scientist course, we offer live webinars where you can ask our mentors questions, but you don't have to attend if it doesn't fit into your schedule.
(Participants in our sponsored training courses are excluded. These must attend a fixed number of hours per week and are obliged to participate in the live webinars).
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