In this course, you'll explore loops, which repeat a portion of code until a process is complete. You’ll learn how to work with different kinds of iterative or repeating code, such as for loops and while loops. Then, you'll explore strings, which are sequences of characters like letters or punctuation marks. You’ll learn how to manipulate strings by indexing, slicing, and formatting them.



Loops and Strings
Ce cours fait partie de Spécialisation Google Data Analysis with Python

Instructeur : Google Career Certificates
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Ce que vous apprendrez
How to manipulate strings using techniques such as concatenating, indexing, slicing, and formatting
Purpose and logic of iterative statements such as for loops and while loops
Be able to summarize the syntax of the range() function
Compétences que vous acquerrez
- Catégorie : Data Structures
Détails à connaître

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septembre 2025
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Il y a 4 modules dans ce cours
You'll explore loops, which repeat a portion of code until a process is complete. You’ll learn how to work with different kinds of iterative or repeating code, such as while loops.
Inclus
3 vidéos1 lecture1 devoir3 laboratoires non notés
You'll explore for loops, another kind of iterative or repeating code.
Inclus
2 vidéos1 lecture1 devoir2 laboratoires non notés
You'll explore strings, which are sequences of characters like letters or punctuation marks. You’ll learn how to manipulate strings by indexing, slicing, and formatting them.
Inclus
3 vidéos2 lectures1 devoir2 laboratoires non notés
Review everything you’ve learned and take the final assessment.
Inclus
1 lecture1 devoir
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Foire Aux Questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science is part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
We highly recommend taking the courses in the order presented, as the content builds on information from earlier courses. This is the third course in a series of six courses that make up the Google Data Analysis with Python Specialization.
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