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.
By the end of this course, you will be able to:
• Describe how to manipulate strings using techniques such as concatenating, indexing, slicing, and formatting
• Summarize the syntax of the range() function
• Explain the purpose and logic of iterative statements such as for loops and while loops
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.
涵盖的内容
3个视频1篇阅读材料1个作业3个非评分实验室
显示有关单元内容的信息
3个视频•总计13分钟
Introduction to loops and strings•1分钟
Michelle: Approach problems with an analytical mindset•3分钟
Introduction to while loops•9分钟
1篇阅读材料•总计8分钟
Loops, break, and continue statements•8分钟
1个作业•总计6分钟
Test your knowledge: While loops •6分钟
3个非评分实验室•总计50分钟
Annotated follow-along guide: Loops and strings•20分钟
Activity: While loops•20分钟
Exemplar: While loops•10分钟
For loops
第 2 单元•小时 后完成
单元详情
You'll explore for loops, another kind of iterative or repeating code.
涵盖的内容
2个视频1篇阅读材料1个作业2个非评分实验室
显示有关单元内容的信息
2个视频•总计8分钟
Introduction to for loops•4分钟
Loops with multiple range() parameters•4分钟
1篇阅读材料•总计8分钟
For loops•8分钟
1个作业•总计6分钟
Test your knowledge: For loops •6分钟
2个非评分实验室•总计30分钟
Activity: For loops•20分钟
Exemplar: For loops•10分钟
Strings
第 3 单元•小时 后完成
单元详情
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.
涵盖的内容
3个视频2篇阅读材料1个作业2个非评分实验室
显示有关单元内容的信息
3个视频•总计16分钟
Work with strings•4分钟
String slicing•7分钟
Format strings•5分钟
2篇阅读材料•总计16分钟
String indexing and slicing•8分钟
String formatting and regular expressions•8分钟
1个作业•总计6分钟
Test your knowledge: Strings•6分钟
2个非评分实验室•总计30分钟
Activity: Strings•20分钟
Exemplar: Strings•10分钟
Review: Loops and strings
第 4 单元•小时 后完成
单元详情
Review everything you’ve learned and take the final assessment.
Grow with Google is an initiative that draws on Google's decades-long history of building products, platforms, and services that help people and businesses grow. We aim to help everyone – those who make up the workforce of today and the students who will drive the workforce of tomorrow – access the best of Google’s training and tools to grow their skills, careers, and businesses.
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.
What do data professionals do?
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.
Do I need to take the course in a certain order?
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.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.