In this course, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data loading, cleaning, and binning. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas.
By the end of this course, you will be able to:
• Explain how to manipulate dataframes using techniques such as selecting and indexing, boolean masking, grouping and aggregating, and merging and joining
• Describe the main features and methods of core pandas data structures such as dataframes
• Describe the main features and methods of core NumPy data structures such as arrays and series
• Define Python tools such as libraries, packages, modules, and global variables
• Describe the main features and methods of built-in Python data structures such as lists, tuples, dictionaries, and sets
In this module, you will explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on lists and tuples, data structures that are among the most useful for data professionals.
涵盖的内容
5个视频3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
5个视频•总计20分钟
Introduction to data structures in Python•1分钟
Introduction to lists•5分钟
Modify the contents of a list•4分钟
Introduction to tuples•4分钟
More with loops, lists, and tuples•6分钟
3篇阅读材料•总计20分钟
Reference guide: Lists•8分钟
Compare lists, strings, and tuples•8分钟
zip(), enumerate(), and list comprehension•4分钟
1个作业•总计8分钟
Test your knowledge: Lists and tuples•8分钟
3个非评分实验室•总计50分钟
Annotated follow-along guide: Data structures in Python•20分钟
Activity: Lists & tuples •20分钟
Exemplar: Lists & tuples •10分钟
Dictionaries and sets
第 2 单元•小时 后完成
单元详情
In this module, you will focus on dictionaries and sets, some more data structures that are among the most useful for data professionals.
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15分钟
Introduction to dictionaries•5分钟
Dictionary methods•5分钟
Introduction to sets•6分钟
2篇阅读材料•总计8分钟
Reference guide: Dictionaries•4分钟
Reference guide: Sets•4分钟
1个作业•总计6分钟
Test your knowledge: Dictionaries and sets•6分钟
2个非评分实验室•总计30分钟
Activity: Dictionaries & sets•20分钟
Exemplar: Dictionaries & sets•10分钟
Arrays and vectors with NumPy
第 3 单元•小时 后完成
单元详情
In this module, you will focus on arrays. You’ll learn about one of the most widely used and important Python tools for advanced data analysis: NumPy.
涵盖的内容
3个视频3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15分钟
The power of packages•4分钟
Introduction to NumPy•4分钟
Basic array operations•6分钟
3篇阅读材料•总计12分钟
Understand Python libraries, packages, and modules•4分钟
Python’s new versions and features•4分钟
Reference guide: Arrays•4分钟
1个作业•总计6分钟
Test your knowledge: Arrays and vectors with NumPy•6分钟
2个非评分实验室•总计30分钟
Activity: Arrays and vectors with NumPy•20分钟
Exemplar: Arrays and vectors with NumPy•10分钟
Dataframes with pandas
第 4 单元•小时 后完成
单元详情
In this module, you will learn about one of the most widely used and important Python tools for advanced data analysis: pandas. You’ll also discover how to categorize data using data loading, cleaning, and binning.
涵盖的内容
5个视频3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
5个视频•总计35分钟
Introduction to pandas•5分钟
pandas basics•10分钟
Boolean masking•6分钟
Grouping and aggregation•6分钟
Merging and joining data•9分钟
3篇阅读材料•总计12分钟
The fundamentals of pandas•4分钟
Boolean masking in pandas •4分钟
More on grouping and aggregation•4分钟
1个作业•总计8分钟
Test your knowledge: Dataframes with pandas•8分钟
2个非评分实验室•总计30分钟
Activity: Dataframes with pandas•20分钟
Exemplar: Dataframes with pandas•10分钟
Review: Data structures in Python
第 5 单元•小时 后完成
单元详情
Review everything you’ve learned and take the final assessment.
涵盖的内容
1篇阅读材料1个作业
显示有关单元内容的信息
1篇阅读材料•总计5分钟
Wrap-up•5分钟
1个作业•总计55分钟
Course 4 challenge: Data structures in Python•55分钟
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.
Why start a career in data science?
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
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 fourth 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.