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There are 5 modules in this course
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.
What's included
5 videos3 readings1 assignment3 ungraded labs
Show info about module content
5 videos•Total 20 minutes
Introduction to data structures in Python•1 minute
Introduction to lists•5 minutes
Modify the contents of a list•4 minutes
Introduction to tuples•4 minutes
More with loops, lists, and tuples•6 minutes
3 readings•Total 20 minutes
Reference guide: Lists•8 minutes
Compare lists, strings, and tuples•8 minutes
zip(), enumerate(), and list comprehension•4 minutes
1 assignment•Total 8 minutes
Test your knowledge: Lists and tuples•8 minutes
3 ungraded labs•Total 50 minutes
Annotated follow-along guide: Data structures in Python•20 minutes
Activity: Lists & tuples •20 minutes
Exemplar: Lists & tuples •10 minutes
Dictionaries and sets
Module 2•1 hour to complete
Module details
In this module, you will focus on dictionaries and sets, some more data structures that are among the most useful for data professionals.
What's included
3 videos2 readings1 assignment2 ungraded labs
Show info about module content
3 videos•Total 15 minutes
Introduction to dictionaries•5 minutes
Dictionary methods•5 minutes
Introduction to sets•6 minutes
2 readings•Total 8 minutes
Reference guide: Dictionaries•4 minutes
Reference guide: Sets•4 minutes
1 assignment•Total 6 minutes
Test your knowledge: Dictionaries and sets•6 minutes
2 ungraded labs•Total 30 minutes
Activity: Dictionaries & sets•20 minutes
Exemplar: Dictionaries & sets•10 minutes
Arrays and vectors with NumPy
Module 3•1 hour to complete
Module details
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.
What's included
3 videos3 readings1 assignment2 ungraded labs
Show info about module content
3 videos•Total 15 minutes
The power of packages•4 minutes
Introduction to NumPy•4 minutes
Basic array operations•6 minutes
3 readings•Total 12 minutes
Understand Python libraries, packages, and modules•4 minutes
Python’s new versions and features•4 minutes
Reference guide: Arrays•4 minutes
1 assignment•Total 6 minutes
Test your knowledge: Arrays and vectors with NumPy•6 minutes
2 ungraded labs•Total 30 minutes
Activity: Arrays and vectors with NumPy•20 minutes
Exemplar: Arrays and vectors with NumPy•10 minutes
Dataframes with pandas
Module 4•1 hour to complete
Module details
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.
What's included
5 videos3 readings1 assignment2 ungraded labs
Show info about module content
5 videos•Total 35 minutes
Introduction to pandas•5 minutes
pandas basics•10 minutes
Boolean masking•6 minutes
Grouping and aggregation•6 minutes
Merging and joining data•9 minutes
3 readings•Total 12 minutes
The fundamentals of pandas•4 minutes
Boolean masking in pandas •4 minutes
More on grouping and aggregation•4 minutes
1 assignment•Total 8 minutes
Test your knowledge: Dataframes with pandas•8 minutes
2 ungraded labs•Total 30 minutes
Activity: Dataframes with pandas•20 minutes
Exemplar: Dataframes with pandas•10 minutes
Review: Data structures in Python
Module 5•1 hour to complete
Module details
Review everything you’ve learned and take the final assessment.
What's included
1 reading1 assignment
Show info about module content
1 reading•Total 5 minutes
Wrap-up•5 minutes
1 assignment•Total 55 minutes
Course 4 challenge: Data structures in Python•55 minutes
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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.