This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.
Get Started with Python
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Get Started with Python
This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
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4.8
(1,330 reviews)
Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 30 hours
Learn at your own pace
98%
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What you'll learn
Explain how Python is used by data professionals
Explore basic Python building blocks, including syntax and semantics
Understand loops, control statements, and string manipulation
Use data structures to store and organize data
Skills you'll gain
Details to know
Shareable certificate
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Assessments
20 quizzes
Taught in English
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This course is part of the Google Advanced Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google
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There are 5 modules in this course
You’ll begin by exploring the basics of Python programming and why Python is such a powerful tool for data analysis. You’ll learn about Jupyter Notebooks, an interactive environment for coding and data work. You’ll investigate how to use variables and data types to store and organize your data; and, you'll begin practicing important coding skills.
What's included
12 videos7 readings4 quizzes3 ungraded labs
Next, you’ll discover how to call functions to perform useful actions on your data. You’ll also learn how to write conditional statements to tell the computer how to make decisions based on your instructions. And you’ll practice writing clean code that can be easily understood and reused by other data professionals.
What's included
8 videos4 readings3 quizzes5 ungraded labs
Here, you’ll learn how to use iterative statements, or loops, to automate repetitive tasks. You’ll also learn how to manipulate strings using slicing, indexing, and formatting.
What's included
9 videos5 readings4 quizzes7 ungraded labs
Now, you’ll explore fundamental data structures such as lists, tuples, dictionaries, sets, and arrays. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas.
What's included
17 videos12 readings5 quizzes9 ungraded labs
You will put everything you have learned about Python so far into practice with an end-of-course project. You will select a business problem from a list of options and use the given data to solve the problem. This project is an opportunity to demonstrate your skills and build a professional portfolio you can use to showcase your work to potential employers.
What's included
4 videos10 readings4 quizzes6 ungraded labs
Instructor
Instructor ratings
4.8 (353 ratings)Offered by
Recommended if you're interested in Data Analysis
Universidad Austral
Scrimba
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Frequently asked 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 and advanced data analytics are 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 and advanced data analysts 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.
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.