Tips for Writing an Impactful HR Resume
August 19, 2024
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This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
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(752 reviews)
Apply the exploratory data analysis (EDA) process
Explore the benefits of structuring and cleaning data
Investigate raw data using Python
Create data visualizations using Tableau
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20 quizzes, 1 assignment
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This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you build your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Use Python tools to examine raw data structure and format -Select relevant Python libraries to clean raw data -Demonstrate how to transform categorical data into numerical data with Python -Utilize input validation skills to validate a dataset with Python -Identify techniques for creating accessible data visualizations with Tableau -Determine decisions about missing data and outliers -Structure and organize data by manipulating date strings
You’ll learn how to find the stories within data and share them with your audience. You’ll learn about the methods and benefits of data cleaning and how it can help you discover those stories. You’ll also go over the steps of the EDA process and learn how EDA can help you quickly understand data. Finally, you'll explore different ways to visualize data to communicate key insights.
8 videos5 readings2 quizzes1 assignment2 plugins
Finding stories in data using EDA is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. You’ll learn how to use Python to perform the EDA practices of discovering and sculpting.
9 videos6 readings4 quizzes7 ungraded labs2 plugins
You’ll explore three more EDA practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data.
11 videos6 readings5 quizzes5 ungraded labs2 plugins
You’ll practice creating and presenting data stories in an ethical, accessible, and professional way. You'll also explore advanced data visualization techniques in Tableau.
8 videos11 readings5 quizzes2 plugins
In this end-of-course project, you’ll practice using Python to perform EDA on a workplace scenario dataset. Then, you'll use Python and Tableau to visualize the data.
4 videos10 readings4 quizzes6 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on Feb 11, 2024
Very well designed course for anyone having experience of any field willing to dive into data analytics.
Reviewed on Aug 27, 2024
Nice to learn about tools for analyzing data in python! It made my world of data analytics much bigger
Reviewed on Nov 1, 2023
The course is tough, thus it makes me think deeper into each questions in the lab
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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 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.
The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.
During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.
This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders.
The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.
You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.
We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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