Data Visualization with Advanced Excel
This course will teach you how to use advanced Excel 2013 functions and PowerPivot to build databases and data models. You'll learn to perform different types of scenario and simulation analysis, and practice these skills with Excel's built-in tools. In the second half, you'll explore core principles of data visualization and dashboarding, and use Excel to create complex graphs and Power View reports. Note: PowerPivot is required to complete some exercises, and MS Excel 2013 is recommended. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
10th Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [April 24th, 2023]
PricewaterhouseCoopers LLP offers a course on Data Visualization with Advanced Excel. This course provides hands-on instruction of advanced Excel 2013 functions, such as PowerPivot to build databases and data models. Learners will also be able to practice their skills by leveraging some of Excel's built-in tools, such as solver, data tables, scenario manager, and goal seek. The second half of the course covers how to visualize data, tell a story, and explore data by reviewing core principles of data visualization and dashboarding. Learners will use Excel to build complex graphs and Power View reports, and then combine them into dynamic dashboards. It is important to note that learners will need PowerPivot to complete some of the exercises, and that MS Excel 2013 version is required.
[Applications]
Upon completion of this course, learners can apply the skills they have learned to create data models, perform scenario and simulation analysis, and visualize data in Excel. They can also use the knowledge they have gained to create dynamic dashboards and tell stories with data. Additionally, learners can use the course materials to help them understand the core principles of data visualization and dashboarding.
[Career Paths]
1. Business Intelligence Analyst: Business Intelligence Analysts are responsible for collecting, analyzing, and interpreting data to help inform business decisions. They use data visualization tools such as Excel to create reports and dashboards that help organizations understand their data and make better decisions. This role is becoming increasingly important as organizations look to leverage data to gain a competitive advantage.
2. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover patterns and trends. They use advanced statistical techniques and data visualization tools to identify correlations and insights that can be used to inform business decisions. This role is becoming increasingly important as organizations look to leverage data to gain a competitive advantage.
3. Data Visualization Designer: Data Visualization Designers are responsible for creating visual representations of data that are both aesthetically pleasing and informative. They use tools such as Excel to create charts, graphs, and other visualizations that help organizations understand their data and make better decisions. This role is becoming increasingly important as organizations look to leverage data to gain a competitive advantage.
4. Data Analyst: Data Analysts are responsible for collecting, analyzing, and interpreting data to help inform business decisions. They use data visualization tools such as Excel to create reports and dashboards that help organizations understand their data and make better decisions. This role is becoming increasingly important as organizations look to leverage data to gain a competitive advantage.
[Education Paths]
1. Bachelor of Science in Data Science: A Bachelor of Science in Data Science is a degree program that focuses on the analysis and interpretation of data. It combines elements of computer science, mathematics, and statistics to help students develop the skills needed to work with large datasets. This degree is becoming increasingly popular as businesses and organizations rely more and more on data-driven decision making.
2. Master of Science in Business Analytics: A Master of Science in Business Analytics is a degree program that focuses on the application of data science to business problems. It combines elements of business, economics, and computer science to help students develop the skills needed to analyze and interpret data in order to make informed decisions. This degree is becoming increasingly popular as businesses and organizations rely more and more on data-driven decision making.
3. Master of Science in Data Science: A Master of Science in Data Science is a degree program that focuses on the analysis and interpretation of data. It combines elements of computer science, mathematics, and statistics to help students develop the skills needed to work with large datasets. This degree is becoming increasingly popular as businesses and organizations rely more and more on data-driven decision making.
4. Doctor of Philosophy in Data Science: A Doctor of Philosophy in Data Science is a degree program that focuses on the analysis and interpretation of data. It combines elements of computer science, mathematics, and statistics to help students develop the skills needed to work with large datasets. This degree is becoming increasingly popular as businesses and organizations rely more and more on data-driven decision making. Additionally, this degree is becoming more sought after as the demand for data scientists continues to grow.
Course Syllabus
Preparing a Professional Excel
During this first week, you are going to learn about the development of data models and databases. We will cover the components of data sets and the relational database models, database keys, relationships, and joins. We will also look at a tool called PowerPivot that is used to import and prepare data to build relational models, as well as visualize data. By the end of the week, you will have a working knowledge of how to develop a data model.Advanced Scenario Analysis
Data Visualization
Be sure to complete lessons in the order in which they are sequenced in the course.Dashboarding
This week, we are going to explore three different analytical methods used to help model different scenarios and deal with variable uncertainty. These methods are scenario analysis, sensitivity analysis and simulation. We’ll look at what each method is and then go deeper into why and how you use each. Following some guided demonstration, you’ll be given a chance to practice in an Excel workbook and demonstrate what you’ve learned.Course Provider
Provider Coursera's Stats at AZClass
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