Harvard University · edX
Exercising Leadership: Foundational Principles
4.71,590 ratingsMobilize people to tackle tough problems and build the capacity to thrive through the dangers of change
Overview
In this course, you'll learn to contextualize qualitative and quantitative data to improve business decisions. You'll explore data collection tools, compare data-driven and data-inspired approaches, and understand why analysis can sometimes fail. You'll examine performance metrics and use data visualization to communicate the story behind the numbers. You'll study dashboard types, design principles, and mathematical thinking strategies to spot patterns to solve problems. Finally, you'll practice selecting the right analytical tools for different datasets based on their characteristics.
By the end of this course, you will be able to:
• Discuss the importance and benefits of dashboards and reports to the data analyst with reference to Tableau and spreadsheets
• Explain the difference between quantitative and qualitative data including reference to their use and specific examples
• Compare and contrast data-driven decision making with data-inspired decision making
• Discuss the use of data in the decision-making process
• Differentiate between data and metrics, giving specific examples
• Demonstrate an understanding of what is involved in using a mathematical approach to analyze a problem
Syllabus 3
- Understand the power of data 35 minutes
Analysts contextualize individual data points and interpret them to inform business decisions. Qualitative and quantitative data are crucial elements of this process. You'll learn about data collection tools, how to compare data-driven and data-inspired decisions, and why data analysis can fail.
- Follow the evidence 55 minutes
Data visualization and metrics are widely utilized to convert raw data into useful information. You'll learn tools for visualizing data, the types of dashboards, and how metrics are used to measure performance.
- Connect the data dots 55 minutes
Mathematical thinking helps break down problems into smaller parts and identify the right tools for analysis, which often depend on dataset size. You'll also explore the characteristics, challenges, and benefits of big and small data.
Advantages and disadvantages
Advantages
- Structured courses with graded quizzes, assignments and deadlines you can reset.
- A shareable certificate from the university or company when you pay.
- Financial aid is often approved for students in India (apply 15 days before you need it).
- From Google, a well-regarded name.
- Self-paced: start any time.
- A clear syllabus (3 parts) you can see before you start.
Disadvantages
- Paid after a 7-day free trial (Coursera Plus or per course).
- Some courses can be audited for free, but graded work and certificates need payment.
Some points apply to every course of this kind; see how we rank.
This course is paid. Here is how to take it for free
- Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
- Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.
Taught by
- Google Career Certificates
Google is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (88/100). They build what they teach.
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