MISSION 310

Guided Project: Finding the Best Markets to Advertise In

In this Statistics Intermediate course, we've learned the following:

  • How to summarize distributions using the mean, the median, and the mode.
  • How to measure the variability of a distribution using the range, the mean absolute deviation, the variance, and the standard deviation.
  • How to locate any value in a distribution using z-scores.

To make learning smoother and more efficient, we learned about each of these topics in isolation. In this guided project, we go one step further and learn to combine some of these skills to perform practical data analysis. We'll also make use of what we learned in the Statistics Fundamentals course.

In this guided project, we'll work as a data scientist for an e-learning company that offers courses on programming. Most of our courses focus on web and mobile development, but we also cover many other domains, like data science and game development. We want to promote our product and we'd like to invest some money in advertising. Our goal in this project is to determine the best two markets in which to advertise our product.

As with all guided projects, we encourage you to experiment and extend your project, taking it in unique directions to make it a more compelling addition to your portfolio!

Objectives

  • Learn to combine the statistical skills learned in a practical settings.
  • Learn how you can add business value using the statistical skills you learned.
  • How to insert and update data in database tables.

Mission Outline

1. Finding the Best Two Markets to Advertise In
2. Understanding the Data
3. Checking for Sample Representativity
4. New Coders - Locations and Densities
5. Spending Money for Learning
6. Dealing with Extreme Outliers
7. Choosing the Two Best Markets
8. Next steps

statistics-intermediate

Course Info:

Intermediate

The median completion time for this course is 7.4 hours. View Details

This course requires a basic subscription. It includes five missions, and one guided project. It is the 16th course in the Data Analyst in Python and Data Scientist in Python path.

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