Clean and analyze social media usage data with Python

Learning Objectives

• Increase client reach and engagement

• Gain valuable insights that will help improve social media performance

• Achieve their social media goals and provide data-driven recommendations

Before you begin, let's take a quick look at what you'll accomplish in this experience, the steps you'll take to complete it, and what makes it different from other Coursera experiences.

Overview

Estimated Time

• Learners typically set aside 6-8 hours to complete this project

Skills You'll Showcase

• Coding, cleaning, analyzing, and visualizing data using python

• Producing an end-to-end Exploratory Data Analysis using Python and a Jupyter Notebook

• Producing data-driven insights to answer a key business question 

What You'll Create

By the end of this project, you'll have visualized data, including graphs and charts, to upload along with a summary of your findings. We'll take these outputs and help you create a detailed project overview, like the one shown below, to share with potential employers.

How to Complete this Experience

You’ll complete the following steps to create a project that you can showcase to employers. You can access all steps via the course overview or using the navigation panel on the left side of your screen. 

Step 1: Project Scenario


Learn about the real-world challenge you’re going to solve

Step 2: Build Your Project

Get hints and helpful resources as you work to achieve the project objectives

Step 3: Sharing with Employers

See how Coursera helps you showcase your work to employers

Step 4: Add Your Project

Present your unique solution and approach to achieving the project objectives

What Makes This Project Different?

We’ll provide you with a rich case study to solve and break it down into high-level tasks to guide you as you go. However, you'll be responsible for deciding how to execute these high-level tasks on your own.

There's no one right answer for the real-world case study you'll be given. This is great since it'll make your final project unique from other learners. To help you develop this unique solution, you should expect to use the internet, Coursera, and other resources (which we've supplied) when you encounter a problem that you cannot easily solve.

So, now that you know what this experience is all about, let's explore your case study. 

In this project, you will step into the shoes of an entry-level data analyst at a social media agency, helping to create a comprehensive report that analyzes the performance of different categories of social media posts.

Project Scenario

Suppose you work for a social media marketing company that specializes in promoting brands and products on a popular social media platform. Your team is responsible for analyzing the performance of different types of posts based on categories, such as health, family, food, etc. to help clients optimize their social media strategy and increase their reach and engagement. 

They want you to use Python to automatically extract tweets posted from one or more categories, and to clean, analyze and visualize the data. The team will use your analysis to  making data-driven recommendations to clients to improve their social media performance. This feature will help the marketing agency deliver tweets on time, within budget, and gain fast results.

Project Objectives

1. Increase client reach and engagement

2. Gain valuable insights that will help improve social media performance

3. Achieve their social media goals and provide data-driven recommendations

Your Challenge

Your task will be taking on the role of a social media analyst responsible for collecting, cleaning, and analyzing data on a client's social media posts. You will also be responsible for communicating the insights and making data-driven recommendations to clients to improve their social media performance.  To do this, you will  set up the environment, identify the categories for the post (fitness, tech, family, beauty, etc)  process, analyze, and visualize data.

In this project, we'll use data from Twitter; however, to keep this project unique and open-ended, please feel free to choose whichever major social media website you'd prefer.

After you perform your analysis, you will share your findings.

See Example Projects

Before you dive into cleaning and analyzing tweets of your own exploratory data, you may want to see an example of what you'll be making.

To make sure learners create unique analyses, we won't show an example of cleaning and analyzing tweets in an exploratory data analysis. Instead, we've included some examples of cleaning, analyzing, and visualizing other data sets as well. Though their business contexts are different, you can get inspiration from seeing how they explored their datasets and made their recommendations.

Example 1: Predicting the Price of a Home

This analysis conducted using python explores the factors that contribute to the selling price of a home in Ames, Iowa. You might even be interested to see a tutorial on how to conduct an analysis on such a dataset.

Example 2: Predicting the Price of a Car

This analysis was conducted using a dataset from 1985 to determine the factors that contribute to the price of a car.

Example 3: A Sample Visualization

The file below is an example of a visualization taken from an exploratory clean and analyzing tweets of the same media marketing agency. Remember: your project doesn’t have to include this visualization! This is just to get you thinking. Your analysis should be unique to your vision.

Choose Your Platform

Working on Coursera's platform (No Setup Required!)

To complete this project, we've prepared a Jupyter Notebook on Coursera's cloud workspace for you to use.  

There's no setup needed! All the necessary files are already included and set up in the workspace. All you have to do is continue to the next reading item (Option A: Using Coursera's Jupyter Notebook) to find the link to the in-browser Jupyter Notebook environment.

If you'd like to complete this project off-platform:

If you'd like to set up and use your own Jupyter Notebook outside of the Coursera Platform, then skip the next reading item and go to Option B. Working Off Platform for more information.