Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/venkat-0706/social-media-analysis

Clean & analyze social media data with Python. Explore trends, sentiments, & user behavior. Includes data cleaning, visualization, & insights.
https://github.com/venkat-0706/social-media-analysis

data-analysis data-visualization hashtag-trends natural-language-processing sentiment-analysis social-media-analysis text-mining user-engagement-metrics

Last synced: about 13 hours ago
JSON representation

Clean & analyze social media data with Python. Explore trends, sentiments, & user behavior. Includes data cleaning, visualization, & insights.

Awesome Lists containing this project

README 

        
# Coursera Project Network: Clean and analyze social media usage data with Python



# The Project Scenario:



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.



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 make 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 :



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.

# 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:



1.Exploratory Data Analysis with Python: https://www.kaggle.com/code/fazilbtopal/exploratory-data-analysis-with-python



2.A Simple Tutorial on Exploratory Data Analysis: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis



3.Detailed exploratory data analysis with python: https://www.kaggle.com/code/ekami66/detailed-exploratory-data-analysis-with-python/notebook



4.A Simple Tutorial on Exploratory Data Analysis Python · House Prices - Advanced Regression Techniques: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis



5.Exploratory Data Analysis with Python Python · 1985 Automobile Dataset: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis



# Check out these helpful resources:

1.Matplotlib tutorial: https://matplotlib.org/tutorials/introductory/pyplot.html



2.Seaborn examples: https://seaborn.pydata.org/examples/index.html



3.NumPy tutorial: https://numpy.org/doc/stable/user/quickstart.html



4.Pandas user guide: https://pandas.pydata.org/docs/user_guide/10min.html



# Creating an exceptional exploratory data analysis:



Data Understanding: The learner should demonstrate a deep understanding of the data and the problem being explored. The following should be included:



A clear description of the data, including its source and any relevant background information



A detailed exploration of the data, including summary statistics and visualizations



An explanation of any data cleaning or preprocessing techniques that were applied



Data Visualization: The learner should effectively use appropriate visualizations to explore the data and communicate insights. The following should be included:



Clear, well-designed visualizations that effectively communicate key insights



Appropriate visualizations for the type of data being analyzed



Thoughtful design choices, including appropriate labeling, color choices, and formatting



Analysis Techniques: The learner should use a variety of appropriate analysis techniques to explore the data and draw insights. The following should be included:



A clear explanation of the analysis techniques used and why they were chosen



Appropriate statistical tests, models, and algorithms to analyze the data



Thoughtful consideration of the limitations and assumptions of the analysis techniques used



Insights and Conclusions: The learner should draw insightful conclusions from the data and effectively communicate those conclusions. The following should be included:



Clear and well-supported conclusions that provide meaningful insights into the problem being explored



Appropriate recommendations or next steps based on the analysis



A clear explanation of any limitations or areas for further research