Unlocking Insights: Data Analysis of Google Analytics Users Data in Python

In this video we dive deep into the world of data analysis using Python and Google Analytics data. In this comprehensive analysis, we explore various facets of user data to draw meaningful conclusions that can supercharge your business strategies.

📈 Here's what we'll cover in this video:

User Growth Over Time: Discover how to track the growth in the number of users and new users over time, helping you spot critical periods of growth or decline in your audience.

Seasonal Patterns: Uncover hidden seasonal patterns or trends in user acquisition by examining user and new user counts on a monthly basis.

Company Performance: Learn how to assess the user and new user growth, retention, and churn rates of a specific company (e.g., "Company C") to make data-driven decisions.

City-Based Analysis: Explore user counts by city to identify areas with the highest and lowest user populations, providing valuable insights for marketing and expansion strategies.

Browser and Data Source Usage: Optimize your web development and marketing efforts by delving into which browsers and data sources are most commonly used by your users.

Top Cities for New Users: Tailor your marketing campaigns and resource allocation by identifying the top cities for new user acquisition.

Year-over-Year Comparisons: Highlight trends and changes in user behavior and market conditions by comparing user and new user counts and rates between different years.

Geographic Expansion Opportunities: If certain cities or regions exhibit high growth potential, learn how to strategize for expansion and maximize your growth opportunities.

Whether you're a marketer, business owner, or data enthusiast, this video will equip you with the knowledge and tools to harness the power of data analysis in your decision-making process. Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest content on data analytics and more!

Part 2 (Build Web App using Streamlit):

Github code:

Keywords:
#dataanalysis
#dataanalytics
#googleanalytics
#python
#datascience
#datacleaning
#modelbuilding
#datapreprocessing
#machinelearning
#datatransformation
#dataquality
#accuracy
#marketing
#campaigns
#datadrivendecisions