Complete Google Analytics 4 Tutorial GA4 Course in Hindi - 2023 Umar Tazkeer

Course Overview

This Complete Google Analytics 4 Tutorial GA4 Course in Hindi - 2023 Umar Tazkeer provides an overview of the essential features of Google Analytics 4 (GA4). It covers the differences between the two products, installation of GA4 on a website, access to a Demo Account, Acquisition and Engagement Reports, Bounce Rate and Retention Report, GATag, User Report, connecting Google Ads to GA4, Events, Custom Events, Dimensions and Metrics, Reports, excluding IPs and Internal Traffic, Landing Page Report, Basic - Free Form Reports and Segments, Funnels, Debug View and Path Explore, Cohort Analysis, Automated Insights feature, Data Filters, Reporting Identity, Subdomain Tracking and Cross-Domain Configuration. Examples are provided of when to best use GA4 and the new features present with the new release.

[Applications]
Upon completion of this course, students should be able to apply their knowledge of Google Analytics 4 to their own websites. They should be able to install GA4, create custom events, use dimensions and metrics, create reports, use funnels, debug view and path explore, and use cohort analysis. They should also be able to give access in GA4, use automated insights, use data filters, use reporting identity, set up subdomain tracking, and configure cross-domain tracking.

[Career Paths]
1. Digital Analytics Manager: Digital analytics managers are responsible for overseeing the collection, analysis, and reporting of data from digital channels such as websites, mobile apps, and social media. They use this data to inform decisions about marketing campaigns, product development, and customer experience. Digital analytics managers must have a strong understanding of analytics tools and techniques, as well as the ability to interpret and communicate data to stakeholders.

2. Data Scientist: Data scientists use data to solve complex problems and develop insights. They use a variety of tools and techniques to analyze data, identify trends, and develop predictive models. Data scientists must have a strong understanding of mathematics, statistics, and computer science, as well as the ability to communicate their findings to stakeholders.

3. Data Analyst: Data analysts are responsible for collecting, organizing, and analyzing data. They use a variety of tools and techniques to identify trends, develop insights, and inform decisions. Data analysts must have a strong understanding of data analysis tools and techniques, as well as the ability to interpret and communicate data to stakeholders.

4. Business Intelligence Analyst: Business intelligence analysts are responsible for collecting, organizing, and analyzing data to inform decisions. They use a variety of tools and techniques to identify trends, develop insights, and inform decisions. Business intelligence analysts must have a strong understanding of data analysis tools and techniques, as well as the ability to interpret and communicate data to stakeholders.

[Education Paths]
1. Bachelor of Science in Analytics: This degree program focuses on the application of data analysis and predictive modeling to solve business problems. It covers topics such as data mining, machine learning, statistical analysis, and data visualization. Students learn to use software tools such as SAS, R, and Python to analyze data and develop predictive models. This degree is becoming increasingly popular as businesses look to leverage data to gain a competitive edge.

2. Master of Science in Business Analytics: This degree program focuses on the application of data analysis and predictive modeling to solve business problems. It covers topics such as data mining, machine learning, statistical analysis, and data visualization. Students learn to use software tools such as SAS, R, and Python to analyze data and develop predictive models. This degree is becoming increasingly popular as businesses look to leverage data to gain a competitive edge.

3. Master of Science in Data Science: This degree program focuses on the application of data analysis and predictive modeling to solve business problems. It covers topics such as data mining, machine learning, statistical analysis, and data visualization. Students learn to use software tools such as SAS, R, and Python to analyze data and develop predictive models. This degree is becoming increasingly popular as businesses look to leverage data to gain a competitive edge.

4. Master of Science in Artificial Intelligence: This degree program focuses on the application of artificial intelligence and machine learning to solve business problems. It covers topics such as natural language processing, computer vision, robotics, and deep learning. Students learn to use software tools such as TensorFlow, Keras, and PyTorch to develop AI models. This degree is becoming increasingly popular as businesses look to leverage AI to gain a competitive edge.