This comprehensive game data analytics training covers essential topics crucial in the industry, including SQL best practices, mobile game analytics fundamentals, and key metrics such as cohort analysis, retention rates, and monetization strategies. Participants delve into user acquisition metrics and the concept of A/B testing, gaining practical insights into optimizing marketing strategies and enhancing player experiences. Through hands-on projects utilizing tools like Postgres, BigQuery, Tableau, and Looker, students develop proficiency in event design, data pipelines, and player data analysis, preparing them for real-world challenges. Project assignments and technical interviews simulate authentic industry scenarios, ensuring graduates are well-equipped to deliver impactful insights and drive success in the gaming industry.

Bootcamp Subjects: • Excel Analytics• MS SQL Query• Data Analysis and Data Visualization with Python• Decision Making with Statistics and Analytical Methods• CRM Analytics• Data Analysis and Data Visualization with Power BI• Introduction to Marketing Engineering• Advanced Marketing Engineering• Industry 4.0 and IOT Analytics• HR Analytics• Brand Management with Big Data and Artificial Intelligence TechnologiesProjects: • Rule-Based Classification • RFM Analysis • Customer Profile Analysis • Customer Segmentation • CLTV Analysis • Startup Growth Metrics Analysis • Product Rating / Ranking • A/B Test • Market Basket Analysis and Product Recommendation • Multiple Regression Analysis • NPS Analysis • Predictive Maintenance Analysis • Product Quality Development and Control • Customer Satisfaction Analysis • Customer Analysis Lost Sales Forecasting • Employee Leave/Absence Analysis • Workforce Turnover Analysis (Turn Over Rate)• Final Project (Data analysis of hotel booking data, Ruled base classification & Hotel price prediction)

💻 Introduction to PythonPython syntax essentials Flow controls Functions Data structures Files and errors 💻 Introduction to Python: The Road to Machine LearningObject-oriented programming Preparing data Processing data Working with datasets Data visualization

- Python Programming for Data Science- Data Analysis with Python- CRM Analytics (Introduction to CRM Analytics, RFM Analysis, Customer Lifetime Value, Customer Lifetime Value Estimation)- Measurement Problems (Product Rating, Product Ranking, Comment Ranking, A/B Testing)- Recommendation Systems (Association Rule Learning, Content Filtering, Memory-Based Collaborative Filtering, Model-Based Collaborative Filtering (Matrix Factorization))- Feature Engineering (Outliers, Missing Values, Encoding & Scaling, Feature Extraction)- Machine Learning (Linear Regression, Logistic Regression, KNN, CART, Unsupervised Learning)- Time Series (Statistical and Econometric Time Series, Time Series Modeling with Machine Learning)- SQL

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Ensuring communication between software department and other departments, Jira management, testing of improvements, evaluating in terms of user experience, manual testing of existing projects, creating Jira issues according to test results, assigning to related person, monitoring to progress of new and old projects.#Manueltest #Jira #Teamwork #Customercentric