Welcome to Bussma
All you need is an idea. We handle the rest.
All you need is an idea. We handle the rest.
This comprehensive course provides a solid foundation in probability, statistics, and data analysis, with hands-on applications in Python. Designed for aspiring data scientists and analysts, it integrates theoretical understanding with practical skills through real-world datasets and industry-relevant case studies. The course covers essential statistical concepts such as exploratory data analysis (EDA), hypothesis testing, probability distributions, regression analysis, multivariate statistics, and experimental design. Learners gain proficiency in Python libraries including NumPy, Pandas, SciPy, Matplotlib, Seaborn, and Scikit-learn. By the end of the course, students are able to: Clean and explore data using statistical and visual techniques Apply statistical tests and interpret their results Build and evaluate regression and classification models Design A/B tests and perform time series analysis Simulate probability distributions and apply the Central Limit Theorem Complete end-to-end data science projects using real-world datasets This course emphasizes both the theoretical underpinnings and the practical implementation of statistical methods used in modern data science.
Esraa Elsayed
Data Engineer
This comprehensive course provides a solid foundation in probability, statistics, and data analysis, with hands-on applications in Python. Designed for aspiring data scientists and analysts, it integrates theoretical understanding with practical skills through real-world datasets and industry-relevant case studies. The course covers essential statistical concepts such as exploratory data analysis (EDA), hypothesis testing, probability distributions, regression analysis, multivariate statistics, and experimental design. Learners gain proficiency in Python libraries including NumPy, Pandas, SciPy, Matplotlib, Seaborn, and Scikit-learn. By the end of the course, students are able to: Clean and explore data using statistical and visual techniques Apply statistical tests and interpret their results Build and evaluate regression and classification models Design A/B tests and perform time series analysis Simulate probability distributions and apply the Central Limit Theorem Complete end-to-end data science projects using real-world datasets This course emphasizes both the theoretical underpinnings and the practical implementation of statistical methods used in modern data science.
Lessons
14
Duration
7 Hours
Skill Level
Entry level
Views
10
Data Engineer
I’m Esraa Elsayed Mostafa, a Data Engineer and Teaching Assistant with a Bachelor’s degree in Data Science from Alexandria University and ongoing Master’s studies at Ain Shams University. I specialize in Python programming, AI, and data analysis, and have taught subjects like databases, deep learning, and intelligent programming at multiple universities.
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