Master Descriptive Statistics, Data Visualization, Probability, and Hypothesis Testing from Scratch using Python
Sub Category
- Business Analytics & Intelligence
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Objectives
- Calculate and interpret key descriptive statistics (mean, median, standard deviation) for data summaries
- Apply probability rules and Bayes’ Theorem to solve conditional probability problems
- Analyse and summarise datasets using Python to compute statistics and create data visualisations
- Formulate null/alternative hypotheses and conduct one-sample Z and T-tests for population means
- Apply descriptive statistics (mean, median, mode, standard deviation) to summarize any dataset.
- Calculate and interpret conditional probability and apply the powerful Bayes' Theorem to real-world problems.
- Model real-world scenarios using key probability distributions (Binomial, Poisson, Normal).
- Understand and explain the core concepts of statistical inference and the Central Limit Theorem.
- Perform hypothesis testing (like T-tests) in Python to make data-driven decisions and validate results.
Pre Requisites
- No prior statistics or advanced programming experience is required; we start from the basics!
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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