Name: Data Science: Probability and Statistics
Availability: InStock

What You'll Learn

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.

Requirements

No prior statistics or advanced programming experience is required; we start from the basics!

Beginners in Data Science or Machine Learning who need to build a strong, foundational understanding of Probability and Statistics
Analysts or researchers who want to start using Python for reliable data exploration and hypothesis testing
Students looking for a comprehensive and foundational course in statistical methods and inference
Aspiring Data Scientists, Data Analysts, and Business Intelligence (BI) Professionals.
Python developers who want to add statistical skills to their toolkit for Machine Learning.
Business Analysts who want to move beyond basic Excel analysis and make reliable data inferences.
Students or professionals from any field (finance, marketing, engineering) who need to work with data and validate decisions.
Anyone who is curious about statistics but finds traditional textbooks boring and impractical.

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