Coursera · IBM — Murtaza Haider
Statistics for Data Science with Python
Compare IBM’s Python statistics course for descriptive analysis, visualization, hypothesis tests and regression, followed by a housing-data project.
Explore linear algebra, discrete mathematics, and statistics resources, with advice on prerequisites, problem solving, and explaining each reasoning step.
Mathematics develops through solving problems and explaining why an approach works. Different branches support different goals: linear algebra connects with data and systems, discrete mathematics with computer science, and statistics with uncertainty. Choose a route that matches the reasoning you need.
Check algebra and calculus prerequisites before starting. A lecture can make a solution appear clear even when reproducing it independently is difficult. Favor material with exercises, worked explanations, and opportunities to examine the assumptions behind a theorem or statistical result.
Attempt a problem before reading the solution, identify the first step you cannot justify, and review that idea directly. Explain the result in words as well as symbols. Returning to a prerequisite is often the fastest way forward.
Coursera · IBM — Murtaza Haider
Compare IBM’s Python statistics course for descriptive analysis, visualization, hypothesis tests and regression, followed by a housing-data project.
Coursera · University of London — George Kapetanios
Review London’s business-statistics course for graphs, numerical summaries, probability distributions and estimation in an international-business setting.