Curated learning path for Statistical Hypothesis Testing. Build practical skills through expert-selected courses.
Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Follow these courses in order to complete the learning path. Click on any course to enroll.
This Linked In Learning course provides a practical introduction to hypothesis testing for data science. You will learn about the different types of hypothesis tests and how to apply them to real-world data. The course includes hands-on exercises using Python.
This Udacity course, developed by Google, provides a practical introduction to A/B testing. You will learn how to design and analyze A/B tests. The course covers topics such as metrics, sample size, and statistical significance.
This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability. We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work. Extensive coverage of statistics in detail: The measure of Central Tendency (Mean Median and Mode) The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation) Regression and Advanced regression in details with Hypothesis understanding (P-value) Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examples Detailed understanding of Normal Distribution and its properties Symmetric Distribution, Skewness, Kurtosis, and KDE. Probability and its in-depth knowledge Permutations and Combinations Combinatorics and Probability Understanding of Random Variables Various distributions like Binomial, Bernoulli, Geometric, and Poisson Sampling distributions and Central Limit Theorem Confidence Interval Margin of ErrorT-statistic and F-statistic Significance tests in detail with various examples Type 1 and Type 2 Errors Chi-Square Test ANOVA and F-statistic By completing this course we are sure you will be very much proficient in Statistics and able to talk to anyone about stats with confidence apply the knowledge in your day to day work.
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