This course on Statistics for Data Science & Machine Learning aims to teach learners key statistical concepts essential for these fields. The course covers a wide range of topics including basics, categorical and numerical data, probability distribution, hypothesis testing, regression analysis, and chi-square tests. By the end of the course, students will have a solid understanding of various statistical formulas and their real-world applications. The teaching method involves explaining each formula thoroughly and solving practical problems to reinforce learning. This course is designed for individuals interested in data science, machine learning, and statistics, looking to enhance their analytical skills and knowledge in these areas.
Overview
Syllabus
Intro.
Basics.
Categorical Data.
Numerical Data.
Continuous Data.
Qualitative Data.
Quantitative Data.
Cross Table.
Pie Charts.
Bar Charts.
Pareto Charts.
Frequency Distribution Table.
Histograms.
Mean.
Median.
Mode.
Variance.
Standard Deviation.
Coefficient of Variation.
Covariance.
Correlation Coefficient.
Maximize Profit.
Probability Distribution.
Relative Frequency Histogram.
Normal Distribution.
Standard Normal Distribution.
Central Limit Theorem.
Standard Error.
Z Score.
Z Table.
Confidence Interval.
Alpha.
Margin of Error.
Confidence Interval Example.
Critical Probability.
Student's T Distribution.
Degrees of Freedom.
T Distribution Example.
T Table.
Dependent Samples.
Independent Samples.
Hypothesis.
Null Hypothesis.
Alternative Hypothesis.
Significance Level.
1 Sided Tests.
Type 1 Errors.
Type 2 Errors.
Hypothesis Error Example.
Means Testing.
P Value.
Regression.
Regression Example.
Correlation Coefficient.
Coefficient of Determination.
Root Mean Squared Deviation.
Residual.
Chi Square Tests.
Chi Square Table.
Taught by
Derek Banas
