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Statistics for Data Science & Machine Learning

Derek Banas via YouTube

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Overview

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Dive into a comprehensive 48-minute video tutorial covering essential statistics concepts for data science and machine learning. Learn about categorical and numerical data, data visualization techniques, measures of central tendency, variability, probability distributions, hypothesis testing, regression analysis, and more. Gain practical insights through real-world problem-solving examples and explore key statistical formulas applicable to data science and machine learning tasks. Perfect for aspiring data scientists and machine learning enthusiasts looking to strengthen their statistical foundation.

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

Reviews

4.0 rating, based on 1 Class Central review

Start your review of Statistics for Data Science & Machine Learning

  • Profile image for Subham Dey
    Subham Dey
    1
    The way the creator has explained the topic was good , but he could have made it more descriptive and could have included more mathematical based examples . With solved examples especially the part where there is regression must include math examples - bcuz what is data science without maths
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