Python for Data Science

The importance of statistics in data analytics and data science cannot be over emphasized. Join me in this free tutorial as i introduce you to statistics for data science. You will learn key statistic skills required for your data science job. Other modules of this course are building a machine model, data collection, data preprocessing, model training and testing, result interpretation and deployment of models. 

Contents for this module:

– Random variables
– Probability distribution functions (PDFs)
– Mean, Variance, Standard Deviation
– Covariance and Correlation
– Bayes Theorem
– Linear Regression and Ordinary Least Squares (OLS)
– Gauss-Markov Theorem
– Parameter properties (Bias, Consistency, Efficiency)
– Confidence intervals
– Hypothesis testing
– Statistical significance
– Type I & Type II Errors
– Statistical tests (Student’s t-test, F-test)
– p-value and its limitations
– Inferential Statistics
– Central Limit Theorem & Law of Large Numbers
– Dimensionality reduction techniques (PCA, FA)

Joshua Sopuru (Bsc. Mathematics and computer Science, MBA | PhD. Management Information Systems)

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