Business Analysis, Business Intelligence Analyst Course 2022

Explore contingency tables and two-way frequency tables to analyze the relationship between gender and handedness, and apply chi-square tests and p-values to assess independence.

Explore distribution concepts and measure distribution for a ten-row bank teller salary data set, covering mean, median, outliers, mode, minimum and maximum values, plus quintiles and quartiles.

Explore measures of variation, including range, interquartile range, variance, standard deviation, and standardization, to analyze how salaries differ by gender and test mean differences using a t-test.

Explore distribution visualizations by constructing buckets and histograms from salary data, introduce area line graphs and PDFs, and summarize distributions using mean, median, range, IQR, variance, and standard deviation.

Describe normal and continuous distributions, including density functions and CDFs, and demonstrate the 68-95-99.7 rule with real data on mean and standard deviation.

Explain kurtosis and positive or negative skewness, show how outliers affect mean and median, and why the median better represents central tendency in skewed distributions (NBA heights, baseball salaries).

Understand populations and inferences and how to select a sample using probability sampling. Learn simple random sampling, stratified sampling, and cluster sampling, and how strata and clusters affect accuracy.

Explore how bivariate data reveal relationships using scatter plots and correlation, preserve pairings to interpret positive and linear associations between variables, and handle missing values.

Explore uncertainty and entropy in data analysis, learn handling missing values via filtering, category creation, or imputation, and use information to reduce entropy and guide reporting.

Master the parts of an analytical report: introduction, data analysis, results, and conclusions, covering data preparation, abnormalities, methods, visuals, and actionable recommendations.

Explore automation in business analytics with macros and stored procedures that save time, enable reusable steps, and support a code base and logs for reliable, scalable analyses.

Learn to perform simple linear regression to predict Y from X, estimate the regression line by least squares, and evaluate fit with RMSE and R-squared, including significance and assumptions.

Examine the t distribution and its comparison with the normal curve, noting fatter tails for small samples and how it converges to normal as sample size grows.

Apply logistic regression to predict binary outcomes by modeling the log odds of an event, using the logistic function to bound probabilities between zero and one and interpret odds ratios.

Hypothesis testing, or significance testing, uses sample evidence to assess a population parameter, covering null and alternative hypotheses, critical region, alpha, and Z or T statistic to draw conclusions.

Explain type I (alpha) and type II (beta) errors and how sample variability affects the null and alternative hypotheses, including the power of a test and confidence intervals.

Discover how correlation measures the strength of linear and curvilinear relationships using variance, covariance, and the Pearson coefficient, and distinguish correlation from causation with scatter plots.

Explore discrete and continuous distributions, with emphasis on the binomial distribution. Learn how to model independent trials, define p, calculate mean, variance, and use normal approximation for large samples.

explains how sampling and the central limit theorem connect sample means to population parameters, covering populations, representative samples, sampling error, and implications for estimating television viewing proportions.

Define probability and explain complements, the addition rule, independence, and conditional probability, with Bayes’ theorem for estimating posterior probabilities from data.