Last Update: October, 2018
1. Course Objective
Learn Business Statistics main topics using Python programming language® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.
2. Skills Learned
At the end of this course you will know how to:
- Chart frequency histograms (absolute frequency, relative frequency, cumulative absolute frequency, cumulative relative frequency).
- Estimate central tendency measures (sample mean and median), dispersion measures (sample standard deviation, variance and mean absolute deviation), frequency distribution measures (sample skewness and kurtosis) and association measures (samples correlation and covariance).
- Define probability distributions (normal, standard normal, Student’s t) and evaluate probability distribution goodness of fit (Kolmogorov-Smirnov test, Anderson-Darling test).
- Approximate point estimations, confidence intervals (population mean, population proportion, bootstrap population mean) and calculate sample size for specific margin of error (population mean).
- Estimate statistical inference tests probability values (population mean two tails and right tail tests, population proportion left tail test, paired population means two tails test).
- Assess statistical inference test power for several levels of statistical significance (population mean two tails test).
3. Typical Student
This course is ideal for you as:
- Undergraduate or postgraduate who wants to learn about the subject.
- Academic researcher who wishes to deepen your knowledge in applied statistics or quantitative finance.
- Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.