Plan Szkolenia
What statistics can offer to Decision Makers
- Descriptive Statistics
- Basic statistics - which of the statistics (e.g. median, average, percentiles etc...) are more relevant to different distributions
- Graphs - significance of getting it right (e.g. how the way the graph is created reflects the decision)
- Variable types - what variables are easier to deal with
- Ceteris paribus, things are always in motion
- Third variable problem - how to find the real influencer
- Inferential Statistics
- Probability value - what is the meaning of P-value
- Repeated experiment - how to interpret repeated experiment results
- Data collection - you can minimize bias, but not get rid of it
- Understanding confidence level
Statistical Thinking
- Decision making with limited information
- how to check how much information is enough
- prioritizing goals based on probability and potential return (benefit/cost ratio ration, decision trees)
- How errors add up
- Butterfly effect
- Black swans
- What is Schrödinger's cat and what is Newton's Apple in business
- Cassandra Problem - how to measure a forecast if the course of action has changed
- Google Flu trends - how it went wrong
- How decisions make forecast outdated
- Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are usually more responsive
- How far a forecast should look into the past?
- Why more data can mean worse forecast?
Statistical Methods useful for Decision Makers
- Describing Bivariate Data
- Univariate data and bivariate data
- Probability
- why things differ each time we measure them?
- Normal Distributions and normally distributed errors
- Estimation
- Independent sources of information and degrees of freedom
- Logic of Hypothesis Testing
- What can be proven, and why it is always the opposite what we want (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
- Power
- How to determine a good (and cheap) sample size
- False positive and false negative and why it is always a trade-off
Wymagania
Good maths skills are required. Exposure to basic statistics (i.e. working with people who do the statistical analysis) is required.
Opinie uczestników (9)
Trenerka w jasny i usystematyzowany sposób prowadziła zajęcia, będąc przy tym bardzo elastyczną. Posiadała ogromną wiedzę, którą potrafiła się podzielić i cierpliwie tłumaczyła, jeśli pojawiły się jakieś wątpliwości albo kod nie działał. Mam nadzieję, że spotkamy się jeszcze na jakimś kursie :)
Anna - Uniwersytet Jagielloński Collegium Medicum
Szkolenie - Introduction to R
dużo ćwiczeń, które bezpośrednio mogę wykorzystać w mojej pracy
Alior Bank S.A.
Szkolenie - Sieci Neuronowe w R
Nie było nudno, trener potrafił utrzymać uwagę, tematy zostały omówione dogłębnie.
Marta - Ministerstwo Zdrowia
Szkolenie - Advanced R Programming
Przetłumaczone przez sztuczną inteligencję
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Szkolenie - Introduction to Data Visualization with Tidyverse and R
We were using road accident data for practicals
Maphahamiso Ralienyane - Road Safety Department
Szkolenie - Statistical Analysis using SPSS
Dobrze przemyślane i wysokiej jakości materiały planistyczne.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Szkolenie - Forecasting with R
Przetłumaczone przez sztuczną inteligencję
very tailored to needs
Yashan Wang
Szkolenie - Data Mining with R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Szkolenie - Programming with Big Data in R
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.