Applied Statistics

Eğitmen: Dr. H. Sait Ölmez

Eğitimin İçeriği:

Descriptive Statistics

  • Introduction
    • Major types and characteristics of data
    • Experiment design and setup: Identifying the target and predictor variables; Collecting and preparing data for analysis
    • Exploratory Data Analysis (EDA): Visualization techniques for summarizing and displaying
  • Univariate Analysis of Data
    • Tabular presentation of data: Histograms (frequency distribution)
    • Common shapes of distributions and skewness
    • Measure of central tendency in data (mean, median, mode, range)
    • Measure of spread (variability) in data (variance and standard deviation)
    • Quantile-Quantile plots
  • Bivariate Analysis of Data from a visualization perspective
    • Relations between any combinations of categorical and continuous variables
  • A primer on Probability
    • Basic rules of probability
    • Conditional probability (Bayes’ rule)
    • Discrete probability distributions
    • Continuous probability distributions
    • Properties of a Normal Distribution
    • Standard Normal Distribution and its applications

Inferential Statistics

  • Point/Interval Estimators
    • Basic terminology in sampling process
    • Description and calculation of point estimators
    • The use and importance of interval estimators
    • Forming and interpreting the uncertainty around a point estimation using confidence intervals
  • Hypothesis Testing
    • Forming a proper hypothesis
    • Critical components of a hypothesis test: Significance level and the p-value
    • 1-sample z-test vs 1-sample t-test
    • 2-sample t-tests
    • Types of errors (Type I and Type II)
    • Power of a hypothesis test and the factors that affect power
    • Problems with hypothesis tests and why the p-value isn’t enough
    • Effect size and its importance from a practical standpoint
  • Statistical Tests
    • A summary on statistical tests and which one to use when
    • Pearson’s correlation
    • Spearman’s rank-order correlation
    • Point-Biserial correlation
    • Chi-square test
    • One-way ANOVA test
  • Simple/Multiple Linear Regression
    • Simple Linear Regression
    • Ordinary Least Squares solution
    • Multiple Linear Regression
    • Interpretation of regression coefficients
    • Assessing the overall quality of a regression
    • Assumptions of a Linear Regression solution and how to check them
    • Outliers in Linear Regression and interpretation of Cook’s distance
    • Converting nominal predictors to numerical values and the dummy variable trap
    • Model selection process
    • Regularization in Linear Regression
      • What is regularization and why do we need it?
      • Ridge and Lasso regularization

Eğitimin Hedef Kitlesi: Kurum içinde “Veri Analisti” ve/veya “Veri Bilimci” olarak çalışanlar.

Eğitimin Seviyesi: Başlangıç-Orta

Eğitim Süresi: 5 gün

Ek Bilgiler: Katılımcıların Python programlama dilini pratik seviyede kullanabildikleri varsayılmaktadır.