• What is Data?

• Multimedia

• Structured and Unstructured Data

• Data Types

• Data Visualization

  • What is Visualization?
  • Tufte's 6 Principle
  • Visualization Types
    • Line Plot
    • Scatter Plot
    • Bar Plot
    • Histogram
    • Pie Charts
    • Heatmap
    • Box Plot
    • Kartil Nedir? Nasıl Hesaplanır?
    • Joint Plot
    • KDE(Kernel Density Estimate)

• Statistics

  • Descriptive Statistics Concepts
  • The Concept of Skewness
  • Correlation and Correlation Matrix
  • The Simpsons Paradox
  • Anscombe Quartet
  • Data Distribution and Hypothesis Testing

• Data Distribution

  • Data and Distribution
  • Gaussian(Normal) Distribution
  • t-Distribution
  • Degrees of Freedom
  • Bernoulli's Distribution
  • Exponential Distribution

• Application

  • Pandas Revision
  • Introduction to Data Preprocessing with Pandas

• Hypothesis Tests

  • Basic Hypothesis testing
  • P value
  • T test
  • Z test
  • Chi-square (Chi-Square) Test
  • Errors in Hypothesis Testing

• Data Cleaning

  • The 68-95-99.7 Rule and 3 Sigma
  • Outlier, Missing and Duplicate Data and their Detection
  • Z-Score
  • Handling missing values
  • Null vs NaN
  • Pandas Functions for missing values
  • Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • Collinearity (Multiple Linear Connection

• Data Transformation

  • Data Conversion Techniques
    • round
    • Scaling
    • Label Encoding
    • One Hot Encoding
    • Stack
    • melt
    • Shorts
    • Feature Engineering

• Data Augmentation

  • Aggregation Functions

• Application

  • Data Visualization with Seaborn
  • Data Preprocessing with Pandas

• ML Review

  • What is Machine Learning?
  • Supervised Learning
  • Unsupervised Learning
  • Errors That May Be Encountered in Model Training
  • Tools Used in Data Analysis and Machine Learning
  • End-to-End Machine Learning Project Steps

• Application

  • Training An End-to-End ML Model with a Real Dataset

The course completion is certified.