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Data Analysis and Model Training Course by Global AI Hub
Syllabus:
Day 1
-
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
Day 2
-
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 Conversion Techniques
-
Data Augmentation
- Aggregation Functions
-
Application
- Data Visualization with Seaborn
- Data Preprocessing with Pandas
Day 3
-
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
Certification
The course completion is certified.