Overview
The Data Mining fundamentals series is jam-packed with all the background information, technical terminology, and basic knowledge that you will need to hit the ground running.
In this course, you will:
- What data is, the basic vocabulary and data attributes
- Data quality, data noise, missing values and data preprocessing, known as data cleaning
- Different strategies used in data mining such as data aggregation and data sampling
- Feature selection
- Various evaluation measures
- Data Visualization using various plots
Course Contents
1. Fundamentals of Data Mining
- Basic Vocabulary
- Data Attributes
- Data Attributes (Cont.)
- Basic Data Types
2. Datasets for Data Mining
- Transaction Data & Document Data
- Ordered Data & Graph Data
3. Data Issues in Data Mining
- Data Quality
- Data Noise
4. Data Preprocessing & Transformation
- Missing Values and Duplicated Data
- Data Cleaning
- Data Aggregation
- Data Sampling
- Data Sampling Types
- Dimensionality Reduction
- Data Transformation
- Feature Selection
5. Evaluation Measures
- Similarity and Dissimilarity
- Euclidean Distance & Cosine Similarity
- Evaluating Correlation
- Center and Spread Measurement
- Summary Statistics
6. Data Visualization & Exploration
- Data Visualization & Exploration
- Histograms & Box Plots
- Scatter Plots & Contour Plots
