Ementorhub

What you'll learn

Understand the importance of data processing and manipulation in the data analysis pipeline.

Learn techniques to handle missing values and outliers, data reduction, and data scaling and discretization.

Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.

Skills you'll gain

Data Warehousing

Exploratory Data Analysis

Statistical Methods

Sampling (Statistics)

Data Transformation

Feature Engineering

Dimensionality Reduction

Data Processing

Data Cleansing

Data Manipulation

Pivot Tables And Charts

Anomaly Detection

Data Quality

There are 4 modules in this course

Learning Objectives: 1. Understand the importance of data processing and manipulation in the data analysis pipeline. 2. Learn techniques to handle missing values in datasets, including imputation and exclusion strategies. 3. Identify and detect outliers to assess their impact on data analysis and decision-making. 4. Explore sampling methods and dimension reduction techniques for large datasets and high-dimensional data. 5. Apply data scaling techniques to normalize and standardize variables for meaningful comparisons. 6. Utilize discretization to transform continuous data into categorical representations, simplifying analysis. 7. Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis. 8. Create pivot tables to summarize and reshape data, gaining valuable insights from complex datasets. Throughout the course, students will actively engage in practical exercises and projects, allowing them to apply data processing and manipulation techniques to real-world datasets. By the end of the course, participants will be well-equipped to effectively prepare, clean, and transform data for subsequent analysis tasks and data-driven decision-making.

Data Reduction

Scaling and Discretization

Data Warehouse