CRISP-ML(Q)-Data Pre-processing Using Python(2023)

سرفصل های دوره

Data Science - Data Pre-processing Using Python

1. Introduction

1. Introduction to Project Management Methodology CRISP ML(Q)

2. Agenda & Stages of Analytics

3. What is Diagnostic Analytics

4. What is Predictive Analytics

5. What is Prescriptive Analytics

6. What is CRISP ML (Q)

2. Business Understanding Phase

1. Business Understanding - Define the Scope of Application

2. Business Understanding - Define Success Criteria

3. Business Understanding - Use Cases

3. Data Understanding Phase Data Types

1. Agenda Data Understanding

2. Introduction to Data Understanding

3. Data types-continuous data (vs) Discrete data

4. Categorical Data Vs Count Data

5. Practical Data Understanding Using Realtime Example

6. Scale of Measurement

7. Quantitative (vs) Qualitative

8. Structured Vs Unstructured Data

9. Bigdata vs Not Big Data

10. Cross Sectional vs Time Series vs PanelLongitudinal Data

11. Balanced vs Imbalanced (or) Rare Events

12. Batch data(offline) vs Live streming data(Online)

4. Data Understanding Phase Data Collection

1. What is Data collection

2. Understanding Secondary Datasources

3. Understanding Primary Datasources

4. Understanding Data collection using survey

5. Understanding Data collection using DoE

6. Understanding Possible Errors in Data Collection Stage

7. Understanding Bias & Fairness

5. Understanding Basic Statistics

1. Introduction to CRISP ML(Q) Data Preparation & Agenda

2. What is Probability

3. What is Random Variables

4. Understanding Probability and its Application, Probability Distribution

5. What is Inferencial Statistics

6. Data Preparation Phase Exploratory Data Analysis (EDA)

1. Recap of Priliminaries Concepts

2. Understanding Normal Distribution

3. Understanding Standard Normal Distribution & Whats is Z Scores

4. Understanding Measures of central tendency ( First moment business decession )

5. Understanding Measures of Dispersion ( Second moment business decision)

6. Understanding Box Plot(Diff B-w Percentile and Quantile and Quartile)

7. Understanding Graphical Techniques-Q-Q-Plot

8. Understanding about Bivariate Scatter Plot

7. Python Installation & Set-up

1. Python Installation

2. Anakonda Installation

3. Understand about Anakonda Navigator & Spyder & Python Libraries

4. Understand about Jupyter & Google Colab

8. Data Preparation Phase EDA Using Python

1. Recap of Concepts until Phase-2

2. Understanding 1st & 2nd Moment Business Decision Using Python

3. Understanding 3rd Moment Business Decision Using Python

4. Understanding 4th Moment Business Decision Using Python

5. Understanding Unvariate (Bar Plot & Histogram) Using Python

6. Understanding Unvariate Plots Using Python

7. Understanding Unvariate Box Plot Using Python

8. Understanding Unvariate Q-Q-Plot Using Python

9. Understanding Bivariate Scatter Plot Using Python

9. Data Preparation Phase Data Cleansing- Type Casting

1. Recap of Concepts

2. Understanding Data Cleansing Typecasting

3. Understanding Data Cleansing Typecasting Using Python

10. Data Preparation Phase Data Cleansing- Handling Duplicates

1. Recap of Concepts

2. Understanding Handling Duplicates

3. Understanding Handling Duplicates Using Python

11. Data Preparation Phase Data Cleansing-Outlier Analysis Treatment

1. Understanding Outlier Analysis Treatment

2. Understanding Outlier Analysis Treatment Using Python

3. Understanding Winsorization Using Python

12. Data Preparation Phase Data Cleansing-Zero & Variance Features

1. Understanding Zero & Variance Features using Python

13. Data Preparation Phase Data Cleansing-Discretization Techniques

1. Understanding Discretization Techniques - Binarization & Rounding & Binning

14. Data Preparation Phase Data Cleansing-Dummy Variable Creation

1. Understanding Encoding Technique - Binary Encoding

2. Understanding Encoding Technique - Ordinal Encoding & Attribute Construction

3. Understanding Binarization & Discretization Using Python

4. Understanding Dummpy Variables Using Python

5. Understanding One Hot & Label Encoding Using Python

6. Understanding about Attribute Construction

15. Data Preparation Phase Data Cleansing-Missing Values

1. Understanding Missing Values Variants - MCAR, MAR, MNAR

2. Understanding Missing Values Imputation Technique - Deletion & Single Imputat

3. Understanding Missing Values Imputation Types Using Python

16. Data Preparation Phase Data Cleansing-Transformation

1. Understanding Log & Exponential Transformation, Normal Q-Q Plot Using Python

2. Understanding Power, Sqrt, Reciprocal Transformations

3. Understanding Box-Cox Transformations Using Python

4. Understanding Yeo -Johnson Transformations Using Python

17. Data Preparation Phase Data Cleansing-Standarzation

1. Understanding Data Preprocessing - Data Scaling Method

2. Understanding Normalization & Standardization & Q-Q Plot & Robust Scaling

3. Normalization & Standardization & Q-Q Plot & Robust Scaling Using Python

4. Understanding Feature Extraction & Feature Selection

5. What is AutoEDA and Understanding Sweetviz Using Python

6. Understanding Auto Clean Library Using Python

7. Understanding Autoviz, D-Tale, Pandas Profilling & Data Prep Using Python

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تولید کننده: Udemy-Training