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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Project Management Methodology CRISP ML(Q)

Lecture 2: Agenda & Stages of Analytics

Lecture 3: What is Diagnostic Analytics ?

Lecture 4: What is Predictive Analytics ?

Lecture 5: What is Prescriptive Analytics ?

Lecture 6: What is CRISP ML (Q) ?

Chapter 2: Business Understanding Phase

Lecture 1: Business Understanding – Define the Scope of Application

Lecture 2: Business Understanding – Define Success Criteria

Lecture 3: Business Understanding – Use Cases

Chapter 3: Data Understanding Phase | Data Types

Lecture 1: Agenda Data Understanding

Lecture 2: Introduction to Data Understanding

Lecture 3: Data types-continuous data (vs) Discrete data.mp4

Lecture 4: Categorical Data Vs Count Data

Lecture 5: Practical Data Understanding Using Realtime Example

Lecture 6: Scale of Measurement

Lecture 7: Quantitative (vs) Qualitative

Lecture 8: Structured Vs Unstructured Data

Lecture 9: Bigdata vs Not Big Data

Lecture 10: Cross Sectional vs Time Series vs Panel/Longitudinal Data

Lecture 11: Balanced vs Imbalanced (or) Rare Events

Lecture 12: Batch data(offline) vs Live streming data(Online)

Chapter 4: Data Understanding Phase | Data Collection

Lecture 1: What is Data collection ?

Lecture 2: Understanding Secondary Datasources

Lecture 3: Understanding Primary Datasources

Lecture 4: Understanding Data collection using survey

Lecture 5: Understanding Data collection using DoE

Lecture 6: Understanding Possible Errors in Data Collection Stage

Lecture 7: Understanding Bias & Fairness

Chapter 5: Understanding Basic Statistics

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

Lecture 2: What is Probability ?

Lecture 3: What is Random Variables ?

Lecture 4: Understanding Probability and its Application, Probability Distribution

Lecture 5: What is Inferencial Statistics ?

Chapter 6: Data Preparation Phase | Exploratory Data Analysis (EDA)

Lecture 1: Recap of Priliminaries Concepts

Lecture 2: Understanding Normal Distribution

Lecture 3: Understanding Standard Normal Distribution & Whats is Z Scores

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

Lecture 5: Understanding Measures of Dispersion ( Second moment business decision)

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

Lecture 7: Understanding Graphical Techniques-Q-Q-Plot

Lecture 8: Understanding about Bivariate Scatter Plot

Chapter 7: Python Installation & Set-up

Lecture 1: Python Installation

Lecture 2: Anakonda Installation

Lecture 3: Understand about Anakonda Navigator & Spyder & Python Libraries

Lecture 4: Understand about Jupyter & Google Colab

Chapter 8: Data Preparation Phase | EDA Using Python

Lecture 1: Recap of Concepts until Phase-2

Lecture 2: Understanding 1st & 2nd Moment Business Decision Using Python

Lecture 3: Understanding 3rd Moment Business Decision Using Python

Lecture 4: Understanding 4th Moment Business Decision Using Python

Lecture 5: Understanding Unvariate (Bar Plot & Histogram) Using Python

Lecture 6: Understanding Unvariate Plots Using Python

Lecture 7: Understanding Unvariate Box Plot Using Python

Lecture 8: Understanding Unvariate Q-Q-Plot Using Python ?

Lecture 9: Understanding Bivariate Scatter Plot Using Python

Chapter 9: Data Preparation Phase | Data Cleansing- Type Casting

Lecture 1: Recap of Concepts

Lecture 2: Understanding Data Cleansing Typecasting

Lecture 3: Understanding Data Cleansing Typecasting Using Python

Chapter 10: Data Preparation Phase | Data Cleansing- Handling Duplicates

Lecture 1: Recap of Concepts

Lecture 2: Understanding Handling Duplicates

Lecture 3: Understanding Handling Duplicates Using Python

Chapter 11: Data Preparation Phase | Data Cleansing-Outlier Analysis Treatment

Lecture 1: Understanding Outlier Analysis Treatment

Lecture 2: Understanding Outlier Analysis Treatment Using Python

Lecture 3: Understanding Winsorization Using Python

Chapter 12: Data Preparation Phase | Data Cleansing-Zero & Variance Features

Lecture 1: Understanding Zero & Variance Features using Python

Chapter 13: Data Preparation Phase | Data Cleansing-Discretization Techniques

Lecture 1: Understanding Discretization Techniques – Binarization & Rounding & Binning

Chapter 14: Data Preparation Phase | Data Cleansing-Dummy Variable Creation

Lecture 1: Understanding Encoding Technique – Binary Encoding

Lecture 2: Understanding Encoding Technique – Ordinal Encoding & Attribute Construction

Lecture 3: Understanding Binarization & Discretization Using Python

Lecture 4: Understanding Dummpy Variables Using Python

Lecture 5: Understanding One Hot & Label Encoding Using Python

Lecture 6: Understanding about Attribute Construction

Chapter 15: Data Preparation Phase | Data Cleansing-Missing Values

Lecture 1: Understanding Missing Values Variants – MCAR, MAR, MNAR

Lecture 2: Understanding Missing Values Imputation Technique – Deletion & Single Imputat

Lecture 3: Understanding Missing Values Imputation Types Using Python

Chapter 16: Data Preparation Phase | Data Cleansing-Transformation

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

Lecture 2: Understanding Power, Sqrt, Reciprocal Transformations

Lecture 3: Understanding Box-Cox Transformations Using Python

Lecture 4: Understanding Yeo -Johnson Transformations Using Python

Chapter 17: Data Preparation Phase | Data Cleansing-Standarzation

Lecture 1: Understanding Data Preprocessing – Data Scaling Method

Lecture 2: Understanding Normalization & Standardization & Q-Q Plot & Robust Scaling

Lecture 3: Normalization & Standardization & Q-Q Plot & Robust Scaling Using Python

Lecture 4: Understanding Feature Extraction & Feature Selection

Lecture 5: What is AutoEDA ? and Understanding Sweetviz Using Python