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

CRISP-ML(Q)-Data Pre-processing Using Python(2023), available at $69.99, has an average rating of 4.83, with 85 lectures, based on 3 reviews, and has 1052 subscribers.

You will learn about Understand Project Management Methodology to Handle Data Related Projects in Structured Manner. Understand Business Problem Definition, Setting Objectives & Constraints. Understand Data Types as well as Data Collection Mechanisms. Understand Exploratory Data Analytics (EDA) / Descriptive Statistics as well as Graphical Representation Understand the various Data Cleansing /Pre-Processing Tasks using Python. This course is ideal for individuals who are Beginners, Intermediate as well as Advanced learners or Freshers who are new of data science and want to embark into the field of data science or Working professionals who are working in different industries or Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts It is particularly useful for Beginners, Intermediate as well as Advanced learners or Freshers who are new of data science and want to embark into the field of data science or Working professionals who are working in different industries or Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts.

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

Summary

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

Price: $69.99

Average Rating: 4.83

Number of Lectures: 85

Number of Published Lectures: 85

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand Project Management Methodology to Handle Data Related Projects in Structured Manner.

Understand Business Problem Definition, Setting Objectives & Constraints.

Understand Data Types as well as Data Collection Mechanisms.

Understand Exploratory Data Analytics (EDA) / Descriptive Statistics as well as Graphical Representation

Understand the various Data Cleansing /Pre-Processing Tasks using Python.

Who Should Attend

Beginners, Intermediate as well as Advanced learners

Freshers who are new of data science and want to embark into the field of data science

Working professionals who are working in different industries

Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts

Target Audiences

Beginners, Intermediate as well as Advanced learners

Freshers who are new of data science and want to embark into the field of data science

Working professionals who are working in different industries

Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts

This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the ‘4’ moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets.

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

Instructors

360DigiTMG Elearning
360DigiTMG is a leading training institute

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!