EDA / Descriptive Statistics using Python (Part – 1)

EDA / Descriptive Statistics using Python (Part – 1), available at $19.99, has an average rating of 4.25, with 47 lectures, 8 quizzes, based on 53 reviews, and has 1177 subscribers.

You will learn about Students will get an elaborate understanding of exploratory data analysis, also known as descriptive statistics. We dig deep into the first-moment business decision, aka measures of central tendency. We gain an understanding of second-moment business decisions, aka measures of dispersion. We further understand the importance of third and fourth-moment business decisions, aka skewness. Finally, we also look at the multitude of graphical representations like univariate, bivariate, and multivariate plots. This course is ideal for individuals who are This course is for individuals who want to upskill and make a career in the field of data science. or It is also for working professionals who would like to upskill their understanding of CRISP-ML(Q). or Students from any background are encouraged to take up this course. or Students from engineering backgrounds are welcome to enrich their learning process using this program. It is particularly useful for This course is for individuals who want to upskill and make a career in the field of data science. or It is also for working professionals who would like to upskill their understanding of CRISP-ML(Q). or Students from any background are encouraged to take up this course. or Students from engineering backgrounds are welcome to enrich their learning process using this program.

Enroll now: EDA / Descriptive Statistics using Python (Part – 1)

Summary

Title: EDA / Descriptive Statistics using Python (Part – 1)

Price: $19.99

Average Rating: 4.25

Number of Lectures: 47

Number of Quizzes: 8

Number of Published Lectures: 47

Number of Published Quizzes: 8

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Students will get an elaborate understanding of exploratory data analysis, also known as descriptive statistics.
We dig deep into the first-moment business decision, aka measures of central tendency.
We gain an understanding of second-moment business decisions, aka measures of dispersion.
We further understand the importance of third and fourth-moment business decisions, aka skewness.
Finally, we also look at the multitude of graphical representations like univariate, bivariate, and multivariate plots.

Who Should Attend

This course is for individuals who want to upskill and make a career in the field of data science.
It is also for working professionals who would like to upskill their understanding of CRISP-ML(Q).
Students from any background are encouraged to take up this course.
Students from engineering backgrounds are welcome to enrich their learning process using this program.

Target Audiences

This course is for individuals who want to upskill and make a career in the field of data science.
It is also for working professionals who would like to upskill their understanding of CRISP-ML(Q).
Students from any background are encouraged to take up this course.
Students from engineering backgrounds are welcome to enrich their learning process using this program.

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 about Tutor

Lecture 2: Agenda and stages of analytics

Lecture 3: What is diagnoistic Analytics

Lecture 4: What is Predicative Analytics

Lecture 5: What is CRISP – ML(Q)

Chapter 2: Business Understanding Phase

Lecture 1: Business Understanding – Define the Scope of Application

Lecture 2: Business Understanding – Define Business Criteria

Lecture 3: Business Understanding – Use Case

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

Lecture 4: Pratical Data Understanding using Real time Experiences

Lecture 5: Scale Of Measurement

Lecture 6: Quantitative vs Qualitative

Lecture 7: Structured Vs Unstructured Data

Chapter 4: Data Understanding Phase – Data Collection

Lecture 1: What Is Data Collection ?

Lecture 2: Understanding Secondary Data Sources

Lecture 3: Undersatanding Primary Data Sources

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 and 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 Preliminaries Concepts

Lecture 2: Understanding Normal Distribution

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

Lecture 4: Understanding Measures of Central Tendency (First Moment Business Decision )

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 and setup

Lecture 1: Python Installation

Lecture 2: Anaconda Installation

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

Lecture 4: Understand about Jupyter & Google Colab

Chapter 8: Data Preparation Phase | EDA Using Python

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

Lecture 2: Understanding 3rd Moment Business Decision Using Python

Lecture 3: Understanding 4th Moment Business Decision Using Python

Lecture 4: Understanding Univariate (Bar plot & Histogram ) Using Python

Lecture 5: Understanding Univariate Plot Using Python

Lecture 6: Understanding Univariate Box plot Using Python

Lecture 7: Understanding Univariate Q-Q-Plot Using Python

Lecture 8: Understanding Bivariate Scatter Plot Using Python

Instructors

AISPRY TUTOR
AISPRY Tutor is a branch of learning platform with360DigitMG

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 7 votes
5 stars: 41 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!

Menu