Data Pre-Processing for Data Analytics and Data Science

Data Pre-Processing for Data Analytics and Data Science, available at $64.99, has an average rating of 4.05, with 49 lectures, 10 quizzes, based on 123 reviews, and has 2398 subscribers.

You will learn about Students will get in-depth knowledge of Exploratory Data Analysis & Data Pre-Processing We learn about Data Cleaning & how to handle the data. We will learn about how to handle Duplicate & Missing Data. Finally, we will learn a variety of Outlier Analysis Treatment. We will learn about Features Scaling and Transformation Techniques This course is ideal for individuals who are This course is designed for people who desire to advance their careers in Data Analytics & Data Science. or It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q). or Students of all backgrounds are invited to enroll in this program. or Students with engineering backgrounds are invited to use this program to supplement their education. or Anyone who wants to get into the field of Data and Analyse the Data. It is particularly useful for This course is designed for people who desire to advance their careers in Data Analytics & Data Science. or It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q). or Students of all backgrounds are invited to enroll in this program. or Students with engineering backgrounds are invited to use this program to supplement their education. or Anyone who wants to get into the field of Data and Analyse the Data.

Enroll now: Data Pre-Processing for Data Analytics and Data Science

Summary

Title: Data Pre-Processing for Data Analytics and Data Science

Price: $64.99

Average Rating: 4.05

Number of Lectures: 49

Number of Quizzes: 10

Number of Published Lectures: 48

Number of Published Quizzes: 10

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 58

Original Price: ₹1,199

Quality Status: approved

Status: Live

What You Will Learn

Students will get in-depth knowledge of Exploratory Data Analysis & Data Pre-Processing
We learn about Data Cleaning & how to handle the data.
We will learn about how to handle Duplicate & Missing Data.
Finally, we will learn a variety of Outlier Analysis Treatment.
We will learn about Features Scaling and Transformation Techniques

Who Should Attend

This course is designed for people who desire to advance their careers in Data Analytics & Data Science.
It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q).
Students of all backgrounds are invited to enroll in this program.
Students with engineering backgrounds are invited to use this program to supplement their education.
Anyone who wants to get into the field of Data and Analyse the Data.

Target Audiences

This course is designed for people who desire to advance their careers in Data Analytics & Data Science.
It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q).
Students of all backgrounds are invited to enroll in this program.
Students with engineering backgrounds are invited to use this program to supplement their education.
Anyone who wants to get into the field of Data and Analyse the Data.

The Data Pre-processing for Data Analytics and Data Science course provides students with a comprehensive understanding of the crucial steps involved in preparing raw data for analysis. Data pre- processing is a fundamental stage in the data science workflow, as it involves transforming, cleaning, and integrating data to ensure its quality and usability for subsequent analysis.

Throughout this course, students will learn various techniques and strategies for handling real-world data, which is often messy, inconsistent, and incomplete. They will gain hands-on experience with popular tools and libraries used for data pre-processing, such as Python and its data manipulation libraries (e.g., Pandas), and explore practical examples to reinforce their learning.

Key topics covered in this course include:

Introduction to Data Pre-processing:

– Understanding the importance of data pre-processing in data analytics and data science

– Overview of the data pre-processing pipeline

– Data Cleaning Techniques:

Identifying and handling missing values:

– Dealing with outliers and noisy data

– Resolving inconsistencies and errors in the data

– Data Transformation:

Feature scaling and normalization:

– Handling categorical variables through encoding techniques

– Dimensionality reduction methods (e.g., Principal Component Analysis)

– Data Integration and Aggregation:

Merging and joining datasets:

– Handling data from multiple sources

– Aggregating data for analysis and visualization

– Handling Text and Time-Series Data:

Text preprocessing techniques (e.g., tokenization, stemming, stop-word removal):

– Time-series data cleaning and feature extraction

– Data Quality Assessment:

Data profiling and exploratory data analysis

– Data quality metrics and assessment techniques

– Best Practices and Tools:

Effective data cleaning and pre- processing strategies:

– Introduction to popular data pre-processing libraries and tools (e.g., Pandas, NumPy)

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 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 Scope Of Application

Lecture 2: Business Understanding – Define Sucess 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 vs Discrete

Lecture 4: Categorical Data vs Count Data

Lecture 5: Pratical Data Understanding Using Realtime Examples

Lecture 6: Scale of Measurement

Lecture 7: Quantitative Vs Qualitative

Chapter 4: Data Understanding Phase – Data Collection

Lecture 1: Structured vs Unstructured Data

Lecture 2: What is Data Collection?

Lecture 3: Understanding Primary Data Sources

Lecture 4: Understanding Secondary Data Sources

Lecture 5: Understanding Data Collection using Survey

Lecture 6: Understanding Data Collection using DoE

Lecture 7: Understanding Bias and Fairness

Lecture 8: Understanding Possible errors in Data Collection stage

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 Variable?

Lecture 4: Understanding Probability and its Application,Probability Distribution .

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

Lecture 1: Understanding Normal Distribution

Lecture 2: What is Inferencial Statistics?

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 and Setup

Lecture 1: Anakonda Installation

Lecture 2: Understand about Anakonda Navigator, Spyder & Python Libraries

Lecture 3: Python Installation

Lecture 4: Understanding about Jupyter and Google Colab

Chapter 8: 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 9: 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 10: Data Preparation Phase | Data Cleansing-Outlier Analysis Treatment

Lecture 1: Understanding Outlier Analysis Treatment

Lecture 2: Understanding Outlier Analysis Treatment using Python

Instructors

AISPRY TUTOR
AISPRY Tutor is a branch of learning platform with360DigitMG

Rating Distribution

1 stars: 2 votes
2 stars: 4 votes
3 stars: 13 votes
4 stars: 24 votes
5 stars: 80 votes

Frequently Asked Questions

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