Complete Data Preprocessing in Python- Hands-on Data Science

Complete Data Preprocessing in Python: Hands-on Data Science, available at $54.99, has an average rating of 5, with 180 lectures, 9 quizzes, based on 4 reviews, and has 52 subscribers.

You will learn about Learn how to clean your data the right way for Data Science and Machine Learning Projects For each topic learn multiple approaches to perform Data Pre-processing – Common Approaches vs Practical Approaches Learn Missing Value Treatment, Outlier Treatment, Feature Scaling, Feature Selection, Multicollinearity Treatment, Anomaly Detection, Imbalanced Data Treatment In-depth Theory plus Hands-on exercises for all topics related to Data Preparation for Data Science and Machine Learning Refresh the foundation Python modules like working with Numpy arrays, Pandas data frames, Data Visualization using Matplotlib, Seaborn, and Basic Statistics This course is ideal for individuals who are Data Science students who are interested in Data Preprocessing, Data Preparation, Data Wrangling or Data Science practitioners who want to learn the practical industry level practices for Data Preprocessing, Data Preparation, Data Wrangling It is particularly useful for Data Science students who are interested in Data Preprocessing, Data Preparation, Data Wrangling or Data Science practitioners who want to learn the practical industry level practices for Data Preprocessing, Data Preparation, Data Wrangling.

Enroll now: Complete Data Preprocessing in Python: Hands-on Data Science

Summary

Title: Complete Data Preprocessing in Python: Hands-on Data Science

Price: $54.99

Average Rating: 5

Number of Lectures: 180

Number of Quizzes: 9

Number of Published Lectures: 180

Number of Published Quizzes: 9

Number of Curriculum Items: 189

Number of Published Curriculum Objects: 189

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to clean your data the right way for Data Science and Machine Learning Projects

For each topic learn multiple approaches to perform Data Pre-processing – Common Approaches vs Practical Approaches

Learn Missing Value Treatment, Outlier Treatment, Feature Scaling, Feature Selection, Multicollinearity Treatment, Anomaly Detection, Imbalanced Data Treatment

In-depth Theory plus Hands-on exercises for all topics related to Data Preparation for Data Science and Machine Learning

Refresh the foundation Python modules like working with Numpy arrays, Pandas data frames, Data Visualization using Matplotlib, Seaborn, and Basic Statistics

Who Should Attend

Data Science students who are interested in Data Preprocessing, Data Preparation, Data Wrangling

Data Science practitioners who want to learn the practical industry level practices for Data Preprocessing, Data Preparation, Data Wrangling

Target Audiences

Data Science students who are interested in Data Preprocessing, Data Preparation, Data Wrangling

Data Science practitioners who want to learn the practical industry level practices for Data Preprocessing, Data Preparation, Data Wrangling

This course focuses on Data Preprocessing. Mastering data cleaning is an absolute must for anyone venturing into the world of data science. Picture this: you’re diving into a new dataset, eager to extract insights and build models, only to find it’s riddled with missing values, outliers, and inconsistencies. Sound familiar? That’s where data preprocessing skills come in handy. By learning how to wrangle messy data into shape, you’re setting yourself up for success. Clean data means accurate analyses, reliable models, and ultimately, more impactful insights. Plus, it shows you’re serious about your craft, which can go a long way in a competitive field like data science. So, embrace the data cleaning process—this course helps you unlock the true potential of your data! What sets this course apart is our unique approach. We don’t just teach you the standard methods. We show you the limitations of common approaches and the strengths of practical, real-world techniques. This course provides you a unique blend of theory and hands-on exercises in Python which will help boost your confidence while dealing with any type of data. In addition, we’ll help you refresh Python programming basics and learn to leverage popular libraries like NumPy, Pandas, and Matplotlib for efficient data preprocessing.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Common Approach to EDA and Data Pre-processing

Lecture 3: Practical Approach to EDA and Data Pre-processing

Lecture 4: Practice Exercises

Lecture 5: Getting started with Google Colaboratory

Lecture 6: Reading your data in Google Colab

Chapter 2: Data Preprocessing: Feature Scaling

Lecture 1: Section Intro

Lecture 2: What is Feature Scaling?

Lecture 3: Why Feature Scaling is needed?

Lecture 4: Scaling Treatment Options

Lecture 5: Advantages and Disadvantages of Scaling

Lecture 6: Getting started with the Data

Lecture 7: The Normal Distribution

Lecture 8: The StandardScaler

Lecture 9: The MinMaxScaler

Lecture 10: The RobustScaler

Lecture 11: Important note related to next tutorial

Lecture 12: Data Leakage

Lecture 13: Practice Exercise Part 1

Lecture 14: Solution: Practice Exercise Part 1

Chapter 3: Data Preprocessing: Missing Value Treatment

Lecture 1: Section Intro

Lecture 2: What are missing values?

Lecture 3: Common mistakes to be avoided

Lecture 4: Getting started with the Data

Lecture 5: Dropping Missing Values

Lecture 6: Filling Missing Values

Lecture 7: Forward Fill and Backward Fill

Lecture 8: The Simple Imputer

Lecture 9: KNN Intuition

Lecture 10: The KNN Imputer

Lecture 11: The Iterative Imputer

Lecture 12: A closer look at Missing Values

Lecture 13: Recognizing Missing Values Hands-on

Chapter 4: Data Preprocessing: Outlier Treatment

Lecture 1: Section Intro

Lecture 2: What are Outliers?

Lecture 3: Why to treat Outliers?

Lecture 4: Choosing the Outlier Treatment

Lecture 5: Getting started with the Data

Lecture 6: Detecting Outliers Using Tukeys Approach

Lecture 7: Remove Outlier Rows

Lecture 8: Replace Outliers with the Median

Lecture 9: Feature Transformation for Outliers

Lecture 10: Winsorization

Lecture 11: Algorithmic Treatment

Lecture 12: Practice Exercise Part 2

Lecture 13: Solution: Practice Exercise Part 2

Chapter 5: Data Preprocessing: Multicollinearity Treatment

Lecture 1: Section Intro

Lecture 2: What is Multicollinearity?

Lecture 3: Why to treat Multicollinearity?

Lecture 4: Choices for Multicollinearity Treatment

Lecture 5: Common mistakes to be avoided

Lecture 6: What is Variance Inflation Factor(VIF)?

Lecture 7: Getting started with the Data

Lecture 8: Dropping Correlated Variables

Lecture 9: Eliminating Highly Correlated Features using VIF

Lecture 10: Practice Exercise Part 3

Lecture 11: Solution: Practice Exercise Part 3

Chapter 6: Data Preprocessing: Feature Selection

Lecture 1: What is Feature Selection?

Lecture 2: Variance Threshold

Lecture 3: Select K Best

Lecture 4: Recursive Feature Elimination

Lecture 5: Select From Model

Lecture 6: Sequential Feature Selector

Lecture 7: Getting started with the data

Lecture 8: Supervised Feature Selection

Lecture 9: Hands-on Variance Threshold

Lecture 10: Hands-on Select K Best

Lecture 11: Hands-on Recursive Feature Elimination

Lecture 12: Hands-on Select From Model

Lecture 13: Hands-on Sequential Feature Selector

Chapter 7: Data Preprocessing: Feature Encoding

Lecture 1: Section Intro

Lecture 2: What is Feature Encoding?

Lecture 3: Why to perform Feature Encoding?

Lecture 4: Encoding Choices

Lecture 5: Label Encoding

Lecture 6: One Hot Encoding

Lecture 7: Ordinal and Custom Encoding

Lecture 8: Target Encoding

Lecture 9: Common mistakes to be avoided

Lecture 10: Getting started with the Data

Lecture 11: Label Encoder Hands-on

Lecture 12: One Hot Encoder Hands-on (sklearn)

Lecture 13: One Hot Encoder Hands-on (pandas)

Lecture 14: Encoding Features with High Cardinality

Lecture 15: Ordinal Encoder Hands-on

Lecture 16: Custom Encoder Hands-on

Lecture 17: Target Encoder Hands-on

Instructors

Nash J
Executive Coach for Data Science

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5 stars: 4 votes

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