Feature Engineering For Data Science & Analytics

Learn Industry Level Data Cleaning, Data Preprocessing and Advanced Feature Engineering. All You Need Is Covered!!

What you'll learn

Master Data Analysis With Python

Master Beginner To Advance Level Data Analytics Techniques

Master How To 𝑫𝒆𝒂𝒍 𝑾𝒊𝒕𝒉 𝑴𝒆𝒔𝒔𝒚 𝑫𝒂𝒕𝒂 (outliers, missing values, data imbalance, data leakage etc.)

Know How To Deal With 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗜𝘀𝘀𝘂𝗲𝘀 In Python

Learn 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗼𝗼𝗹𝘀 And Libraries For Professional Data Cleaning And Analysis

Get The Skill Needed To Be Part Of The 𝗧𝗼𝗽 𝟭𝟬% Data Analytics and Data Science

Learn The Best Ways To 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 To Build Machine Learning Models

Master Different Techniques Of 𝗗𝗲𝗮𝗹𝗶𝗻𝗴 𝗪𝗶𝘁𝗵 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮

Perform Industry Level Data Engineering

Master How To Deal with 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬

Master 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴

Master How To Deal with 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮

Master How To Deal with 𝗗𝗮𝘁𝗮 𝗜𝗺𝗯𝗮𝗹𝗮𝗻𝗰𝗲

Master How To Deal with 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗸𝗮𝗴𝗲

Requirements

This is a beginner to advanced course and the instructor with many years of experience in the industry and classroom breaks the concepts down for anyone at any level to understand. A laptop, internet connections and willingness to learn is enough to succeed in this comprehensive course.

Description

Interested in the field of Data Analytics, Business Analytics, Data Science or Machine Learning?Do you want to know the best ways to clean data and derive useful insights from it?Do you want to save time and easily perform Exploratory Data Analysis(EDA)?Then this course is for you!!According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organizing the data…"In this course, you will not just get to know the industry level strategies but also I will practically demonstrate them for better understanding.This course has been practically and carefully designed by industry experts to reflect the real-world scenario of working with messy data.This course will help you learn complex Data Analytic techniques and concepts for easier understanding and data manipulations.We will walk you through step-by-step on each topic explaining each line of code for your understanding.This course has been structured in the following form:Introduction To Basic ConceptsIntroduction To Data Analysis ToolsHow To Properly Deal With Python Data TypesHow To Properly Deal With Date and Time In PythonHow To Properly Deal With Missing ValuesHow To Properly Deal With OutliersHow To Properly Deal With Data ImbalanceHow To Properly Deal With Data LeakageHow To Properly Deal With Categorical ValuesBeginner To Advanced Data VisualizationDifferent Feature Engineering Techniques including:Feature EncodingFeature ScalingFeature TransformationFeature NormalizationAutomated Feature EDA Toolspandas-profilingDoraAutovizSweetvizAutomated Feature EngineeringRFECVFeatureToolsFeatureSelectorAutofeatWeb scrapingWikipediaonline bookstoreAmazon .comThis course aims to help beginners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.

Overview

Section 1: Levels Of Data Analysis Techniques

Lecture 1 Overview

Section 2: Bivariate Data Analysis

Lecture 2 Bivariate Data Analysis

Section 3: Multivariate Data Analysis

Lecture 3 Multivariate Data Analysis

Lecture 4 Multivariate Data Analysis: Barplot

Lecture 5 Multivariate Data Analysis: Pairplot

Section 4: Feature Engineering

Lecture 6 Introduction To Feature Engineering

Lecture 7 What Is Feature Engineering?

Lecture 8 What We Will Learn

Section 5: How To Deal With Data Types In Python

Lecture 9 Integer & Floating Point Numbers

Lecture 10 Complex Numbers & Strings

Lecture 11 LIST

Lecture 12 Tuple & List Mutability

Lecture 13 Tuple Immutability

Lecture 14 Set

Lecture 15 Dictionary

Section 6: Working With Data Types In Python

Lecture 16 Continuous Vs Discrete

Lecture 17 Dataset Intro

Lecture 18 Describing The Dataset

Lecture 19 Rounding/Bucketing/Binning

Lecture 20 Rounding(Hands-On Demonstration)

Lecture 21 Rounding Continuation

Lecture 22 Counts

Lecture 23 Binarization

Lecture 24 Binning / Quantisation/ Grouping

Lecture 25 Bining / Quantisation/ group (Continuation)

Section 7: How To Deal With Date And Time In Python

Lecture 26 Introduction

Lecture 27 How To Extract Day, Month and Year from a given Time

Lecture 28 How to Extract Hours, Minutes, Seconds and Micro-seconds from a given Time

Lecture 29 How To Update current Date

Lecture 30 Working With TimeDelta in Python

Lecture 31 How To Extract Week-Day From A Given Date 1

Lecture 32 How To Extract Week-Day From A Given Date 2

Lecture 33 How To How To Generate Calendar

Lecture 34 How To Format Date and Time in Python

Lecture 35 Date and Time Formatting Using STRFTIME and STRPTIME

Lecture 36 How To Extract The Year, Month, Day Time Using STRFTIME

Lecture 37 How To Work With Timestamp

Lecture 38 How To Convert Strings To DateTime Using STRPTIME

Lecture 39 How To Handle Different Time Zones

Lecture 40 DataFrame: Get Year, Month and Day from a DataFrame 1

Lecture 41 DataFrame: Get Year, Month and Day from a DataFrame 2

Lecture 42 DataFrame: How to Get The Week and Leap Year from DataFrame

Lecture 43 DataFrame: How to Get Age from Date

Lecture 44 Operations on Date and Time with Dataset 1

Lecture 45 Operations on Date and Time with Dataset 2

Lecture 46 Operations on Date and Time with Dataset 3

Section 8: How To Deal With Missing Values

Lecture 47 What are Missing Values?

Lecture 48 Overview Of Dataset

Lecture 49 Counting And Replacing Missing Values

Lecture 50 Replacing Missing Values With NaN

Lecture 51 Visualising The Missing Using MissingNO (Matrix)

Lecture 52 Visualising The Missing Using MissingNO (Bar Plot)

Lecture 53 Linear Discriminant Analysis

Lecture 54 Dropping Missing Values Using Dropna

Lecture 55 Linear Discriminant Analysis With No Missing Value

Lecture 56 Missing Value Imputation

Lecture 57 Feature Distribution

Lecture 58 Outlier Effect

Lecture 59 Impute Missing Values With The Right Statistics

Lecture 60 Simple Imputer

Lecture 61 Testing Machine Learning Model on Clean Dataset

Students who want to become Data Analyst or Data Scientist and are serious about their career,Anyone interesting in diving deeper into driving critical insights from data,Working professionals who want to transition to the field of Artificial Intelligence(A.I.) , Machine Learning, Deep learning, Computer Vision(CV), Natural Language Processing(NLP),Anyone interesting in diving deeper into knowing how to deal with messy data,Anyone finding it difficult to understand the field and concepts in Data Analytics and wants a breakdown step-by-step guide in understanding these concepts.,Anyone who wants to be career secured and not easily affected by layoffs in organizations,Anyone looking for salary hikes and increase in salary with a lucrative tech career.,NB: This course is not for lazy students who are not serious about their career. I spent a lot of time creating a comprehensive course like this, I expect you to be serious about your career. The course is good and there is no two ways about it. You just have to be serious to work towards your goals and we can achieve it together.,Any student ready to learn how to deal with complex machine learning problems such as imbalance data, data leakage, basic to advanced Feature Engineering etc.