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**BaDshaH** · 06-08-2023, 12:41 PM

[Image: S4-NLPLa-YI48-Jo1s-YE7f-XDe-Ku-Zy-E0la0-K.jpg]

Published 6/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.55 GB | Duration: 3h 33m

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

[b]What you'll learn[/b]
Master Data Analysis With Python
Master Beginner To Advance Level Data Analytics Techniques
Master How To ???? ???? ????? ???? (outliers, missing values, data imbalance, data leakage etc.)
Know How To Deal With ??????? ???? ???????? ?????? In Python
Learn ????????? ?????? ????? And Libraries For Professional Data Cleaning And Analysis
Get The Skill Needed To Be Part Of The ??? ??% Data Analytics and Data Science
Learn The Best Ways To ??????? ???? ???? To Build Machine Learning Models
Master Different Techniques Of ??????? ???? ??? ????
Perform Industry Level Data Engineering
Master How To Deal with ????????
Master ???????????? ???? ????????
Master How To Deal with ??????? ????
Master How To Deal with ???? ?????????
Master How To Deal with ???? ???????

[b]Requirements[/b]
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.

[b]Description[/b]
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.

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