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2021 Python Data Analysis | Data Engineering

Posted By: ELK1nG
2021 Python Data Analysis | Data Engineering

2021 Python Data Analysis | Data Engineering
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 321 lectures (24h 55m) | Size: 10.9 GB

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
Learn The Latest Data Analytics Skills And Techniques In 2021
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
Master The Art Of Visualisation And Data Story Telling
Perform Industry Level Data Engineering

Requirements
This course is a beginner to advance level course with a step-by-step walk through.
If you are a complete beginner, you have all the lessons from introduction to python to dealing with complex data issues and building a web-scraper.
If you already have the basics in Python, feel free to skip the Python Crush course at the BONUS session.

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 organising 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 Concepts

Introduction To Data Analysis Tools

BONUS: Python Crush Course

How To Properly Deal With Python Data Types

How To Properly Deal With Date and Time In Python

How To Properly Deal With Missing Values

How To Properly Deal With Outliers

How To Properly Deal With Data Imbalance

How To Properly Deal With Data Leakage

How To Properly Deal With Categorical Values

Beginner To Advanced Data Visualisation

Different Feature Engineering Techniques including:

Feature Encoding

Feature Scaling

Feature Transformation

Feature Normalisation

Automated Feature EDA Tools

pandas-profiling

Dora

Autoviz

Sweetviz

Automated Feature Engineering

RFECV

FeatureTools

FeatureSelector

Autofeat

Web scraping

Wikipedia

online bookstore

Amazon .com

This 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.

Who this course is for
This course is from a beginner level to advance level, and therefore anyone interested in learning basic to complex Data Analytics techniques for Data Science and Machine Learning is strongly advised to enrol.
Anyone preparing for a career in Data Analytics, Data Science, Business Analytics, Business Intelligence, Machine Learning will highly find this course very useful.
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. is strongly recommended to enrol.
Anyone who is looking for a career transition to Data Analytics, Data Science, Business Analytics, Business Intelligence, Machine Learning role and wants to understand the concepts very well from scratch is recommended to enrol.