Exploratory Data Analysis In Python, Pandas & Excel

Exploratory Data Analysis In Python, Pandas & Excel
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.53 GB | Duration: 2h 42m

Analyze data quickly and easily with Python's powerful pandas library! All datasets included –- beginners welcome!

What you'll learn

Exploratory data analysis with Excel, Pandas & Python

A course about how to approach a dataset for the first time

How to perform EDA Analysis with Power Query

Apply your skills to real-life business cases

Data Analysis & Exploratory Data Analysis

Requirements

Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables etc)

Description

Master Data Analysis: Python, Statistics, EDA, Feature Engineering, Power BI, and SQL Server in Comprehensive B in Comprehensive Bootcamp. Step-by-step projects with clear explanations. EDA is an important step of data science and machine learning.With this course, the student will learn:How to visualize information that is hidden inside the datasetHow to visualize the correlation and the importance of the columns of a datasetSome useful Python librariesThis is the best course for people who have just learnt python basics(prerequisite for this course) and want to become Data Analyst/Data Scientist.Before diving into data modeling, it's crucial to understand your data. EDA is the process of analyzing data sets to summarize their main characteristics, often with visual methods. You'll learn how to identify trends, patterns, and outliers using visualization tools like Matplotlib and Seaborn. This step is essential for uncovering insights and ensuring data quality.Everyone who want to step into Data Science/Data Analytics. Learn to build interactive and insightful dashboards using Power BI, applying DAX for complex calculations, and integrating real-world data to produce reports.Resolve common issues in broken or incomplete data sets. Learn python in detail and get exposure. good luck and hands on python.

Overview

Section 1: Basics Concepts Data Analysis

Lecture 1 Introduction to Data Analysis

Lecture 2 Understanding Data

Lecture 3 Understanding Data II

Lecture 4 Role of data in business

Lecture 5 Rise of Data Driven Culture

Lecture 6 Role of Data Engineer

Lecture 7 Role of Business Intelligence Analyst

Section 2: Understanding Data Analyst Job Description

Lecture 8 Data Analyst Job Description

Lecture 9 Data Analysis Tools

Section 3: Understanding Different Roles in Data Science Field

Lecture 10 Data Analyst Role

Lecture 11 Role of Data Scientist

Section 4: Exploratory Data Analysis with Power Query

Lecture 12 introduction to Power query

Lecture 13 Data Tranformation with Power Query

Lecture 14 Custom Column Creation Transformation

Lecture 15 How to Apply if condition

Lecture 16 Fill Series with Power Query

Lecture 17 Delimeters Remove

Lecture 18 How to Append Excel Sheet in 1 Master Sheet

Section 5: Exploratory Data Analysis with Excel

Lecture 19 EDA with Microsoft Excel

Lecture 20 Live Operation with Power Query

Lecture 21 How to change Data Types

Section 6: Final Assignment for Exploratory Data Analysis

Lecture 22 Final Assignments for EDA

Section 7: Introduction to Pandas Library

Lecture 23 What is Pandas

Lecture 0 How to Install Python

Lecture 24 How to Import Libraries in VS Code

Lecture 25 How to save data set

Lecture 26 how to get column information

Lecture 27 How to perform Descriptive Analysis

Lecture 28 How to get unique values

Lecture 29 How to filter Data

Lecture 30 How to filter specific Records

Lecture 31 Data Filter

Lecture 32 Null value Sum

Lecture 33 How to group Data

Lecture 34 How to Replace Null Values

Section 8: Data Visualization

Lecture 35 Data Visualization Count Plot

Lecture 36 Histogram Plot

Lecture 37 Bar Plot

Lecture 38 Scatter Plot

Lecture 39 Box Plot

Section 9: Pandas Cheat Sheet

Lecture 40 Pandas Cheat Sheet

Lecture 41 what is data cleaning

Section 10: Final Assignment for Pandas

Lecture 42 Final Assignment for Pandas

Data analysts and business analysts