5 H Data Challenge Course – Scraping-Cleaning-Analysis

5 H Data Challenge Course – Scraping-Cleaning-Analysis, available at $69.99, has an average rating of 4.25, with 35 lectures, based on 61 reviews, and has 7966 subscribers.

You will learn about Data Extraction Data Cleaning Data Analysis Pandas PostgreSQL This course is ideal for individuals who are Data Scientists or Data Analysts or People who want to create their own dataset, clean the data and analyze the data or Answer questions with Pandas, PostgreSQL, Matplotlib It is particularly useful for Data Scientists or Data Analysts or People who want to create their own dataset, clean the data and analyze the data or Answer questions with Pandas, PostgreSQL, Matplotlib.

Enroll now: 5 H Data Challenge Course – Scraping-Cleaning-Analysis

Summary

Title: 5 H Data Challenge Course – Scraping-Cleaning-Analysis

Price: $69.99

Average Rating: 4.25

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Data Extraction
Data Cleaning
Data Analysis
Pandas
PostgreSQL

Who Should Attend

Data Scientists
Data Analysts
People who want to create their own dataset, clean the data and analyze the data
Answer questions with Pandas, PostgreSQL, Matplotlib

Target Audiences

Data Scientists
Data Analysts
People who want to create their own dataset, clean the data and analyze the data
Answer questions with Pandas, PostgreSQL, Matplotlib

3 Main Topics will be covered:

1) Data Extraction/ Web Scraping

2) Data Cleaning

3) Data Analysis

We start with the extraction of Real Estate Data from 10 different cities/states. Once we have collected all the necessary data, the datasets will be merged to one dataset. Then the cleaning process with the Pandas Library will start. The goal is to make the data database readable and modifying/ manipulating the data. Once our data is cleaned, we start with the Data Analysis part. That means we will answer 10 different questions, based on our extracted and cleaned dataset. To be able to answer the 10 questions, we make use of the Pandas Dataframe, PostgreSQL and Matplotlib. You will get to know different approaches to how answer real life questions based your own created dataset.

After this course you will have the knowledge and the experience to scrape your own data and create your own dataset. After the datasets is created, we will clean the data and finally focus on the data analysis.

With the help of the course resources you will always have documents you can refer to. If you have a question or if a concept just does not make sense to you, you can ask your questions anytime inside the Q&A – Forum. Either the instructor or other students will answer your question. Thanks to the community you will never have the feeling to learn alone by yourself.

What you’ll learn

Web Scraping
Pandas
Beautiful Soup
Data Extraction
Web Scraping for Data Science
Data Mining
Data Scraping & Data Cleaning
Data Analysis
PostgreSQL

Are there any course requirements or prerequisites?

Basic understanding of Python Programming
Basic understanding of Beautiful Soup

Who this course is for:

Everybody who is interested in Web Scraping (Create own dataset), Data Cleaning & Data Analysis
Professionals who want to create their own dataset without being dependent on some else

Disclaimer : I teach web scraping as a tutor for educational purposes. That’s it.

The first rule of scraping the web is: do not harm a certain website. The second rule of web crawling is: do NOT harm a certain website.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Before you start

Chapter 2: Relevant Packages

Lecture 1: Beautiful Soup & Requests

Lecture 2: Jupyter Notebook & Pandas & sqlalchemy

Lecture 3: PostgreSQL

Chapter 3: Dataset Creation – Web Scraping

Lecture 1: Dataset Creation – Overview

Lecture 2: Initial Steps

Lecture 3: Concatenate URL Parts

Lecture 4: Extract Data from First Link

Lecture 5: Put all together and loop through results

Lecture 6: Scrape Multiple Pages

Chapter 4: Data Cleaning

Lecture 1: Data Cleaning – Overview

Lecture 2: Imports & Merge Datasets

Lecture 3: Missing Data, Doubles & Index Change

Lecture 4: Change Price Column

Lecture 5: Change Area Column

Lecture 6: Change Bedrooms & Bathrooms Column

Lecture 7: Modify Parking Column

Lecture 8: Numbers to Integers Part 1

Lecture 9: Numbers to Integers Part 2

Lecture 10: Numbers to Integers Part 3

Lecture 11: Create New Price/Sqft Column

Chapter 5: Data Analysis

Lecture 1: Data Analysis Section – Overview

Lecture 2: Imports & Output Data (Pandas/PostgreSQL)

Lecture 3: Question 1

Lecture 4: Question 2

Lecture 5: Question 3

Lecture 6: Question 4

Lecture 7: Question 5

Lecture 8: Question 6

Lecture 9: Question 7

Lecture 10: Question 8

Lecture 11: Question 9

Lecture 12: Question 10

Chapter 6: Next Steps

Lecture 1: Resources

Instructors

Alexander Schlee
Python Developer | Data Science

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 9 votes
5 stars: 49 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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