5 H Data Challenge Course – Scraping-Cleaning-Analysis
5 H Data Challenge Course – Scraping-Cleaning-Analysis, Data Extraction – Data Cleaning – Data Analysis.
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
