Kaggle
I have started taking the courses at Kaggle. It is a fantastic website. Thank you, Google, for keeping it free.
At first, I was intimidated by how fast they jumped into machine learning, building a model in the first lesson of the first course. It goes quickly, but I think it is beneficial to start getting used to the concepts, and then I can go deeper in other classes or practice in the competitions.
I was also watching a video by Joma Tech where he talks about the whole data science pipeline, what he calls the Data Science Hierarchy of Needs:
It is about how the job of a data scientist/analyst/engineer can mean many different things in different companies and how there is often a disconnect between what you see in data science articles or videos and the company’s requirements.
In a different video, Krish Naik talks about his version of the data science ecosystem. It helped me get an overview, see which areas I am attacking in my learning, and what other topics/tools I will need to focus on in the future. Here is his list:
- Programming Language
- Python
- R
- Java
- Web Scraping
- Beautiful Soup
- Scrapy
- Urllib
- Data Analysis
- Feature Engineering
- Data wrangling
- Exploratory Data Analysis
- Data Visualization
- Tableau
- Power BI
- Matplotlib, GGplot, Seaborn
- Machine Learning
- Classification
- Regression
- Reinforcement
- Deep Learning
- Dimensionality Reduction
- Clustering
- IDE
- Pycharm
- Jupyter
- Spyder
- R Studio
- VS Studio
- Math
- Statistics
- Linear Algebra
- Differential Equations
- Deploy
- AWS
- Azure