(the minimize effort maximize outcome way)

Tina Huang covers how to learn data science in 2021 – the minimize effort and maximize outcome way.

Best of all, she provides a clear framework.

If you’re committed to learning data science in 2021, I have nothing more to say than to watch this video! I spend a lot of time putting this vid together and I can’t wait for us to start 😉

Udacity highlights the top 8 skills needed to be a data scientist.

Back in 2016, Glassdoor declared that being a Data Scientist was the best job in America.

Four years later, the world of big data has evolved rapidly, and the role of Data Scientist is still a top job. It’s currently ranked as the number three job for 2020 by Glassdoor, and it’s only surpassed by Java Developer and Front End Engineer.

What is it about being a Data Scientist that makes it so appealing, and what skills does someone looking to get into this field need to have?

Let’s take a look.

As the demand for data scientists increases, the field presents an appealing career path for students and existing technologists.

Here’s list of the top technical and non-technical skills every data scientist needs to be successful.

Utilizing big data, as an insight-producing engine has driven the demand for data scientists, across industry verticals. Regardless of whether it is to refine the process of product advancement, improve customer retention, or mine through the data to discover new business opportunities, companies are progressively depending on the expertise of data scientists to support, develop, and outshine their competition.

Learn the basics of Data Science in the crash course created by Marco Peixeiro, where you will learn about the theory and code behind the most common algorithms used in data science.

Datasets:

Course Contents

  • ⌨️ (00:00) Introduction
  • ⌨️ (03:06) Setup
  • ⌨️ (04:29) Linear regression (theory)
  • ⌨️ (09:29) Linear regression (Python)
  • ⌨️ (20:59) Classification (theory)
  • ⌨️ (30:16) Classification (Python)
  • ⌨️ (49:30) Resampling & regularization (theory)
  • ⌨️ (56:09) Resampling and regularization (Python)
  • ⌨️ (1:05:17) Decision trees (theory)
  • ⌨️ (1:13:12) Decision trees (Python)
  • ⌨️ (1:24:50) SVM (theory)
  • ⌨️ (1:28:17) SVM (Python)
  • ⌨️ (1:58:24) Unsupervised learning (theory)
  • ⌨️ (2:06:54) Unsupervised learning (Python)
  • ⌨️ (2:20:55) Conclusion

Yannic Kilcher explains why transformers are ruining convolutions.

This paper, under review at ICLR, shows that given enough data, a standard Transformer can outperform Convolutional Neural Networks in image recognition tasks, which are classically tasks where CNNs excel. In this Video, I explain the architecture of the Vision Transformer (ViT), the reason why it works better and rant about why double-bline peer review is broken.

OUTLINE:

  • 0:00 – Introduction
  • 0:30 – Double-Blind Review is Broken
  • 5:20 – Overview
  • 6:55 – Transformers for Images
  • 10:40 – Vision Transformer Architecture
  • 16:30 – Experimental Results
  • 18:45 – What does the Model Learn?
  • 21:00 – Why Transformers are Ruining Everything
  • 27:45 – Inductive Biases in Transformers
  • 29:05 – Conclusion & Comments

Related resources:

  • Paper (Under Review): https://openreview.net/forum?id=YicbFdNTTy