Science

Motion Science – AE Academy Volume 2 Intermediate Techniques

Motion Science – AE Academy Volume 2: Intermediate Techniques
English | Size: 18.6GB
Category: Tutorial


In this 6+ hour training course, you will learn how to expertly utilize the graph editor, implement expressions, stylize color, operate keylight, remove elements with the rotobrush, motion track and stabilize footage with both 2D & 3D tracking systems, render using advanced methods in both After Effects and Media Encoder, and much more.

Linkedin Learning – R For Data Science Lunchbreak Lessons Weekly UPDATED

Linkedin Learning – R For Data Science Lunchbreak Lessons Weekly UPDATED 2020/12/18-QUiD
English | Size: 1.46 GB
Category: Tutorial


Programming is learned in small bits. You build on basic concepts. You transfer the knowledge you already have to the next language. Lunch Break Lessons teaches R one of the most popular programming languages for data analysis and reporting in short lessons that expand on what existing programmers already know

Lynda – R For Data Science: Lunchbreak Lessons

Lynda – R For Data Science: Lunchbreak Lessons Bookware-KNiSO
English | Size: 1.43 GB
Category: Tutorial


Learn R on your lunch break. This weekly series reviews the language features, development tools, and libraries that will make you a more productive R programmer

Lynda – 15 Mistakes to Avoid in Data Science

15 Mistakes to Avoid in Data Science
English | Size: 358.9 MB
Category: Tutorial


As a data scientist, your goal is to always be growing your skills. But, if you realize it or not, there are errors you may be making that are keeping you from moving to the next level. In this course, learn the top 15 data science mistakes: misunderstanding business problems, using the wrong tools, starting without a plan, and much more. Four leading data scientists share the hard-won lessons they’ve learned about alienating colleagues with technical jargon, moving too fast, and using sample sizes that are just too small. Find out why you should make your best effort to prevent bias-and avoid overpromising solutions to stakeholders. Plus, learn why writing custom code can lead to a big waste of time and why the most promising data science insights fall flat without a compelling story.