Site icon R-bloggers

DATA SCIENCE SESSIONS VOL. 1 :: 2021/22 :: Introduction to Data Science in R

[This article was first published on R – DataKolektiv, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Hey: we are sharing some twenty open, free R notebooks to learn Data Science in R here!

Our big educational gig is back: DATA SCIENCE SESSIONS VOL. 1 :: 2021/22, a 24 weeks, intense, introductory Data Science course in R will begin in October 2021.

The course material is GPLv2 licensed, which means that you can use it for free. DataKolektiv charges only our time in direct work with our students.

In DataKolektiv we do not offer any self-paced courses. In this course, you will be working directly with Goran S. Milovanović, Phd, owner of DataKolektiv, expert Data Scientist, and full-stack R developer who provides analytics services for some of the most complex, big datasets in the World, with more than 20 years of experience in Data Science and Analytics. We will begin in October 2021, so make sure to get in touch and enroll.

Figure 1: Start simple – the Sampling Distribution of the Mean.

And no, in DataKolektiv we do not believe that one can learn Data Science by investing 2 -3 hours of self-paced work weekly. We are very sorry to dissapoint many, but that is simply not possible. The weekly workload here is: 3h of tuition, at least 1h of labs, 1:1 sessions with the lecturer upon request monthly, and a minimum of 4h of individual work.

Figure 2: Likelihood Function – a fundamental concept in Estimation Theory.

The course encompasses 24 sessions, organized so to provide everything from an elementary introduction to R, RStudio, basic mathematical statistics, data wrangling, through data visualization, working with databases from R, advanced concepts in estimation theory, Linear and Generalized Linear Models, Decision Trees, Random Forests, and Model Selection techniques, towards reporting in R markdown and interactive visualizations. In other words, this course is a thorough, intensive introduction to Data Science in R, designed to meet the needs of a student who is determined to enter the field, providing rock-solid foundations to support future individual development. Several labs are there to support your learning and more will be added until the end of the year.

Figure 3: Understanding Entropy.

We especially encourage students of a non-technical, non-STEM background to apply. Your lecturer has studied mathematics, philosophy, and psychology, holds a Phd in psychology, and has years of experience as a full-stack R developer, providing software engineering in R from back-end interactions with Big Data systems (Spark, Hadoop), through advanced Machine Learning, and towards front-end development in R in production-grade code. So, it takes a lot of work to learn Data Science – but it is certainly doable. A non-STEM background will do just fine, but investing 2 – 3 hours weekly in a self-paced course alone will probably not.

The course is focused on Supervised Learning in Prediction and Classification problems.

Figure 4: Understanding Model Selection: an ROC analysis .

Here is an overview of the DATA SCIENCE SESSIONS VOL. 1 :: 2021/22 course with links to R markdown notebooks for each respective session:

Join me to learn R together!

Goran S. Milovanović, Phd
DataKolektiv 2021.

To leave a comment for the author, please follow the link and comment on their blog: R – DataKolektiv.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.