1 hr 1 min
#40 Becoming a Data Scientist DataFramed
-
- Technology
Hugo speaks with Renee Teate about the many paths to becoming a data scientist. Renee is a Data Scientist at higher ed analytics start-up HelioCampus, and creator and host of the Becoming a Data Scientist Podcast. In addition to discussing the many possible ways to become becoming a data scientist, they will discuss the common data scientist profiles and how to figure out which ones may be a fit for you. They’ll also dive into the fact that you need to figure out both where you are in terms of skills and knowledge and where you want to go in terms of your career. Renee has a bunch of great suggestions for aspiring data scientists and also flags several important pitfalls and warnings. On top of this, they'll dive into how much statistics, linear algebra and calculus you need to know in order to become an effective data scientist and/or data analyst.
Links from the show
FROM THE INTERVIEW
Becoming a Data Scientist (Renée's Blog)
Renée's Twitter
Data Sci Guide (Data Science Learning Directory)
FROM THE SEGMENTS
Statistical Distributions and their Stories (with Justin Bois at ~19:20)
Justin's Website at Caltech
Probability distributions and their stories
Programming Topic of the Week (with Emily Robinson at ~43:20)
Categorical Data in the Tidyverse, a DataCamp Course taught by Emily Robinson.
R for Data Science Book by Hadley Wickham (Factors Chapter)
Inference for Categorical Data, a DataCamp Course taught by Andrew Bray.
stringsAsFactors: An unauthorized biography (Roger Peng, July 24, 2015)
Wrangling categorical data in R (Amelia McNamara & Nicholas J Horton, August 30, 2017)
Original music and sounds by The Sticks.
Hugo speaks with Renee Teate about the many paths to becoming a data scientist. Renee is a Data Scientist at higher ed analytics start-up HelioCampus, and creator and host of the Becoming a Data Scientist Podcast. In addition to discussing the many possible ways to become becoming a data scientist, they will discuss the common data scientist profiles and how to figure out which ones may be a fit for you. They’ll also dive into the fact that you need to figure out both where you are in terms of skills and knowledge and where you want to go in terms of your career. Renee has a bunch of great suggestions for aspiring data scientists and also flags several important pitfalls and warnings. On top of this, they'll dive into how much statistics, linear algebra and calculus you need to know in order to become an effective data scientist and/or data analyst.
Links from the show
FROM THE INTERVIEW
Becoming a Data Scientist (Renée's Blog)
Renée's Twitter
Data Sci Guide (Data Science Learning Directory)
FROM THE SEGMENTS
Statistical Distributions and their Stories (with Justin Bois at ~19:20)
Justin's Website at Caltech
Probability distributions and their stories
Programming Topic of the Week (with Emily Robinson at ~43:20)
Categorical Data in the Tidyverse, a DataCamp Course taught by Emily Robinson.
R for Data Science Book by Hadley Wickham (Factors Chapter)
Inference for Categorical Data, a DataCamp Course taught by Andrew Bray.
stringsAsFactors: An unauthorized biography (Roger Peng, July 24, 2015)
Wrangling categorical data in R (Amelia McNamara & Nicholas J Horton, August 30, 2017)
Original music and sounds by The Sticks.
1 hr 1 min