A thread: How I prepared for a #tech role in #FAANG—
1. Technical preparation of #python and #SQL
2. Behavioral and cultural screen with @Latesha_Byrd
3. Deep dive on department domain and current tools and techniques Technical preparation included practicing @LeetCode and @CodeSignalCom for #SQL and #python. I started to time myself and also spoke out loud what I was doing and why to get familiar with #whiteboarding etiquette. I also had my friends mock interview me.

Why mid-sized companies are behind the #datascience curve: A thread—
Mid-sized companies are behind in terms of being data-driven when compared to startups and large entities. There are several reasons for this which puts them at a disadvantage. 1/ The average age of a mid-sized company is c years. X years older than the average startup and large entities such as Facebook or Apple. Overall, their workforce is older as well which translates into greater domain knowledge and also little technical capability.

Transitioning into #DataScience: A (long) thread—
Transitioning into a new field can be cumbersome, especially #datascience which usually requires some level of #math #statistics #machinelearning #programming. But fret not! This thread will go over the fundamentals of what you should know to become a #datascientist. First thing first, there are hundreds of tools for visualization (Power BI, Tableau, Dash), cloud computing (GCP, AWS, Azure) and SQL variants (Postgres, MSSQL, MySQL).

I’m amazed by how many transitioning into #datascience aren’t aware of the specialities that exist within the field. Careers within #datascience - A thread: 1/ These are the main careers within #datascience based on my experience and which will likely continue to evolve.
1. Product Analytics DS
2. Generalist DS
3. Specialist (NLP, Computer Vision)
4. ML Researcher
5. DataOps Engineer
6. DS Product Manager
7. Data Vizualizer/CC