In a previous post, I showed you how to use AWS CDK to automate the deployment and configuration of your Apache Airflow environments using Managed Workflows for Apache Airflow (MWAA) on AWS. In this quick how-to guide, I will share how you can use Terraform to do the same thing.
You will need:
Facebook and Twitter have left most other companies around the world far behind when it comes to using machine learning to improve their business model. And while their practices haven’t always resulted in the best reactions from end-users, there’s much to be learned from these companies on what to do–and what not to do–when it comes to scaling and applying data analytics.
While Facebook seemingly uses machine learning for everything — it is used for content detection and content integrity, sentiment analysis, speech recognition, and fraudulent account detection, as well as operating functions like facial recognition, language translation, and content search functions. The Facebook algorithm manages all this while offloading some computation to edge devices in order to reduce latency.
A data scientist extracts manipulate and generate insights from humongous data. To leverage the power of data science, data scientists apply statistics, programming languages, data visualization, databases, etc.
So, when we observe the required skills for a data scientist in any job description, we understand that data science is mainly associated with Python, SQL, and R. The common skills and knowledge expected from a data scientist in the data science industry includes – Probability, Statistics, Calculus, Algebra, Programming, data visualization, machine learning, deep learning, and cloud computing. Also, they expect non-technical skills like business acumen, communication, and intellectual curiosity.
