Level Up 7 Data Science Skills with These YouTube Channels
Updated: Feb 1
What If Learning Data Science is a Game
We are all familiar with the modern game design that champions or heroes are always equipped with certain attributes and specialties. For example, Dota heroes are scored on the aspects of agility, intelligence, and strength. To excel in the battlefield, the hero needs to have above-average scores among all attributes while additionally specialized in at least one.
So what if we think of learning data science as playing a game where all of us possess multi-dimensional abilities. Playing this game demands constantly sharpen our skillset with weapons, trainings or magic potions which resembles learning through reading, tutorials, and of course those YouTube learning resources mentioned later on.
First of all, let's walk through these seven essential skills that guarantee your success in the data science field.
1. Machine Learning Algorithms Understand the underlying theory behind supervised, unsupervised, and reinforcement learning algorithms, such as:
2. Statistics & Math Stats and math are the building blocks of data science, especially in machine learning and AI, including fundamental knowledge of:
hypothesis testing: t-test, ANVOA, correlation
3. SQL SQL is the language used to communicate with the database and derive insights through data extracts and queries, some essentials techniques include:
CRUD - create, read, update, delete
filter, sort, aggregate
date, string, number manipulation
join and union
If you would like to get into more details, these articles may help:
4. Programming There are some easy to start yet powerful programming languages such as Python and R. Instead of focusing on the coding syntax, more importantly, is to learn the programming logics as well as the developer mindset:
loop structure: for loop, while loop
conditional structure: if ... else statement
data structure and complexity
5. Data Visualization Data Visualization is embedded throughout the data science journey, from the exploratory data analysis in the beginning to the final reporting and deliverables. Some commonly used tools are:
seaborn (Python package)
ggplot2 (R package)
6. Project Implementation Understanding the theory and concepts is crucial, but implementation is also imperative. This skill is more focused on how to put knowledge into practice by building projects and implement the data science lifecycle:
7. Storytelling This is the soft skill that is often neglected. This determines whether the data scientists are capable of conveying the message succinctly to their audiences while also keeping them engaged. Since data science is building the bridge between business and technology, being able to articulate complex techniques and processes to people from various disciplines becomes an essential skill to have.