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The ABCs of Data Science: 26 of the Most Important Concepts
From artificial neural networks to Z-scores
Data science has become one of the fastest growing fields in the world. The term itself has grown to celebrity status in the language of tech, and more and more people are getting interested and want a piece of that pie.
To learn data science, however, takes time — there are many concepts and practices one must know first before even taking a slice.
Using the letters of the alphabet, I’m briefly going to go over 26 of the most important concepts in data science.
With that being said, lets start with A.
Artificial Neural Networks
Computing system designed “to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner.”
Bayes Theorem
Very popular mathematical formula for determining conditional probability. Conditional probability is the probability of an event based on the prior knowledge of conditions.
Classification
Process of predicting the class of given data points. In other words mapping input variables (x) to a discrete variable (y).
An example would be predicting if a person bought a specific product given variables like age, salary, or gender.
Decision Trees
Treelike model used to map the possible outcomes of every decision. Each branch of the decision tree represents a possible outcome or decision with the farthest branches being the end results/final decision.
Ensemble Learning
Combining weak models (weak learners) together to obtain a predictive model that’s more accurate than any of the added models alone.
F-Score
Type of measurement that analyzes the accuracy of a model. The F-score combines the precision and recall…
