Read about 5 fundamental Data Science Concepts to the study of CRISP-DM, complex subject Machine Learning, Data Models, etc Learn more: https://www.staffordglobal.org/articles-and-blogs/data-science-articles-and-blogs/fundamental-data-science-concepts/

1. 2: Statistical Models
Mathematical models that specify relationships
between random and non-random variables to
make inferences. They are assumptions used to
calculate the probability of an event occurring,
such as predicting the outcome of a dice roll.
3: Regression Analysis
A statistical process that estimates relationships
between dependent variable & independent
variables, to provide a real number value
representing a quantity on a line. For example,
temperature, sales turnover etc. t is used for
statistical modeling, forecasting etc.
4: Machine Learning
AI programming of a system to automatically
perform a specific task, self-learn from the data
collected, perform pattern recognition & make
decisions, with little to no human intervention. It is
use to build predictive models.
1: Algorithms
A specific set of rules or processes used in a
calculation to solve problems or perform a task.
For example, a recipe – a set of rules to follow to
get a specific outcome. It is used for data analysis
and data modeling, among other functions.
5 Data Science
Fundamentals
5: Programming
Programming languages are used to develop and
build data models, clean and organise data and
visualise the data. Popular languages are Python,
R and SQL.
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