Data Science Concepts

5 Fundamental Data Science Concepts

Table of Contents

Data Science, Data Analytics and Business analytics are complex subjects that interweave a myriad of concepts from maths, statistics, programming, computing and management level skills. This article covers 5 fundamental Data Science Concepts.


Data Science Concept #1: Machine Learning

Machine Learning is a branch of Artificial Intelligence that works by programming a system to automatically perform a specific task. The system then self-learns from data, performs pattern recognition and make decisions, with little to no human intervention.

With relation to Data Science, it is use to build predictive models. As a system is exposed to new data, the machine learning algorithm is able to independently process and adapt it to predict outcomes more accurately. In addition, these predictions are not only based on new data, but also all the previous computations that were run. Machine Learning is the crux of managing and working with Big Data.


Data Science Concept #2: Algorithms

Algorithms are a specific set of rules or processes used in a calculation to solve problems or perform a task. The simplest algorithm for example, is a recipe – a set of rules to follow to get a specific outcome.

In Data Science, many data models and data analysis are accomplished with the use of algorithms. These can either be automated to self-learn, as in the case of Machine Learning, or simple Macros applied to excel sheets in oder to generate results based on the data provided.


Data Science Concept #3: Statistical Models

Statistical Models are mathematical models that specify relationships between random and non-random variables. It is the process of analysing datasets by mathematically representing observed data in order to make inferences from the samples provided. This Data Science Concept is a crux of this field. Models can be used to extract information or predict probable outcomes, based on available data.

Statistical models can be considered as statistical assumptions, allowing Data Scientists to calculate the probability of an event occurring. A simple example of one such model is predicting the probable outcome of a dice roll.


Data Science Concept #4: Regression Analysis

Regression analysis is a statistical process that estimates relationships between a dependent variable and independent variables, to provide a real number value representing a quantity on a line. For example, temperature, sales turnover etc.

It is used in Data Science for statistical modelling, to find trends in the data to predict or forecast specific behaviours. For example, forecasting monthly sales trend for the year based on past and current data. Regression analysis substantially overlaps with the Machine Learning field.


Data Science Concept #5: Programming

Computer Programming languages are used to develop and build the models used in Data analysis. In addition to the this, programming can also be used to clean data, organise data and help visualise data in understandable formats for stakeholders.

Commonly used programming languages in Data Science are Python, R for Statistics as well as SQL to help with database management and creation.

Business Analytics however, or individuals who specialise only in data interpretation and analysis do not often study these languages.



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5 Data Science Fundamentals
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