Fundamental 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.


What is Data Science?

Data science is a scientific field that utilises structured and unstructured data and manipulates this data through different processes and algorithms aiming to extract purpose specific knowledge.


The Data Science Lifecycle

You can have the same set of data that is being analysed by different industries for different purposes or projects resulting in specific sought after knowledge, the followed data science lifecycle is largely similar.

The structure consists of data collection and storage, data preparation, exploration and visualization, experimentation and prediction, and data Storytelling and communication.

The aim is to be able to take data that is very large and / or incomprehensible to the majority of people, analyse it and present it in a non-technical way comprehensible to your stakeholders for them to use in their decision-making process.


Why is Data Science Important?

From the dawn of the internet, generated data grew exponentially. Whether you are researching online, buying on line or socialising online, every click, pause or stop generates data. Depending on the industry, your data, and the data of people similar to you is extremely valuable to companies who can use this data to better understand customer preferences, buying habits which helps these companies make better business decisions.

This has created huge demands for data science professionals prompting universities to invest in programmes such as the bachelor’s or master’s degree in data science.


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.


What is Data Science Used For?

The overall aim of data science is to help business and industries see the bigger picture and make better decisions. This is done through different analytics mainly descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.


Difference between Data Scientist, Data Analyst, and Data Engineer

With a degree such as a master’s degree in data science, different career options would be available to you. Here is how each would describe their jobs:

As a data scientist, I dive into an organisation’s data to extract and convey meaningful insights. My profound understanding of machine learning workflows and their application to real-world business scenarios guides me as I predominantly work with coding tools, conducting in-depth analyses, and frequently engaging with big data tools.

In my role as a Data Analyst, I assume a vital position in interpreting an organisation’s data. My expertise in mathematical and statistical analysis equips me to transform intricate datasets into actionable insights that drive business decisions. Using data visualisation tools, I effectively communicate findings to both technical and non-technical stakeholders.

Data Engineers, like me, serve as architects. I design, construct, and manage data infrastructure, facilitating efficient data analysis for Data Scientists. My focus spans data collection, storage, and processing, where I establish data pipelines that streamline the analytical process.



Stafford offers Postgraduate Online Data Science courses. Contact a Higher Education Consultant for a personal evaluation. Available Data Science Degrees are;


5 Data Science Fundamentals
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