What is Data Science? A Comprehensive Guide for Beginners

Curious about data science? This beginner’s guide breaks down the basics of what data science is, how it works, and why it’s important in today’s world.

A MacBook with lines of code on its screen on a busy desk

Photo by Christopher Gower on Unsplash


At its core, Data Science aims to derive actionable insights and make informed decisions based on data-driven evidence. It involves employing various tools and technologies to collect, clean, and analyse large volumes of data from diverse sources, such as databases, social media, sensors, and more. As a Data Scientist you will apply advanced statistical and computational techniques such as linear regressions and hypothesis testing to uncover hidden patterns, predict future outcomes, and solve complex problems be it for a business or a public institution.

Data Science is an interdisciplinary field that combines Statistics, Mathematics and Computer Science to extract meaningful insights and knowledge from structured data. This can include data in excel files and unstructured data such as audio and video files. It has grown in popularity in recent years, as we can see from the search trends on Google from the past five years.

codeop what is data science

Interest over time based on Google searches of the term ‘Data Science’; Source: Author

Note: Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term.


In this beginner’s guide, we’ll explore the basics of data science, including its applications, tools, and importance in today’s world. Specifically, we’ll cover the following topics along the way:


What is Data Science?

Data science involves studying data to extract meaningful insights.The main goal of data science is to uncover patterns, trends, and insights that you can use to inform decision-making and drive innovation in an organisation.The data science process typically involves several key steps-

Problem Definition 

This involves clearly defining the problem or question you want to address through data analysis. It is essential to understand the objective, scope, and constraints of the project.

Data Collection 

In this step, relevant data is gathered from various sources, such as databases (a systematic collection of data stored electronically), surveys or APIs (Application Programme Interfaces). These are an accessible way to extract and share data within and across organisations . Your data should be comprehensive, accurate, and representative of the problem at hand.

Data Preparation

This step involves cleaning and preprocessing the collected data. It includes handling missing values, removing duplicates, handling outliers, and transforming data into a suitable format for analysis.

Exploratory Data Analysis (EDA)

EDA is performed to gain insights into the data and understand its characteristics. This includes visualising data, identifying patterns, correlations, and outliers (data points which are in the extremes compared to most of the other data points), and conducting statistical analyses. This involves investigating the patterns and trends in the data.

Feature Engineering

Feature engineering involves selecting, creating, or transforming variables (features) from the raw data that can improve the performance of machine learning models. It may include feature selection, redefining categorical variables and creating new features.

Model Development

In this step, machine learning models or statistical algorithms are selected and developed based on the problem and data. This can include techniques such as regression, classification, clustering, or deep learning.

Model Evaluation

The developed models are evaluated using appropriate performance metrics and validation techniques (i.e. techniques which help understand if the models perform well with new data). This step helps assess the model’s accuracy, robustness, and generalisability.

Model Deployment

Once you identify a satisfactory model, you deploy it in a real-world setting. This involves integrating the model into existing systems or creating an application or dashboard to make predictions or generate insights.

Model Monitoring and Maintenance

Models need to be monitored and updated regularly to ensure their performance remains optimal. This includes tracking model accuracy and retraining models when new data becomes available.

Communication and Visualisation

Throughout the entire process, effective communication of findings and insights is crucial. You should use visualisations, reports, and presentations to communicate the results to stakeholders in a clear and understandable manner.

By following this process, data scientists can turn raw data into actionable insights that can drive business decisions and improve outcomes.


What do data scientists do?

Data Scientists play a crucial role in the field of data science. As a data scientist, you will use a variety of tools and techniques, including statistical analysis, machine learning, and data visualisation, to extract meaning from data in your day-to-day role and help solve business problems. You will also work closely with other teams within the organisation, such as business analysts and data engineers, to ensure that data is collected and analysed in a way that is accurate, reliable, and useful. In addition to technical skills, as a Data Scientist you should be able to demonstrate communication and problem-solving abilities. You must be able to effectively communicate the findings to non-technical stakeholders and collaborate with team members effectively.

Sometimes the role of data scientists can be confused with other roles such as data analysts, machine learning engineers and data engineers.Here are some key differences between them:

Data Analyst

A data analyst is someone who is responsible for analysing data and helping to make business decisions. They usually have a background in statistics or mathematics. A data analyst might use their skills to help a company decide which products to stock in their stores, or how to price those products.

Data Scientist

A data scientist is someone who is responsible for analysing data and extracting insights from it. They usually have a background in computer science or machine learning. A data scientist might use their skills to help a company understand how their customers are using their products, or to predict which products a customer is likely to buy in the future.

Data Engineer

A data engineer is someone who is responsible for designing, building, and maintaining data systems. They usually have a background in computer science or software engineering. A data engineer might use their skills to help a company build a data warehouse, or to create a data pipeline that ingests data from multiple sources.

Machine Learning Engineer

A Machine Learning Engineer is someone who researches, builds, and designs self-running software to automate predictive models. An ML Engineer builds artificial intelligence (AI) systems that leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions. Think of this as the person who helped build ChatGPT!

CodeOp How much time each role typically spends on different tasks, and the overlap of skills

How much time each role typically spends on different tasks, and the overlap of skills; Source: Data Captains


What are the Techniques, Tools and Technologies used in Data Science?

Data scientists use different types of analysis to extract insights from large datasets. These can be broadly categorised as follows:

Descriptive analysis

One important aspect of data science is descriptive analysis, which involves describing the data to gain insights into what happened or what is happening in a particular environment. For example, you might look at the distribution of your data to understand what the mean, median or mode looks like. Or what the extreme points which lie further away from the average data point tell you about your data. This can be done through various data visualisations such as pie charts, bar charts, line graphs, tables, or generated narratives. 

For example, a flight booking service may use descriptive analysis to reveal booking spikes, booking slumps, and high-performing months based on the number of tickets booked each day.

Diagnostic analysis

Another technique is diagnostic analysis, which involves taking a deep dive into data to uncover the reasons behind certain events or patterns. This can involve using methods like data mining i.e. extracting information from data to understand correlations i.e. how different variables are associated with each other, and trends. 

For example, a flight service might use diagnostic analysis to better understand a spike in bookings during a particular month, which could lead to the discovery that many customers are travelling to attend an annual music festival in a specific city.

Predictive analysis

Data science is a field that involves using historical data to make predictions about future patterns. This is done through a variety of techniques, including machine learning algorithms and time-series forecasting. By analysing data and identifying causal connections to understand why any event has occurred, computers can be trained to make accurate predictions about future events. 

For example, a flight service team might use data science to predict booking patterns for the coming year, allowing them to anticipate their customers’ travel needs and target their advertising accordingly.

Prescriptive analysis

Prescriptive analytics takes predictive analytics a step further by not only predicting outcomes but also recommending the best course of action to take in response. This involves using a variety of advanced techniques, such as graph analysis, simulation, neural networks, and recommendation engines from machine learning.

For example, after understanding the travel patterns of a customer and predicting where and when they might want to travel next, a flight company can start to recommend potential holiday options to the user to get them to book a flight with them.

In order to perform the above types of analysis, data scientists rely on a number of specialised tools and programs developed specifically for data cleaning, analysis, and modelling. These include:

  • SQL – to extract and transform data from databases
  • Python and R – , programming tools which help with data cleaning, analysis and modelling
  • Jupyter Notebook – which helps to write code 
  • Tableau and Power BI – visualisation tools
The visual shows which tools to use for data storing (MongoDB, MYSQL, etc.), transforming (Spark, Python, SQL), modeling (Pandas, Spark etc.) visualizing (R ggplot 2, DB etc.) and other tools (Kafka).

Different tools you can expect to use as a  Data Scientist; Source: AI Multiple


How do businesses use data science?

By examining data based on numbers, statistics and facts, data science in business helps to solve business problems. Analytics tools can be used to generate predictive models simulating a wide range of possible outcomes in various situations. For example, if a business identifies five potential ways to grow revenues, data science can predict the way that is most likely to work and presents the lowest level of risk. Here some ways in which businesses use data science in real life-

Discover unknown transformative patterns

By analysing large amounts of data, data scientists can identify patterns and trends that might not be immediately apparent, and use this information to make informed decisions. 

For example, an accommodation booking company might use data science to identify inefficiencies in their customer service operations when dealing with booking cancellations, and implement changes such as a chatbox with frequently asked questions that lead to increased revenue and customer satisfaction.

Innovate new products and solutions

By leveraging data science, businesses can gain a deeper understanding of their customers, operations, and market trends such as trends in customer preferences. 

For example, an online fashion company can use data science to analyse customer feedback on social media on their promotional offers and identify areas for improvement in their messaging. This can lead to the development of innovative solutions that improve customer satisfaction and drive business growth.

Real-time optimisation

Data science has become increasingly important in recent years as businesses seek to gain a competitive advantage by leveraging the vast amounts of data they collect to provide optimal service to the customers in real time. By using data science techniques, companies can identify patterns and trends in their data, make predictions about future events, and optimise their operations to improve efficiency and reduce costs. 

For example, a shipping company might use data science to optimise their routes in real time by adapting to changing economic situations, reduce downtime, and improve their overall performance


What is the role of data science in the future?

Data science is becoming increasingly important in today’s world as more and more industries are relying on data-driven insights to make informed decisions. From healthcare to finance to marketing, data science is being used to analyse large data sets and uncover valuable insights that can help organisations improve their operations, increase efficiency, and drive innovation. With the explosion of data in recent years, the demand for skilled data scientists has also increased, making it a lucrative and rewarding career path if you have a passion for data analysis and problem-solving. As the amount of data continues to grow, the importance of data science in today’s world will only continue to increase.

On top of that, with the growing use of AI in day-to-day use, data science and AI are becoming increasingly important for businesses. Data scientists play an important role in AI development. They’re creating algorithms that will learn patterns and correlations in the data, which can be used by AI to build predictive models for generating insights out of them. Data scientists, too, are using AI as a tool for data understanding and information on business decisions. The future of data science will see it evolve as the field adapts to the new technologies on the rise. But one thing is for sure, data science is here to stay!



What is data science in simple words?

Answer: Data science is the study of data. It involves using statistical and computational methods to extract insights and knowledge from data. Data scientists use a variety of tools and techniques to analyse data, including machine learning, data mining, and predictive analytics. The goal of data science is to uncover patterns and insights that can be used to make better decisions and improve outcomes.

What does data science actually do?

Answer: Data science involves using statistical and computational methods to extract insights and knowledge from data. Data scientists work with large and complex data sets to identify patterns, trends, and relationships that can be used to inform business decisions and solve problems. They use a variety of tools and techniques, including machine learning, data visualisation, and predictive modelling, to analyse data and generate insights.

Does data science require coding?

Answer: Yes, coding is an essential skill for data scientists. While there are some tools and software that allow for more drag-and-drop style data analysis, the majority of data science work involves writing and executing code in languages such as Python, R, and SQL.

What is data science, machine learning and artificial intelligence?

Answer: Data science is the field of study that involves extracting insights and knowledge from data. Machine learning is a subset of data science that involves building algorithms that can learn from data and make predictions or decisions. Artificial intelligence (AI) is a broader term that encompasses machine learning and other techniques that enable machines to perform tasks that typically require human intelligence, such as natural language processing and computer vision.