“In God we trust, all others must bring data.” — W. Edwards Deming
But what do data scientists really do, and why do organisations need to prioritise having them on their teams?
Data science is a multifaceted field that brings together several skills with one main purpose: turning data into useful information and insights. A data scientist is partly a statistician, partly an analyst and, depending on the role, can also have a strong programming or software engineering background.
Data scientists use programming languages, machine learning, statistical methods and other tools to collect, organise, analyse and visualise data. The field requires advanced technical skills because data science often involves working with large and complex datasets and finding ways to make sense of information that may not be immediately useful or easy to understand.
The title “data scientist” is also quite broad. The role and responsibilities of a data scientist can differ from one industry to another. Someone with a software engineering background working for a science and technology company may have a very different day-to-day role from a data scientist analysing web traffic for an advertising or marketing company. This is one reason why understanding the data science career path can be useful for anyone considering entering the field. There is no single version of what a data scientist does. The work depends heavily on the organisation, industry and problem they are trying to solve.
A Day in the Life of a Data Scientist
A data scientist’s day is primarily project-based. Much of the work involves using data science tools to work collaboratively with a team, with data being used to solve problems, answer questions or simplify processes. Some projects may be research-based, while others may use historical data to find a practical business solution. A data scientist may spend part of the day working with datasets, developing a model, checking results, discussing findings with colleagues or presenting insights to decision-makers.
As the U.S. Census Bureau defines data science, “a field of study that uses scientific methods, processes, and systems to extract knowledge and insights from data.” This definition captures an important part of the work. Data science is not simply about collecting large amounts of information. It is about finding meaning in that information and using it to answer questions. The data a data scientist works with does not always fit neatly into rows and columns. It can come from different sources and in different formats. The challenge is to understand the information, identify what is useful and eventually turn it into something that can support a decision.
The Data Science Life Cycle
The data science life cycle is a systematic process where data is transformed into meaningful insights.
1. Obtaining Data
Data scientists gather data from a variety of sources, including databases, sensors, APIs (application programming interfaces), and online platforms. At this stage, the focus is on making sure there is relevant information to work with. The quality and usefulness of the final analysis depend heavily on the data available at the beginning of the process.
2. Cleaning Data
After data has been collected, it often needs to be cleaned and prepared before it can be analysed. This process is commonly referred to as data cleaning or data wrangling. Data scientists may need to deal with missing values, duplicate records, inconsistent formats, and other errors. They may also need to restructure the data so that it can be used effectively for analysis.
3. Exploring Data
After cleaning the data, the next step is exploratory data analysis (EDA). The goal is to understand the underlying structure of the data, identify patterns, and examine its main characteristics. Data scientists may look for relationships between different variables, unusual results, or trends that require further investigation. This stage can also help determine which questions the data can actually answer.
4. Modelling Data
Next, data scientists can use statistical techniques and machine learning algorithms to explain relationships or predict possible outcomes. The type of model used depends on the objective of the project. For example, predictive analysis could be used to forecast future sales, while clustering could be used to group customers based on their behaviour. The model is not the end goal. It is a tool that helps answer the original question.
5. Interpreting Results
This is an important part of the process because producing a result is not enough. The findings need to be presented clearly so that other members of the team, including stakeholders without a technical background, can understand and use them when making decisions. The purpose of the data science life cycle is ultimately to move from raw information to something useful.
What Are the Core Data Science Techniques?
Depending on the focus and objectives of a project, there are four common techniques used in data science.
1. Descriptive Analysis – Focuses on summarising and describing a dataset using measures such as averages, percentages and frequencies. For example, a retail company could analyse customer data to determine the average amount spent per customer. This helps the organisation understand what has already happened.
2. Diagnostic Analysis – Focuses on understanding why something happened. For example, a data scientist working in the healthcare industry could analyse information to investigate factors that may be contributing to higher patient readmission rates in a particular department. The question here moves from “What happened?” to “Why did it happen?”
3. Predictive Analysis – Uses statistical models and machine learning techniques to estimate what is likely to happen in the future. For example, a company could use historical sales data to forecast future sales or analyse customer behaviour to identify possible future purchasing patterns. Predictions are not guarantees of what will happen. They provide organisations with information that can help them prepare for different possibilities.
4. Prescriptive Analysis – This goes a step beyond prediction by using data insights to recommend possible actions. This can support decisions around strategic planning, resource allocation or personalised customer recommendations. Together, these techniques allow organisations to look at what happened, understand why it happened, consider what may happen next and, in some cases, determine what action could be taken.
Can Data Science Help Companies Navigate Uncertainty? The answer is yes.
During uncertain periods, having reliable and usable data can help organisations understand their options. Businesses can use predictive forecasting and scenario modelling to explore potential outcomes based on different decisions.This does not mean data science can predict the future with certainty. Rather, it can help business leaders understand possible outcomes and make more informed choices when the future is difficult to predict. That can be particularly useful when organisations need to decide how to allocate resources, respond to changing customer behaviour or plan for different business scenarios.
So What Data Scientists Do in a Business?
1. Translating Business Questions Into Analytical Problems
Business leaders and decision-makers often ask questions such as, “How can we reduce losses and increase our profit margin this year?” The ability to understand the business problem is just as important as knowing how to use a technical tool. A technically accurate analysis is not particularly useful if it does not answer the question the organisation actually needs to solve.
2. Gathering, Cleaning and Organising Complex Datasets
Data is often messy when it is first collected. It may contain missing information, duplicates, errors or inconsistencies. Data scientists play an important role in cleaning, organising and preparing datasets so that they can be used for analysis. This part of the work may not always be the most visible, but it is essential. Before an organisation can use data to make decisions, it needs to have confidence in the information being analysed.
3. Identifying Trends, Patterns and Predictive Signals
Once the data is ready, data scientists use different techniques to identify trends, patterns and predictive signals that could help answer the original question. They may rely on statistical modelling, machine learning and predictive analytics to make sense of complex datasets. The goal is not simply to find interesting patterns. It is to identify patterns that can help the organisation understand a problem, anticipate what may happen or make a better decision.
4. Communicating Findings to Leaders and Cross-Functional Teams
A data scientist may have developed a complex model, but if they cannot explain what the results mean and why they matter, the analysis may not be useful to the organisation. Communication is therefore an important data science skill. Data scientists need to be able to explain technical concepts in a clear and practical way so that business leaders and cross-functional teams can understand the findings and use them.
Interested in Building a Career in Data Science?
If you are considering a career in data science, there are different ways to start building the technical and analytical skills required in the field.
Moringa School offers full-time hybrid, full-time remote, and part-time remote learning options through its Data Science Bootcamp and Introduction to Data Science courses.
Explore the programmes and find an option that fits your interests and career goals as you build practical data skills for a changing workplace.
