Data science is one of the hottest fields today, but many people still don’t understand what data scientists actually do. As a data scientist myself, I get asked this question a lot! So in this post, I’ll explain the key responsibilities of a data scientist and what a typical day in this job looks like.
What is Data Science?
Data science is a practical discipline that deals with the study of methods for generalized knowledge extraction from data. It consists of various components and is based on methods and theories from many areas of knowledge, including signal processing, mathematics, probabilistic models, machine and statistical learning, programming, technology, pattern recognition, learning theory, visual analysis, uncertainty modelling, warehousing. and high-performance computing to extract meaning from data and create data products.
A Day in the Life of a Data Scientist
My mornings usually start by checking for any urgent requests from stakeholders in the business. As data scientists, we aim to enable data-driven decision making across the company. So if a key decision needs to be made, I may need to quickly analyse some data to provide insights.
Next, I’ll check in with my team to see if we need to prioritise any particular projects or tasks for that day. We manage multiple projects at once, so we need to stay coordinated.
The rest of my day is then spent on some combination of the following core data science tasks:
Meetings and Consultation
A decent chunk of a data scientist’s time is spent in meetings or informal consultation with business teams. I might meet with product managers to understand what metrics they want optimised, or with software engineers to advise on which data models to integrate.
These meetings ensure our work is aligned with wider business objectives. I take time to understand the problems different teams face and think of ways data science could provide solutions.
Data Collection and Management
Before we can analyse data, we first need access to clean, structured data! A big part of my day can be spent on:
- Identifying valuable data sources, whether from internal databases and systems or external providers.
- Writing scripts to automate data collection processes.
- Cleaning and formatting raw data so it’s usable for analysis.
- Structuring and integrating data from diverse sources into a queryable database or data warehouse.
- Developing processes, systems and tools to manage data pipelines and storage.
High-quality analysis requires high-quality data, so this step is crucial!
Exploratory Data Analysis
Once I have the relevant data, I dive in to start uncovering insights. Exploratory data analysis involves:
- Using SQL, Python or other tools to query and manipulate the data.
- Creating visualisations, charts and graphs to better understand data relationships.
- Statistical analysis and modelling to discover patterns, trends and anomalies.
- Formulating hypotheses based on exploration and subject matter expertise.
- Identifying areas that need further investigation.
This exploration guides which models or analyses to pursue next. I might uncover that website conversion rates drop on weekends, or that a certain product drives higher customer loyalty.
Model Building and Statistical Analysis
Based on the insights from exploration, I develop models and run in-depth analysis to create value from our data. Common tasks include:
- Building machine learning models to make predictions, classifications or recommendations.
- Programming algorithms for optimisation, natural language processing or other advanced techniques.
- Running A/B tests to guide business decisions.
- Performing regression analysis to understand relationships between variables.
- Calculating statistical summaries on metrics, demographics or other business data.
The analysis depends on the problem we are trying to solve. The key is applying robust statistical thinking to get valid, useful results.
Results Interpretation and Reporting
Analytics don’t create value until they’re understood! An important aspect of my day is interpreting analysis results and communicating insights to stakeholders. Steps include:
- Interpreting what the data is telling us and deriving insights.
- Identifying key takeaways, as well as limitations or caveats of the analysis.
- Translating technical results into actionable recommendations.
- Creating compelling data visualisations and presentations.
- Writing reports, blog posts or emails to share findings across the business.
I tailor communication based on each audience’s technical background and goals. Effective interpretation ensures stakeholders actually utilise the insights.
Tool and Product Development
Beyond core analysis, data scientists also create tools to make data analysis easier for others. Examples include:
- Building interactive dashboards and data apps for internal teams.
- Developing APIs, packages and modules to share useful code.
- Automating workflows through scripts and macros.
- Contributing algorithms and models to broader data platforms.
- Prototyping new data products as proofs of concept.
While not every day involves this, developing useful tools and products is a fun creative outlet and enables democratisation of data science across an organisation.
Learning and Development
In such a rapidly changing field, learning is an every day activity in this role. Time is often dedicated to:
- Reading academic papers, blogs and news to stay on top of latest techniques.
- Learning new tools through online courses and documentation.
- Attending conferences and meetups to connect with the broader data community.
- Setting aside dedicated time for practicing new skills through online platforms like Kaggle.
- Writing blog posts and analyses to consolidate knowledge.
Making learning a consistent habit is key to thriving as a data scientist.
The actual mix varies day-to-day, but those are the core components that encompass the life of a data scientist! It’s a diverse, fast-paced role filled with analytical thinking, coding, data visualisation, presentations and everything in between. There’s never a dull day when you get to uncover insights from data.
Skills and Background
Now that you know what the job involves, what skills and background do data scientists need to succeed?
First and foremost, you need a strong foundation in maths, statistics and programming. Common required skills include:
- Maths – Statistics, calculus, linear algebra and other advanced maths
- Statistics – Statistical testing, modeling, experimental design
- Programming – Python, R, SQL, C/C++
- Machine Learning – Algorithms like regression, classification and neural networks
- Data Visualisation – Using tools like Tableau, ggplot2 and D3.js
- Big Data – Working with distributed systems like Hadoop and Spark
Solid training in computer science, statistics or a quantitative field provides the necessary technical skills. But data science roles vary in the extent they utilise each skill, so you don’t need to master every last language and algorithm.
Communication and “Soft” Skills
Technical chops alone won’t make you an effective data scientist. You also need:
- Communication skills – Translating complex insights into clear explanations and visuals.
- Creativity – Approaching problems in innovative ways. Designing engaging analysis and deliverables.
- Business acumen – Understanding how your role adds value to the organisation. Thinking critically about analysis results.
- Collaboration – Cooperating with diverse teams of engineers, managers and executives.
A natural curiosity and intellectual rigor also goes a long way. Data scientists aren’t just number crunchers – they think critically about real world problems. An empathetic, user-focused mindset separates good from great.
Education and Experience
A master’s degree in a quantitative field like data science, statistics or computer science is common, though some data scientists come from other backgrounds like economics or the social sciences. Useful university training includes coursework in:
- Data visualisation
- Machine learning
- Mathematical statistics
Beyond academics, previous experience in data analysis or software engineering roles helps prepare for the practical side of things. Internships in data analytics or related fields provide valuable early exposure.
Overall, data scientists hone an interdisciplinary mix of technical data smarts and business communication abilities. A curious, analytical mindset and passion for learning are key.
Why Consider a Career in Data Science?
If breaking down data to uncover hidden insights sounds exciting, a data science career may be rewarding for you! Here are a few key advantages:
Data science is one of the hottest career paths today. Demand for data scientists far exceeds supply, and roles span virtually every industry. It’s projected to be one of the fastest growing jobs over the next decade.
That high demand also comes with lucrative salaries. Data scientists earn a median salary of over £55k in the UK, among the highest of tech roles. Salaries at top companies or with senior titles can exceed £100k.
Diverse Problems to Solve
Every company has unique data challenges waiting to be solved. As a data scientist, you get exposed to an immense diversity of real world problems compared to more niched scientific fields. The variety keeps things exciting.
Data scientists create huge value by guiding business strategy and decisions through analytics. The insights you uncover and share can have profound impacts on your organisation’s performance and offerings.
With innovations in AI and Big Data, the field is constantly advancing with new techniques and tools. You’ll never stop learning new skills, technologies and approaches throughout your career.
If you’re curious to leverage data for real impact while constantly challenging yourself, data science offers a phenomenal career path. The mix of technical rigor, business value and creativity provides endless opportunities to grow.
- Data scientists utilise a diverse mix of technical skills, communication ability and business acumen to extract insights from data.
- Core responsibilities include data collection, analysis, modeling, optimisation, visualisation and reporting.
- Strong maths, statistics and coding skills are required, along with an analytical mindset.
- High demand has created competitive salaries but limited talent supply.
- The role offers variety, learning opportunities and significant positive impacts.
What degree do you need to become a data scientist?
While not required, most data scientists have at least a master’s degree in a quantitative field like data science, computer science, statistics or mathematics. Relevant coursework in programming, algorithms, databases, statistics and machine learning provides important foundations. Degrees specifically in data science are becoming more common.
What programming languages are best to learn?
What maths skills are required?
A strong grasp of statistics, calculus, linear algebra and multivariate calculus allows you to understand machine learning algorithms and advanced statistical modeling. Mathematical maturity and quantitative problem solving skills are key.
Do you need to be good at data visualisation?
Strong visualisation and presentation skills are highly valuable for communicating complex results. Familiarity with tools like Tableau, Matplotlib, ggplot2, Power BI and D3.js allows you to create compelling visuals and dashboards.
How much does a data scientist make?
Median salaries are over £55k, with senior or management roles exceeding £100k. At top tech firms salaries can reach £200-300k. Location, years of experience and specific industry impact salary ranges. But data science is among the highest paid tech roles.
How long does it take to become a data scientist?
From scratch, expect around 2-3 years to gain necessary master’s degree qualifications, learn technical skills and get initial experience. Many data scientists start in related roles like data analyst before transitioning. Lifelong learning is required to stay current.
What industries hire data scientists?
Every knowledge-based industry hires data scientists today, including tech, finance, healthcare, retail, government, manufacturing, transportation and more. Any organisation that makes data-driven decisions needs talent to turn data into value.