What is data science?

We humans have always produced data – from cave paintings and cuneiform inscriptions to bookkeeping ledgers, tax declarations, or election results. But now data has become ubiquitous. We produce it when we fill out a form, make an online purchase, sign a petition, book a room for our upcoming holiday, stream a movie,  make a payment at our local grocery store, send out an email, or even share a picture with a loved one.

All of this data is being collected and analysed so that businesses and governments, but also doctors, academics and engineers can make sense of the data and take action as deemed appropriate. 

Collecting and cleaning data (i.e. preparing data for analysis), using statistical methods and increasingly more often machine learning and AI to analyse it and create predictive models is the daily bread of a data scientist. This is why data scientists are in high demand today by companies all over the world.


Who is a data scientist?

Basically, a data scientist is someone who uses statistical and computational methods to extract insights and knowledge from large, complex data sets. Oftentimes, this is someone with a background in mathematics, statistics, and computer science, but really their background is less relevant than having a keenly analytical mind and possessing the right skill set.

A data scientist needs to be – first and foremost – capable of applying statistical analysis, machine learning, and other advanced techniques to the process of solving complex problems and making predictions based on data. In the world of finance, for instance, a data scientist can help business leaders understand their market together with its needs and expectations so they can make smart decisions. Or, they can help a logistics company better understand the challenges they face and suggest ways to improve efficiency and cut costs. But data science doesn’t stop there.

  • In healthcare, data science is widely used to improve patient outcomes, reduce costs, and optimise the delivery of care by analysing patient data to identify risk factors for certain diseases and creating personalised treatment plans based on a patient's medical history and genetic profile.
  • In education, data science can be used to improve the quality of education by analysing student data to identify areas where students are struggling and create targeted interventions to address those areas.
  • Data science is used to improve public safety by analysing crime data to identify patterns and predict where crimes are likely to occur so those in charge can allocate resources more effectively and can also target high-risk areas with increased patrols.
  • Data science is used to optimise energy consumption and reduce waste by analysing energy usage patterns and identifying areas where energy is being wasted.
  • In transportation, data science is used to improve transportation systems by analysing traffic patterns and predicting travel times. This helps city officials and operators optimise traffic flow and reduce congestion, resulting in shorter travel times and lower emissions.


What’s the difference between a data scientist and a data analyst?

Many people might confuse data scientists with data analysts, so a clarification is in order: Data analysts typically work with smaller datasets and focus on descriptive analysis, which involves summarising and interpreting data to identify patterns, trends, and insights. On the other hand, data scientists work with much larger and more complex datasets, and are responsible for both descriptive and predictive analysis. To this end, they make use of machine learning and other advanced techniques to develop models that can be used to make predictions or solve complex problems.


What does one need to know to be a data scientist?

Becoming a data scientist requires a combination of education, technical skills, experience, and communication skills. Now, while experience cannot be learned, there are many things one can do to come within reach of a career in data science. A good first step is mastering the key technical skills used in data science. This includes a good grasp of the following:

  • Python: This is a popular programming language for data science because of its simplicity, versatility, and powerful data analysis libraries.
  • SQL (Structured Query Language): This is a standard language used to manage and query relational databases, which are often used to store and manage large datasets.
  • Git: This is a version control system that is widely used in software development to track changes to code and collaborate with others. It is also useful for managing and tracking changes to data science projects.
  • Machine Learning Frameworks: These allow you to build and train machine learning models.


What else makes a good data scientist?

While technical skills such as using programming languages and data tools are important, there are also several non-technical skills that can make a data scientist stand out from the crowd. Among these are:

  • Problem-solving skills: Data scientists must be able to quickly identify problems, develop hypotheses, and find solutions, so they should be able to think outside the box and come up with creative solutions that may not be immediately obvious.
  • Critical thinking skills: This involves being able to analyse complex datasets, draw meaningful conclusions from them, and present those findings in a clear manner. A great data scientist should be able to look at a problem from multiple angles and determine the best course of action.
  • Business acumen: Business acumen helps data scientists contextualise their findings so that they can make better decisions about how the company should proceed with its operations or marketing efforts.

While it may seem a daunting task to master the above (and more!), there are many resources available to help you learn and develop the skills needed to become a data scientist, including programmes such as our Data Science Bootcamp.


What is Data Science Bootcamp?


It's important to remember that becoming a data scientist is a journey, and it's okay to start small and work your way up. Focus on developing your skills, building your experience, and building your network, and over time you will be able to achieve your goals.


Why consider a career in data science?

Data scientists are in high demand in many industries, including finance, healthcare, technology, and marketing, among others. They play a critical role in helping organisations make data-driven decisions and improve their overall performance.

Data scientists typically receive competitive salaries, and the job offers opportunities for growth and advancement. Additionally, data scientists often have the opportunity to work on interesting and challenging projects, which can be intellectually stimulating.