What is data science?
Data science seems to be all the rage in today’s technological environment. It is a discipline that is well sought after and continues to be in high demand across all industries around the world.
An IT skills report by DevSkiller (2022) highlighted that data science was the fastest growing technology skill in 2021. With such a high demand for data scientists, companies are still struggling to fill these roles due to a skills mismatch in technology.
So what exactly is data science and what about data science makes it such a popular technology role?
Here, we are going to look at the following factors to explain what data science is:
Data science defined
Data science is often classified as a cross-discipline between technology and statistics. It is a domain that deals with managing very large data sets using modern technology tools and statistical techniques to derive meaningful insights from data that is often used to drive an organisations strategic decisions. Simply put, data science makes use of machine learning algorithms to generate predictive models based on raw data.
Data scientist responsibilities
Data scientists are responsible for an array of activities which result in finding immense value in large sets of data, often referred to as big data. These individuals are responsible for identifying relevant data sources from which to retrieve raw data.
Data sources can vary in nature and may include databases, data warehouses, data lakes and other channels where data is generated at high speeds with massive volumes. This process is known as the collection of structured and unstructured data.
Once data has been collected, data scientists will often have to perform the data cleansing procedure which involves making sure that missing data or unusable data is accounted for prior to running a machine learning algorithm on a data set.
After the data has been cleaned and processed, a data scientist will then build and deploy a machine learning model to make sense of the data. These models have predictive power and require raw data as an input which consists of training data and test data – where training data is used to train the machine learning model and test data is used to test the accuracy of the model after it has been trained.
Following adequate training of the model, the analysis process begins; information and insights are generated from the data. These insights are frequently viewed by using software tools such as data visualisation tools which include dashboards, graphs and charts. The insights gained from the machine learning models are what serves as an input to the process of optimising strategic decisions.
When organisations optimise their strategic decisions the goal is often to save costs, generate higher revenues or to create a higher degree of efficiency throughout the organisation’s structure.
Skills required for data science
In order to be a successful data scientist, one requires a relatively strong set of technical and soft skills – technical skills are especially important. These skills include strong mathematical and statistical skills which are imperative to building and making sense of machine learning models.
Multiple linear regression models are quite popular among data scientists and statisticians in the event that a predictive model is required to find a correlation between numerous variables. For example, an organisation may find it useful to determine the correlation between the number of sales of a given product in relation to other factors such as the cost, quality and demand for that product.
This implies that strong analytical skills are also an important factor to becoming a good data scientist. Having strong analytical skills is what gives a good data scientist an advantage when it comes down to identifying trends and patterns from data and information. Given that data science is a blend of statistics and technology, understanding how to use the relevant software tools is arguably one of the most important skills that needs to be developed.
Useful technologies include database software such as SQL Server, Oracle and other database technologies such as NoSQL databases which are popular for storing and managing unstructured data. These are referred to as back-end services where data is usually stored.
To query large sets of data one requires a good understanding of programming languages such as Python, which is currently the world’s most popular programming language according to PYPL (2022) an index which tracks the most widely adopted programming languages among developers. Python is also considered as one of the best data driven programming languages which is endorsed throughout the data science community. This is particularly true given the vast amount of statistical and machine learning libraries that have been developed for Python. The popular libraries include Pandas, Seaborn, Matplotlib, Tensorflow and OpenCV just to mention a few.
Lastly, when one considers the soft skills that are useful for data science, presentation and communication skills are quite valuable. While technical skills allow data scientists to work with data and build models, it is also important to be able to present findings in a coherent manner by effectively communicating with stakeholders.