Data science is a new discipline that combines the fields of computer science, statistics, and mathematics to solve real-world problems by analysing and interpreting data. Data scientists are often involved in big data projects, which are usually focused on finding new ways to use data to improve people’s lives. But there are many other applications of data science, such as finding suspicious patterns in large sets of data, or using data to create new products.
As people are getting more comfortable with the idea of using data to make better decisions, many businesses are realising the benefits of giving data scientists a seat at the table. If you want to get started with data science, keep reading.
The Purpose Of Data Science
Before we dive head first into how data science can benefit your business, it is wise that we first take a look at the purpose of data science.
The main goal of data science is to create research methods and display the data in an informative way to extract business-focused insights from it. This requires an understanding of how value and information flows in a business, and the ability to use that understanding to identify business opportunities.
While that may include small projects and types of smaller research projects that are only run a few times, more typically data science teams seek to identify key data assets that can be turned into data pipelines that produce much more maintainable tools and solutions. Examples of this may include credit card fraud monitoring solutions used by banks.
How Data Science Is Conducted
Conducting data science is no easy task, and takes a lot of planning and hard work to execute correctly. Whilst the specifics of each research project will of course differ, the fundamental aspects that are required to conduct data science are the same:
- Planning: This is the process of creating a project and planning its potential outputs.
- Building a data model: Many data scientists use a variety of instruments such as open source libraries or in-database tools to build machine learning models. Tasks include data ingestion, data profiling and visualisation, and feature engineering that allow the custom build of a data model into what is required. The right tools, as well as access to the right data and other resources, are also of great advantage within this stage.
- Evaluating a model: Data scientists must achieve a high percent of accuracy for their models before they can feel confident using it. Many model evaluations usually create a comprehensive list of visualisations to measure model performance against new data, and also rank them over time to enable optimal behaviour in production.
- Explaining models: Being able to explain the data sets and the internal mechanics of the results of machine learning models in human terms has not always been possible. However since this step has become more and more accessible, it has also become invaluable. Data scientists can create explanations of the important factors that go into generating a prediction, and model-specific explanatory details on model predictions.
- Deploying a model: This stage of the process can take a long time to complete. Even the systems that have been designed by the most experienced data scientist will have some faults that need to be recited before they can move on. Using a model can however be made easier by utilising scalable and secure APIs, or by using in-database machine learning models.
- Monitoring models: Models must always be monitored after installation and use to ensure that they are working properly. This could be for various reasons including that the data the model was trained on may no longer be relevant for future predictions after a period of time.
How Data Science Is Applied
Most successful data science professionals know that they must advance past the traditional skills of analysing large amounts of data, data mining, and programming skills.
To be able to uncover useful intelligence for their companies, data scientists must master the full spectrum of data science skills and possess a level of flexibility and understanding to be able to apply these skills at each stage of the process.
The specific way of applying data science will vary greatly from one business to another, however the core principles that are laid down usually stay the same. These principles are put together and have become very well known as the data science life cycle. Data science’s life cycle consists of five distinct stages, each with its own tasks:
- Capture: This stage of the life cycle involves gathering raw structured and unstructured data, and includes tasks such as data entry, signal reception, and data extraction.
- Maintain: This stage of the life cycle covers taking the raw data and putting it in a form that can be used, and includes tasks such as data processing, data architecture, and data staging.
- Process: This stage of the life cycle is where data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis. It includes tasks such as data mining, data modelling, and data classification.
- Analyse: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. This stage of the life cycle involves performing the various analyses on the data, and includes tasks such as text mining, predictive analysis, and qualitative analysis.
- Communicate: The last stage of the life cycle is where the data is analysed and presented in easily readable and understandable forms including charts, graphs, and reports. Tasks in this stage include data reporting, decision making and data visualisation.
How Can Data Science Help My Business?
One of the main functions of a data scientist is to study and structure your business data so that you can extract more accurate insights from your company. By using the data science life cycle and the techniques listed above, a data ascietenist can bring many benefits to your business.
Increases business predictability
With the help of the data scientist, it is possible to use technologies such as Machine Learning and Artificial Intelligence to work with the data that the company has and build a prediction model for your business. This can help you to make decisions that will positively impact the future of your business.
Improves data security
Many data scientists work on fraud prevention systems within a company to help keep your data safe and secure. This could be beneficial to your business in a number of ways, for example, to keep your company’s customers safer or to study recurring patterns of behaviour in the business’ system to identify any security flaws.
Assists the decision-making process
When tools are created to view data in real-time, it opens your business to a whole new world of growth. This gives business managers, COOs and CEOs much more control and agility when they have to make critical business decisions. Data scientists use dashboards and projections to present the data they have found and processed to assist the crucial choices that need to be made.
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