Data analysis involves the systematic examination, cleaning, transformation, and modelling of large amounts of data to uncover valuable insights, draw informed conclusions, and support effective decision-making. In today’s fast-changing world, bringing together science, engineering, and sustainability is becoming essential for tackling complex challenges, at scales ranging from within individual organisations up to the global scale. For leaders, data science is a key tool for driving innovation, boosting analytical power, and supporting data-driven decisions and strategies that create real impact for both people and the planet.

Main types of data analysis.

• Exploratory Data Analysis (EDA) plays a vital role in data science. It focuses on exploring and visualising data to identify patterns and anomalies, and reveals potential relationships, often before applying more formal statistical methods. EDA is very effective for preparing datasets and guiding the selection of suitable models for more in-depth analysis.
• Descriptive analysis focuses on identifying trends, patterns and behaviours in historical data to answer the question “What happened?” and provides a concise summary of prior conditions and their associated outcomes.
• Diagnostic analysis digs deeper into the data to answer the question “Why did it happen?”, using methods such as data correlation, root cause analysis, regression analysis, data segmentation and hypothesis testing. 
• Predictive analysis uses techniques such as regression analysis, time series analysis, and machine learning algorithms to forecast future trends or outcomes based on historical data. This an important tool for risk management and planning and answers the question “What is likely to happen or not happen?”.
• Prescriptive analysis answers the question “What should be done?”. This is a more in depth analysis employing techniques such as optimization models to find the best solution given certain constraints, simulation models to test which scenarios have the most effective outcome, and decision trees to provide a framework for choosing the optimal course of action.
• Qualitative analysis. Although many data analysis projects are quantitative, they can also be qualitative and can be useful for analysis of data such as text, audio, and images. Techniques include, but are not limited to, thematic analysis for identifying patterns or themes in data and grounded theory analysis for generating a theory from data without being limited by pre-existing hypotheses.

Data science plays a crucial role in the operations of any business or organisation, and can be effectively applied to any role, rather than implementing it with a top-down approach. Get in touch if you want your business or organisation to gain valuable insights, make more informed decisions and develop fact based policies or strategies using scientific, engineering and/or sustainability data. 

To view a sample of my data science work please see my Projects on my LinkedIn profile or go directly to my GitHub site. Links are provided at the bottom of the page.