It’s not so much the amount of information that we are swamped with, but how we are unable to control and interpret it. Though we collect and generate data at never before rates, a lot of it sits idle, waiting to be explored and utilized. In this blog, we will discuss how exploratory and predictive analyses enable the conversion of passive data into active sources of information.

What is Exploratory Data Analysis?-. 

Exploratory Data Analysis (EDA), a term introduced by John Tukey, focuses on examining datasets to highlight their key features using simple statistics and visual tools. It helps businesses understand their data better and uncover useful insights.

Goals of EDA

• Understand the data structure: Obtain an understanding of the data’s arrangements, measures, and characteristics to make a clearer picture.
• Detect patterns and relationships: Search for associations among the factors so as to influence interventions.
• Identify unusual data, variability and errors: Identify the data which is most likely to be incorrect or out of the ordinary
• Inform data transformations: Propose revisions to ensure effective re-use of the data.

What is Predictive Data Analysis?

Predictive analysis utilise previous data to forecast future outcomes. With models like classification and regression, organisations can identify patterns and make informed decisions to prepare for upcoming trends.

Is Exploratory Data the same as Predictive Data Analysis?

EDA provides a clear understanding of data, forming a solid foundation for predictive models. Predictive analytics builds on this to help organisations make proactive decisions, such as allocating resources effectively.

Types of Data Analysis

Predictive analytics utilizes different models that help in the prediction of future events using data collected from the past. Two of the most commonly used models are:

• Classification Models: These models divide data into specified sets or classes. For example, in the area of recruitment, classification models might make a prognosis indicating that a job candidate is likely to fit a certain position in view of his/her skills and background. Applied to the renewable energy industry, such models could, for instance, sort job vacancies into engineering, operational, or sales categories, making the work of organisations easier when recruiting personnel.

• Regression Models: Regression Models: Regression models are also used in sales forecasting as well as performance trends considering past performance and factors such as seasonality, advertising expenditure, or consumer behaviour. For example, an organization dealing in retail may apply regression in an attempt to estimate future sales during the festive seasons, to best prepare for this period in terms of stocks, employees, and promotional activities. They can use this model to guide them to make an appropriate decision in forecasting and be ready to adapt to changes in demand.

Exploratory Data Analysis (EDA) includes different methods to examine and understand datasets:

• Univariate Analysis:  Univariate analysis examines one variable at a time to summarise the data with measures like mean, median, mode, and standard deviation. For example, in a healthcare setting, univariate analysis could be used to assess the average patient recovery time after a specific treatment. This can help identify general trends and set benchmarks for improving treatment outcomes.

• Multivariate Analysis: With univariate analysis, the investigator looks at the data regarding one variable at a time and describes the findings with means, medians, modes and standard deviations. For instance, in a healthcare facility, univariate analysis may be applied to evaluate the number of days taken by the average patient to recover from a given treatment. This not only helps uncover general trends but also establishes standards for considering the effectiveness of treatment plans.

Tools used

Exploratory Data Analysis (EDA) Tools

• Python:

Python is a versatile and widely used programming language in data analysis.

Libraries like Pandas allow for efficient data manipulation and cleaning, while Matplotlib and Seaborn help create detailed visualisations to explore patterns and trends.

For instance, in analysing renewable energy job vacancies, Python can be used to generate graphs showing trends in hiring across different regions or timeframes.

• R:

R is another popular tool for statistical computing and graphics. It excels in handling large datasets and performing in-depth statistical analysis, making it an excellent choice for academics and researchers exploring workforce trends or employment data.

Visualisation packages like ggplot2 help present insights in an intuitive format.

• Tableau:

Tableau is a user-friendly visualisation tool perfect for creating interactive dashboards and summarising data. For instance, a marketing team could use Tableau to track campaign performance, analyse customer engagement, and present the results to stakeholders in a visually engaging way. 

Predictive Analysis Techniques

• Regression Analysis:

Regression is a core technique in predictive analytics used to identify relationships between dependent and independent variables. For example, regression models can predict how factors like marketing spend might affect sales performance, helping companies allocate their resources more effectively for future campaigns.

• Decision Trees:

Decision trees simplify complex datasets by breaking them down into branches based on decision rules. This technique is useful for identifying optimal business strategies. For instance, decision trees can help determine the best pricing strategy for a product by analysing customer preferences, competitor prices, and demand trends.

• Neural Networks:

Neural networks are advanced machine learning models inspired by the human brain, capable of handling non-linear and complex relationships within data. In predictive analysis, neural networks can forecast equipment failure in manufacturing by analysing historical maintenance data, machine performance, and environmental factors, helping companies plan for proactive repairs and avoid downtime.

Process Involved

EDA Process

• Gather and Understand Data: Gather all the information you possibly can and also familiarise yourself with it.
• Initial Exploration: After this, go through the collected data to identify some major trends.
• Clean and Prepare Data: Reduce error and enhance data accuracy.
• Explore Relationships: Interpret relationships between one variable and another.
• Select Key Features: Determine possible variables that should be analyzed.
• Generate and Test Ideas: Develop and test hypotheses.
• Present Findings: Communicate results to the clients in clear and simple language.

Predictive Analysis Process

• Define the Goal: Firstly, consider what you want to estimate, for instance, the number of job vacancies in the future.
• Collect and Organise Data: Collect useful historical information.
• Prepare Data: Remove wrong data and correct it so it can be useful.
• Build Predictive Models: Develop models that are right for the problem.
• Validate and Apply Results: Verify the accuracy of the model and then use it to make predictions.

Benefits of Exploratory Data Analysis (EDA)

• Data Quality Assurance:

EDA helps identify missing values, inconsistencies, or anomalies in data, ensuring the dataset is clean and reliable for deeper analysis.

• Uncovering Hidden Patterns:

By visualising data, EDA reveals trends and patterns that may not be immediately obvious, such as seasonal fluctuations in job vacancies or correlations between different job roles.

• Informed Decision-Making:

 EDA give a clear and detailed understanding of the current status of data thus providing a sound platform for business decisions.

• Flexibility in Approach:

It enables analysts to examine specific hypotheses and varies it, which produces a deeper variety of findings.

• Improved Stakeholder Communication:

One of the biggest advantages of the EDA is the ability to use graphs and charts that can be easily explained to people without much technical knowledge, e.g recruitment teams or policymakers.

Benefits of Predictive Data Analysis

• Future Trend Forecasting:

Predictive analytics models enable organisations to anticipate future needs, such as estimating demand for permanent recruitment services in industries experiencing rapid technological advancements, like artificial intelligence and automation.

• Proactive Decision-Making:

By predicting the outcomes, businesses can plan and take necessary actions before anything happens.

• Cost Efficiency:

Using predictive analysis enables efficient resource planning, making it more cost-effective compared to trial-and-error approaches in determining strategic priorities.

• Customised Solutions:

This makes it possible for businesses to deliver services based on the needs of the industry.

• Risk Mitigation:

Through these models, it is possible to predict possible threats like lack of certain competencies or poor economic conditions within organisations and develop measures to address them.

With appropriate tools such as programming languages, libraries and visualization tools, analysts are equipped to perform a deeper analysis. However, the full potential of exploratory as well as the predictive approach is best seen in an analyst who can ask the right questions and develop proper hypotheses. Moreover, proper interpretation of the result is the core of the success of both, and the analyst should be equipped with skills to deliver solutions to business problems. We hope that this guide has enlightened you on the best way to use these techniques to make better decisions based on real data.

Author Bio:

Betsy Thomas, a freelancer by profession but an educator at heart, has always been fascinated by the confluence of teaching and leadership.With a deep passion for education and management, her writings offer insights drawn from rigorous research and a wealth of industry experience.

Social Media Profile: https://www.linkedin.com/in/betsy-t-641550294/