The importance of data has increased tremendously. From understanding student performance and improving the service system to predicting trends, data science is at the forefront of how we make more informed decisions. But how exactly does one derive useful information from raw data? This blog post will discuss critical steps in the data science process in a lucid, straightforward manner.
Data science has different steps involved; however, it can be quite simple if you go step by step. It's like solving a jigsaw puzzle; you start with collecting the pieces (data), arranging them (cleaning), and finally joining them together (analyzing) to visualise the big picture. Let us look through the key steps that data scientists observe to resolve pending queries arising from gathered data
1. Understanding the Problem
Before going into the data, it is important first to understand what is the problem or question with which you are working. This guides the entire process. For example, if we want to understand why some students are struggling in their courses, we need to ask the right questions. Is it the content of the course? Is it time management? Are students not getting enough help with it?
Once there is a real question, then it can decide which data are needed to be queried for an answer. Understanding the problem helps to ensure that the right data gets collected and that the proper analytical methods are applied.
2. Data Collection
Once understood, appropriate data are gathered. It is certified from multiple sources, like databases, online surveys, sensors, and even social media platforms. For instance, it can include student records, feedback forms, attendance logs, or academic performance reports.
It's important to focus on data collection that relates directly to the researchers' investigation. For instance, research on student performance, study habits, attendance, exam scores, and participation in extracurricular activities could be collected. These data are vital in providing a foundation for finding out patterns and insights.
3. Data Cleaning and Preparation
This is a step that follows the data collection stage: cleaning and preparation. While this may sound simple, this is one of the more critical steps. Usually, collected data is unstructured; it has elements of missing values, duplicate entries, or wrong inputs. It is like solving a puzzle with incompatible pieces coming together in dirty data.
Data cleaning refers to correcting or removing errors and filling in missing information. In some parts, it can very easily even mean changing the form of data to make it usable. For instance, one system has a student graded "A+" while another has this student graded "Excellent." These value sets indeed would need to be normalized for better comparison.
Once the data is cleaned and ready, it can then be organized and well-formatted in an analysis-friendly way.
4. Exploratory Data Analysis EDA
With EDA, we go one step further to the depth of data exploration. In this analysis stage, the data scientists use simple statistical tests and some visual tools in the form of graphs and charts to look for the various patterns, trends, and correlations existing in the data at hand.
With some EDA, we can even get to know whether the students attending classes most regularly perform better during examinations. A graph can be easily used to compare attendance to exam marks. These factors provide us with an insight into what is transpiring in the data before one plunges into deeper analysis.
Analysis and modelling of data come immediately after data cleaning and exploratory analysis. This is where data scientists employ many tools and techniques to build models to facilitate answering a posed problem. These range from a multitude of modelling methods, depending on the type of question that needs to be answered.
If we want to see which students may require assistance in the courses, we can use machine learning technology to create a predictor that reviews past data (like attendance, participation, and grade) and predicts performance. The models therefore help to identify the patterns that come out of the data automatically and improve decision-making.
That could be using predictive modelling to identify a group of students with support needs that we might have to assist with.
5. Interpreting the Results
Right after building the model and analyzing the data, we step into another journey to look for meanings in these results. It means investigating the output of the model and determining what it will tell us. Were there any expected patterns that were there?
For instance, if our model predicts that the low attendance caused the students to fail at a higher rate, we would have to check how much weight the variable has in the failure predictions. Most importantly, we should make it understandable for others, where we can use charts or graphs to summarize our findings.
6. Decision-Making and Action
The last stage in the data science process involves the application of the insights gained to make decisions or choices. More particularly, should our analysis give rise to information that states that students who have attended less than 70% of the classes are more likely to fail, we can put together support programs tutoring, mentoring, or offering flexible learning opportunities for students that fall into this category.
The evidence tells decision-makers what steps to take beyond any guess.
The data science process is a powerful tool used to help us to walk through the maze of decisions. By understanding the problem, collecting the right data, clearing it up, exploring it for patterns, and analyzing it with the right tools, we can come up with some precious insights. These insights then guide our possible actions resulting in improvements, either in the form of improved student performance or improved working methodologies.
At SPARC Institution, data science helps in improving our service to students for enhanced learning environments. We can convert the steps from data into knowledge and wisdom into action in a few simple steps!
