Topic 2: Understanding Data Science Step by Step

As Data Science can easily become a complex, the best way to approach it, innitially, is to break it down to its components and view it step by step.

This first stage is important, as the data scientist along with relevant stakeholders, attempt to identify the problem, the theory, the question, that they will address. After this initial process, the data scientist attempts to foresee the insights and outcomes that should be delivered.

The next step is to begin the process of gathering the data, which will later be analysed. Data deriving from databases, APIs, surveys and questionnaires etc. can be collected. However, the data needs to be relevant to the problem, to provide validity and epistemology.

Raw data or unprocessed information frequently has to be cleared up and processed before it can be used for analysis. In this phase, duties including resolving missing values, removing of duplicates, standardizing formats, and changing variables are included.

EDA includes visually and analytically examining the data to learn more about its features, trends, and potential outliers. Plots, histograms, scatter plots, and other visual representations are made using data visualization tools. EDA assists in finding patterns, correlations, and inconsistencies in the data that may nourish further research and provide direction for decision-making.

At this stage, the preprocessed data is analyzed using data science methods and algorithms. Various techniques, like statistical analysis, machine learning, or deep learning, may be used, depending on the project’s objective. The goal of the analysis phase is to draw out from the data significant patterns, correlations, or forecasts. This stage’s essential elements include model selection, training, and assessment.

This is a crucial step as the data scientist interprets the data and formulates insights. These insights should be able to address the original problem from stage one. Understanding the consequences of the analysis in light of the matter and organizational goals constitutes interpretation. At this stage, it’s essential that findings are communicated clearly.

If the models and insights have been shown to be useful, they are put to use in practical applications. This might entail incorporating predictive models into company operations, developing indicators for continuous evaluation, or integrating suggestions into user interfaces.

Concise reporting is essential for decision-makers to comprehend the outcomes and insights of the analysis and proceed accordingly.