The Data Science Breakdown
By: Taylor Lucero
Tags: Machine Learning, Deep Learning, Learning And Development, Data Science, Data

Descriptive

Interpretation of historical data that yields useful information on an event based on patterns and meaning, and frequently is associated with Data Visualization and Business Intelligence acuity. Descriptive Analytics companies can determine KPI and Metrics that should be focused on to improve goals and internal processes. Descriptive Analytics aims to answer questions revolving around “What happened?”.

Figure 2: Descriptive & Diagnostic Example

Descriptive Analytics Real World Example

Viewer interaction with creator content on Social Media. In this scenario, a creator on a platform like TikTok or Youtube is trying to garner more User interactions on their videos. Based on past Viewer interactions, a bar graph was constructed, showing the interaction between Viewers. With this, the Creator can identify the most to the least interactive viewer. Once information like this is determined, more questions begin to arise like “Why it happened?”.

Diagnostic

Examines data to answer the question, “Why it happened?”. Diagnostic Analytics focuses on the reasoning behind why an event occurred. This analytics section is generally used to identify anomalies, determine a causal relationship, and further delve into the analytics. In this case, anomalies would be outliers or any major extrema that are seen to not fit in with the rest of the data. Hidden relationships are identified by looking at an instance and the events resulting in these instances.

Diagnostic Analytics Real World Example

Why those Viewers interacted with the content, based on the presented pie chart of Leah’s past interactions with content, we can see she tends to like funny videos. This information can help the Creator in determining why Leah interacts the most with this particular content. With this identified relationship, the Creator can then unearth other relative information.

Predictive Analytics

In this section of Analytics Data, Algorithms, Statistics, and Machine learning techniques are utilized to forecast an event, value, or classification accurately. Simply put, it uses historical data to predict the probability of an outcome to answer the question, “What may happen?”. Unlike the previous sections, this field is considered more intensive since algorithms are used to enhance patterns for building predictive models.

Predictive Analytics Real World Example

Forecasting views or interactions on upcoming content based on historical data. Since this section of Analytics is advanced, this concept can be simplified and portrayed by a line graph with the function ( Y = MX + B). Although, there is an assumption that the independent variable, X, has some relationship with dependent variable Y. The function has four parts in terms of predictive analytics, Y, which in this case is the output or dependent variable. M, which is the slope or the rate of change that is applied to X. X, the independent variable or feature that is analyzed to find its connection to Y. Lastly, B, in this case, refers to where the graphed line intersects the vertical Y-axis. This information and historical data can be utilized to estimate where the line is expected to go and can be constructed, in this case, from video #5 to video #6.

Figure 3: Predict and Prescriptive Example

Prescriptive

This section of Analytics focuses on determining the best course of action for a given situation. This section has strong ties to predictive and descriptive analytics because it uses history to make estimations to define the next steps. Prescriptive Analytics answers questions like “What should be done when it happens?

Prescriptive Analytics Real World Example

Determine actions to improve content engagement. Based on the scenario of video #6’s viewer interactions decreasing, the Creator can see that videos with animals tend to get more likes and interactions, with dog videos being the most interacted. So, in the scenario that video 5 doesn’t receive the intended number of likes, consider bringing in a dog for video 6 to improve ratings.