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Descriptive Analytics In Machine Learning

Descriptive Analytics

Descriptive analytics is a type of analytics that is used to describe the data. It involves summarizing the data and identifying patterns and trends. Descriptive analytics is often used as a first step in the machine learning process, as it can help you to understand the data and to identify the features that are important for your model.

Descriptive analytics is a field of statistics that focuses on gathering and summarizing raw data to be easily interpreted. Generally, descriptive analytics concentrate on historical data, providing the context that is vital for understanding information and numbers.

Descriptive analytics can be used to answer questions such as:

  • What are the most common values in the data?

  • What are the relationships between different features in the data?

  • How has the data changed over time?

Descriptive Analytics Divided Into Five Categories 

  • State business metrics: Determine which metrics are important for evaluating performance against business goals. Some goal examples would be to increase revenue, reduce costs, improve operational efficiency, and measure productivity. Each goal must have associated KPIs to help monitor achievement.

  • Identify data required: Business data is located in many different sources within the enterprise, including systems of record, databases, desktops, and shadow IT repositories. To measure accurately against KPIs, companies must catalog and prepare the correct data sources to extract the needed data and calculate metrics based on current state of the business.

  • Extract and prepare data: Data must be prepared for analysis. Deduplication, transformation, and cleansing are a few examples of the data preparation steps that need to take place prior to analysis. Often, this is the most time-consuming and labor-intensive step, requiring up to 80% of an analyst’s time,but it is critical for ensuring accuracy.

  • Analyze data: Data analysts can create models and run analyses such as summary statistics, clustering, and regression analysis on the data to determine patterns and measure performance. Key metrics are calculated and compared with stated

  • Present data: Results of the analytics are usually presented to stakeholders in the form of charts and graphs. This is where the data visualization mentioned earlier comes into play. 

Example Of Descriptive Analytics

  • Tracking course enrollments, course compliance rates,

  • Recording which learning resources are accessed and how often

  • Summarizing the number of times a learner posts in a discussion board

  • Tracking assignment and assessment grades

  • Comparing pre-test and post-test assessments

  • Analyzing course completion rates by learner or by course

  • Collating course survey results

  • Identifying length of time that learners took to complete a course

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