Tell most data analytics practitioners that much of their ETL efforts might not be the best way to do things, and they’ll react with disbelief. After all, that’s the way they’ve always operated.

Data analytics is the science of analyzing raw data in order to make conclusions about that information. It helps businesses perform more efficiently and maximize profit.

Dr. Clarke and his group are core members of the SFU Sports Analytics Group, a collection of researchers, students, and coaches that are interested in applying data and modeling to improve sports ...

Data analytics is a discipline focused on extracting insights from data, ... and modeling data to derive conclusions. ... the average salary for a data analyst is $66,310 per year, ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis ...

Data analytics is used across disciplines to find trends and solve problems using data mining, data cleansing, data transformation, data modeling, and more. Business analytics also involves data ...

Predictive analytics determines a likely outcome based on an examination of current and historical data. Decision trees, regression, and neural networks all are types of predictive models.

Figure 1: Centralized model for data & analytics management. In practice, however, there’s a few big problems with this approach. First, the data has to be so carefully curated and loaded, ...

Sparse data is still representing something within the variables. Missing data, however, means that the data points are unknown. Challenges in machine learning with sparse data. There are several ...