Free Snowflake DSA-C02 Exam Dumps

Data processing and analysis cannot happen without data profiling---reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.

What is data profiling?

Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.

Data profiling is a crucial part of:

* Data warehouse and business intelligence (DW/BI) projects---data profiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.

* Data conversion and migration projects---data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.

* Source system data quality projects---data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).

Data profiling involves:

* Collecting descriptive statistics like min, max, count and sum.

* Collecting data types, length and recurring patterns.

* Tagging data with keywords, descriptions or categories.

* Performing data quality assessment, risk of performing joins on the data.

* Discovering metadata and assessing its accuracy.

* Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.

As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data. The technique enables developers to categorize the test data into multiple binary class labels.

That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.

One-Vs-Rest Classification Model for Multi-Class Classification

Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.

For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:

Problem one: red vs. green/blue

Problem two: blue vs. green/red

Problem three: green vs. blue/red

The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.

The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used for classification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.