Rapid Fire on Data Analytics

Tableau RapidMiner OpenRefine KNIME Google Search Operators Solver NodeXL io Wolfram Alpha’s Google Fusion tables There are several tools available for data analysis, each with its own strengths and weaknesses. The choice of tool often depends on factors such as the specific requirements of the analysis, the size and complexity of the data, and the … Read more

Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that defines an outcome. Logistic regression is a statistical method used for binary classification tasks, where the goal is to predict the probability of a certain event happening or not happening. Despite its name, logistic regression … Read more

Some of the best practices for data cleaning includes, Sort data by different attributes For large datasets cleanse it stepwise and improve the data with each step until you achieve a good data quality For large datasets, break them into small data. Working with less data will increase your iteration speed To handle common cleansing … Read more

Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data. Data cleansing, also known as data cleaning or data scrubbing, refers to the process of detecting and correcting errors, inconsistencies, and inaccuracies in data to improve its quality and reliability. … Read more

Various steps in an analytics project include Problem definition Data exploration Data preparation Modelling Validation of data Implementation and tracking In a data analytics project, the various steps typically include: Problem Definition: Clearly define the business problem or objective that the analytics project aims to address. This step involves understanding stakeholders’ requirements and expectations. Data … Read more